diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..57028691815479f3111f8b976f292d611a4548e1 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,6 @@ +# Ignore everything +** + +# Except scripts +!/scripts +!/requirements.txt \ No newline at end of file diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..5fd718cf2cfb7966eae762e19deeb8dbdf9a5ed5 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +*.stl filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..53274cf7140d2505366fe8733b38742bd2b2d658 --- /dev/null +++ b/.gitignore @@ -0,0 +1,158 @@ +# Folders +cliport/data/ +cliport/outputs/ +cliport/notebooks/ +cliport/jobs/ +jobs/ +videos/ +checkpoints/ + +media/ + +# Large Asserts +cliport/environments/assets/google/ + +# Idea +.idea + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST +wandb +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + + + +output +data +.hydra +cliport/environments/assets_backup +0_VRDemoSettings.txt +demo*.log +outputs +*.DS_Store + +multirun +exps-singletask/ +*.jsonl \ No newline at end of file diff --git a/BLOG.md b/BLOG.md new file mode 100644 index 0000000000000000000000000000000000000000..7a52762fe921e1b39cebc5f8a1cce89f57d0260a --- /dev/null +++ b/BLOG.md @@ -0,0 +1,132 @@ +# Supersizing Simulation Task Generation in Robotics with LLM + + +## Overview +Collecting real-world interaction data to train general robotic policies is prohibitively expensive, thus motivating the use of simulation data. Despite abundant single-task simulation data in terms of object instances and poses among others, the task-level diversity in simulation have remained a challenge. On the other hand, the breakthrough in language domain, such as GPT-4, has shown impressive coding skills and natural language understanding capability, but its usage in robotics has been mostly on policy execution, planning, and log summary. This repository explores the use of a LLM code generation pipeline to generate simulation environments and expert demonstrations for diverse simulation tasks. In particular, the task-level diversity is crucial for general-purpose manipulation policy learning. This simulation task generation pipeline can be top-down: given a target task, it proposes a task curriculum to iteratively approach the complexity of the target task; the pipeline can also work in a bottom-up manner: it bootstraps from previous tasks and iteratively proposes more interesting tasks, and these task code can be used to generate demonstrations to train a policy. + +We develop an LLM pipeline for generating simulation environments and tasks through program synthesis, as data augmentations for robotic policy learning. The framework consists of three novel components: +1. an automated prompting mechanism that proposes new tasks and implementations for open-world task design +2. a task library for developing more complex simulation environments and data generations +3. a GPT-4 generated incremental benchmark and a language-conditioned multi-task policy training method that leverages the large set of generated tasks, and close the loop on evaluating the task generation pipeline. + + +Note: Although the field has different opinions on what tasks and skills are, in this work we consider each simulation code defines a task. Therefore the [Ravens](https://github.com/google-research/ravens/tree/master) benchmark has 10 tasks in total. + + +![](media/zoom_task.gif) + + + +## Prompt Recipe +Although we can prompt GPT-4 directly to generate simulation environment code for training manipulation policies, it lacks the contexts and the capability required to build an increasing task benchmark. We formulate the task and program synthesis problems into an agent prompting mechanism with a task design agent and a task library (or memory). These sub-components are all powered by few-shot and chain-of-thoughts prompts on large language models that have distilled internet-scale knowledge and offer the reasoning and exploration capability necessary for simulation task generations. + + +We have developed both top-down and bottom-up task approaches in our method. The top-down approach takes a desired task as a prompt and gradually generates more complex tasks to achieve this target task. This is helpful if the user apriori has a desired task or wants to design a task curriculum to build complex agents. For instance, to train a policy to accomplish long-horizon tasks such as build-house, we can ask LLM to generate more basic tasks like building a base or building a roof. The bottom-up approach shows the LLM the previous tasks that have been designed, directly requests for a new task, and further bootstraps itself iteratively. The goal is then to generate as diverse and interesting tasks as possible for downstream policy training. The details of the prompt is shown in the figure and section below. + +![](media/prompting_pipeline.gif) + +## Task Design Prompt +The goal of the task design prompt is to propose novel task descriptions and their code implementations, which can be further broken down into scene code and demonstration code. In particular, we use the [Ravens](https://github.com/google-research/ravens/tree/master) benchmark codebase with TransporterNets that use affordance prediction to solve table-top top-down manipulation tasks. The task design can handle any motion primitive like pushing, sliding, etc. that can be parameterized by two +end-effector poses at each timestep. From the figure, a standard gym-style simulation environment code, the reset function, which is inherited from a base task class and takes in the environment as an argument, efficiently represents the assets and their attributes, poses and their relative configurations, and spatial and language goals that are used to parameterize the per-step demonstration. + + +To reach this level of lengthy code generation, we break the prompt of the agent into several steps to enforce its logical structure (e.g. [prompt](prompts/bottomup_task_generation_prompt/)): task description generation, API and common mistake summary, few-shot reference code selection, and code generation. The input prompt to GPT-4 task generation stage consists of several components: +1. available assets that are in the codebase +2. samples of reference tasks from the task library (discussed in the next section) to act as few-shot examples +3. also provide past task names to make sure the agent does not provide overlapped tasks. +4. some examples of bad tasks and the reasons that they are bad (for example, not physically feasible) +5. some additional rules (such as do not use assets beyond what is available) and the output format. This stage has temperature=1 to encourage diversity and the rest components would have temperature=0 to have some robustness. + + +The asset generation, which generates the URDFs for loading into a scene, has not been explored much in the pipeline. The API prompt consists of some major function implementations of the base task class as well as an explanation of the `goal` variable that represents the action labels. This is important to help GPT understands some useful helper functions in the base class (such as `get_random_pose` as well as some pybullet (simulation engine) basics. The common error prompt is a list of past errors that GPT-4 has made as well as some high-level errors that are summarized, to help it avoid making repeated errors. These components are optional. + + +Finally, reference code selection and code reference prompt will show LLM the generated task names and ask GPT which ones are useful to read, and then show GPT the corresponding code to be used as reference code. This part is critical for LLM to know exactly how to implement a task class in Ravens (such as the logic of sample asset urdfs and build scene first, and then add a spatial goal and language goals). + +![](media/code_explanation.png) + +
+Prompt Metric Ablation + + +![](media/prompt_metric.png) + +
+ +## Task Library (Memory) +An important differentiator of an agent-based LLM pipeline is that it has a memory of its past actions. In this case, our agent is a simulation task and code programmer and its environment is the physics simulation and human users. The task library has a few roles, for one it provides what past tasks are (in the task generation stage) and past codes are (in the code generation stage) to the task design agent such that it will not try to overlap tasks. It also acts as a benchmark to accumulate all past tasks and bootstrap for more novel tasks. Its saved task codes can be run offline to generate demonstration data. We can also visualize the tasks with an embedding space. The memory also contains a key component: the critic that reflects on the task and code that the agent designed and the reference code, and decides whether the new code will be added to the task memory. The task reflection stage prompt has the following component +1. the generated task description and code +2. the current tasks in the library +3. some examples of accepting and rejecting new task, and then LLM is prompted to answer whether to accept this new task and this improvement will also be in the context window in the next round of the agent task design. + + +Note that to improve the robustness of this stage, we prompt GPT three times in parallel to get diverse answers with temperature 0.5, and only accept the task if there is an agreement. We show some selected generated tasks by GPT to explore different reasoning capability of GPT-4. + +
+Stack-Tasks Examples (Complexity) + + +![](media/stack_task.gif) + +
+ +
+Build-Task Examples (Creativity) + + +![](media/build_task.gif) + +
+ +
+Pick-and-Place-Task Examples (Compositionality) + + +![](media/pick_place_task.gif) + +
+ + +## Policy Training +Once the tasks are generated, we can use these task codes to generate demonstration data and train manipulation policies. We use similar two-stream architectures and transporter architectures as in [CLIPORT](https://cliport.github.io/) to parametrize the policy $\pi$. The model first (i) attends to a local region to decide where to pick, then (ii) computes +a placement location by finding the best match through cross-correlation of deep visual features. The FCNs are extended to two-pathways: semantic and spatial where the semantic stream is conditioned with language features at the bottleneck and fused with intermediate features from the spatial stream. For more details, we refer the reader to the original papers. + + + + +## Common Failure Cases +0. The `common_error.txt` in the prompt folder shows some common failure cases of the generation. +1. Since the simulation task code (reset function and class definition) is lengthy compared to simple function completions. It can be prone to bugs such as accessing missing functions or assets which cause compilation errors. +2. When the tasks can be run, it could still have runtime errors such as in dynamics and geometry issues. For instance it can generate a huge object or generate task such as `balance a block on a rope` which is not grounded well. +3. When the task has no runtime issues, the experts (represented by the `goal`) might not complete the task. Or the language descriptions can be too ambiguous to train an agent and require manual filtering. +4. Some tasks are not zero-shot generated: such as `build-house`, `build-car`, and `manipulating-two-ropes` etc. Some tasks are mostly coded by the author for bootstrapping purpose such as `push-piles-into-letter` and `connect-boxes-with-rope`. + +
+Failure Cases + + +![](media/failure_case.gif) + +
+ + +## Related Works +LLM has shown impressive potential to explore the environments and reflect upon its own actions, similar to an agent such as in [Voyager](https://voyager.minedojo.org/). Recent works have explored domain randomizations, [parametric task generations](https://sites.google.com/view/active-task-randomization), and procedural asset generations and text to 3D such as [Shape-E](https://github.com/openai/shap-e) and [Point-E](https://openai.com/research/point-e). Moreover, large language models have been applied to policy learning such as in [PALM-e](https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html) and [Say-Can](https://say-can.github.io/), task and motion planning such as in [Inner Monologue](https://innermonologue.github.io/), synthesizing policy programs such as in [Code as Policies](https://code-as-policies.github.io/) and [Language as Rewards](https://language-to-reward.github.io/). Past work has also explored LLM's physical grounded capability such as in [Mine's Eye](https://arxiv.org/abs/2210.05359). + +## Conclusion and Future Directions +Overall we explored the use of LLM in simulation environment and task generation. It has shown impressive capability to write manipulation tasks along with expert demonstrations and yet still has several drawbacks and thus future directions. +There are a few limitations which could be interesting future directions +1. The asset diversity limits how GPT-4 can generate high diverse and creative tasks. One interesting future direction is to explore asset generation jointly with code generation. +2. It would be cool to generate thousands of tasks using this pipeline by bootstrapping as well as train an agent that can fit these number of tasks. +3. It would be interesting to carefully study task-level generalization. We have generated a TSNE plot of the task code embeddings by use GPT embedding AI to encode the generated code for each task below. + +![](media/task_embedding.png) + +## Acknowledgement +I would like to acknowledge [Bailin Wang](https://berlino.github.io/), [Mohit Shridhar](https://mohitshridhar.com/), and [Yoon Kim](https://people.csail.mit.edu/yoonkim/) for the helpful discussions and collaborations. \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..766985a2cfb8e0b18da2c99b0c7351e454cc15b5 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,75 @@ +FROM nvidia/cudagl:11.1.1-devel-ubuntu18.04 + +ARG USER_NAME +ARG USER_PASSWORD +ARG USER_ID +ARG USER_GID + +RUN apt-get update +RUN apt install sudo +RUN useradd -ms /bin/bash $USER_NAME +RUN usermod -aG sudo $USER_NAME +RUN yes $USER_PASSWORD | passwd $USER_NAME + +# set uid and gid to match those outside the container +RUN usermod -u $USER_ID $USER_NAME +RUN groupmod -g $USER_GID $USER_NAME + +# work directory +WORKDIR /home/$USER_NAME + +# install system dependencies +COPY ./scripts/install_deps.sh /tmp/install_deps.sh +RUN yes "Y" | /tmp/install_deps.sh + +# setup python environment +RUN cd $WORKDIR + +# install python requirements +# RUN sudo python3 -m pip install --upgrade pip && \ +# sudo python3 -m pip install --upgrade + +# install pip3 +RUN apt-get -y install python3-pip +RUN sudo python3 -m pip install --upgrade pip + +# install pytorch +RUN sudo pip3 install \ + torch==1.9.1+cu111 \ + torchvision==0.10.1+cu111 \ + -f https://download.pytorch.org/whl/torch_stable.html + +# install GLX-Gears (for debugging) +RUN apt-get update && apt-get install -y \ + mesa-utils \ + python3-setuptools \ + && rm -rf /var/lib/apt/lists/* + + +RUN sudo pip3 install \ + absl-py>=0.7.0 \ + gym==0.17.3 \ + pybullet>=3.0.4 \ + matplotlib>=3.1.1 \ + opencv-python>=4.1.2.30 \ + meshcat>=0.0.18 \ + scipy==1.4.1 \ + scikit-image==0.17.2 \ + transforms3d==0.3.1 \ + pytorch_lightning==1.0.3 \ + tdqm \ + hydra-core==1.0.5 \ + wandb \ + transformers==4.3.2 \ + kornia \ + ftfy \ + regex \ + ffmpeg \ + imageio-ffmpeg + + +# change ownership of everything to our user +RUN mkdir /home/$USER_NAME/cliport +RUN cd /home/$USER_NAME/cliport && echo $(pwd) && chown $USER_NAME:$USER_NAME -R . +RUN echo "export CLIPORT_ROOT=~/cliport" >> /home/$USER_NAME/.bashrc +RUN echo "export PYTHONPATH=$PYTHONPATH:~/cliport" >> /home/$USER_NAME/.bashrc diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5918f4ac521a090671507fce6abaf25e4a64ef7a --- /dev/null +++ b/README.md @@ -0,0 +1,41 @@ +--- +title: GenSim +emoji: 📈 +colorFrom: purple +colorTo: indigo +sdk: gradio +sdk_version: 3.39.0 +app_file: app.py +pinned: false +license: apache-2.0 +--- + +# Generative Simulation Interactive Demo + +This demo is from the paper: + + +Below is an interactive demo for the simulated tabletop manipulation domain, seen in the paper section IV.D + +## Preparations +1. Obtain an [OpenAI API Key](https://openai.com/blog/openai-api/) + +## Usage +1. Type in desired task name in the box. Then GenSim will try to run through the pipeline +2. The task name has the form word separated by dash. For instance, 'place-blue-in-yellow' and 'align-rainbow-along-line'. + +## Guideline + +## Known Limitations +1. The code generation can fail or generate infeasible tasks. +2. The low-level pick place primitive does not do collision checking and cannot pick up certain objects. +3. Top-down generation is typically more challenging if the task name is too vague or too distant from motions such as stacking. + + +## Note +For GPT-4 model, each inference costs about $0.3. For GPT-3.5 model, each inference costs about $0.03. + + +## Acknowledgement +Thanks to Jacky's [code-as-policies](https://huggingface.co/spaces/jackyliang42/code-as-policies/tree/main) demo. \ No newline at end of file diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..c6d98530ebb59069fc07f02c71082b358fed165d --- /dev/null +++ b/app.py @@ -0,0 +1,181 @@ + +from hydra.core.global_hydra import GlobalHydra +import gradio as gr +import os +import hydra +import random + +import re +import openai +import IPython +import time +import pybullet as p +import traceback +from datetime import datetime +from pprint import pprint +import cv2 +import re +import random +import json + +from gensim.agent import Agent +from gensim.critic import Critic +from gensim.sim_runner import SimulationRunner +from gensim.memory import Memory +from gensim.utils import set_gpt_model, clear_messages + + +class DemoRunner: + + def __init__(self): + self._env = None + GlobalHydra.instance().clear() + hydra.initialize(version_base="1.2", config_path='cliport/cfg') + self._cfg = hydra.compose(config_name="data") + + def setup(self, api_key): + cfg = self._cfg + openai.api_key = api_key + cfg['model_output_dir'] = 'temp' + cfg['prompt_folder'] = 'bottomup_task_generation_prompt' + set_gpt_model(cfg['gpt_model']) + cfg['load_memory'] = True + cfg['use_template'] = True + cfg['task_description_candidate_num'] = 10 + cfg['record']['save_video'] = True + + print("cfg = ", cfg) + memory = Memory(cfg) + agent = Agent(cfg, memory) + critic = Critic(cfg, memory) + self.simulation_runner = SimulationRunner(cfg, agent, critic, memory) + + info = '### Set up ' + + return info + + def setup_top_down(self, api_key, target_task_name): + cfg = self._cfg + openai.api_key = api_key + cfg['model_output_dir'] = 'temp' + cfg['prompt_folder'] = 'topdown_task_generation_prompt' + set_gpt_model(cfg['gpt_model']) + cfg['load_memory'] = True + cfg['use_template'] = True + cfg['target_task_name'] = target_task_name + cfg['task_description_candidate_num'] = 10 + cfg['record']['save_video'] = True + + print("cfg = ", cfg) + memory = Memory(cfg) + agent = Agent(cfg, memory) + critic = Critic(cfg, memory) + self.simulation_runner = SimulationRunner(cfg, agent, critic, memory) + + info = '### Set up ' + + return info + + def run(self, instruction, progress): + cfg = self._cfg + cfg['target_task_name'] = instruction + + # self._env.cache_video = [] + self.simulation_runner._md_logger = '' + # progress(0.2) + yield "Task Generating ==>", None, None + yield from self.simulation_runner.task_creation() + yield from self.simulation_runner.simulate_task() + + def run_example(self): + cfg = self._cfg + + # self._env.cache_video = [] + self.simulation_runner._md_logger = '' + # progress(0.2) + yield "Task Generating ==>", None, None + yield from self.simulation_runner.example_task_creation() + yield from self.simulation_runner.simulate_task() + + +def setup(api_key, option_choice, target_task_name): + print(option_choice) + if not api_key: + return 'Please enter your OpenAI API key!', None + + if option_choice is None: + return 'Please choose the mode!', None + demo_runner = DemoRunner() + + if option_choice == 'top-down': + info = demo_runner.setup_top_down(api_key, target_task_name) + option_choice + elif option_choice == 'bottom-up': + info = demo_runner.setup(api_key) + option_choice + else: + raise NotImplementedError + return info, demo_runner + + + +def run(instruction, demo_runner, progress=gr.Progress()): + yield from demo_runner.run(instruction, progress=progress) + +def run_example(): + demo_runner = DemoRunner() + demo_runner.setup(1) + yield from demo_runner.run_example() + + +if __name__ == '__main__': + os.environ['GENSIM_ROOT'] = os.getcwd() + with open('README.md', 'r') as f: + for _ in range(12): + next(f) + readme_text = f.read() + + with gr.Blocks() as demo: + state = gr.State(None) + + gr.Markdown(readme_text) + gr.Markdown('# Interactive Demo') + with gr.Row(): + with gr.Column(): + + + btn_example_run = gr.Button("Run Example (OpenAI API Key not required)") + with gr.Row(): + inp_api_key = gr.Textbox(label='OpenAI API Key (this is not stored anywhere)', lines=1) + + option_choice = gr.Radio(["bottom-up", "top-down"], label="Which mode?", interactive=True) + inp_instruction = gr.Textbox(label='Target Task Name (if top-down)', lines=1) + info_setup = gr.Markdown(label='Setup Info') + btn_setup = gr.Button("Setup/Reset Simulation") + btn_run = gr.Button("Run (this may take 30+ seconds)") + # with gr.Column(): + + with gr.Row(): + with gr.Column(scale=1, min_width=600): + progress = gr.Markdown(label='Progress') + generated_task = gr.Markdown(label='Generated Task') + generated_asset = gr.Markdown(label='Generated Asset') + generated_code = gr.Code(label='Generated Code', language="python", interactive=True) + video_run = gr.Video(label='Video of Last Instruction') + btn_setup.click( + setup, + inputs=[inp_api_key, option_choice, inp_instruction], + outputs=[info_setup, state] + ) + btn_run.click( + run, + inputs=[inp_instruction, state], + outputs=[progress, generated_code, video_run] + ) + + btn_example_run.click( + run_example, + inputs=[], + outputs=[progress, generated_code, video_run] + ) + + + demo.queue().launch(show_error=True) diff --git a/cliport/__init__.py b/cliport/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d4cedd82c66da3bb9e701277035398b9c7528b5b --- /dev/null +++ b/cliport/__init__.py @@ -0,0 +1,7 @@ +"""Package init.""" + +from cliport import agents +from cliport import models +from cliport import tasks +from cliport.dataset import RavensDataset +from cliport.environments.environment import Environment diff --git a/cliport/agents/__init__.py b/cliport/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..458a388af068f7997fd427cfca97bb5745f1b00a --- /dev/null +++ b/cliport/agents/__init__.py @@ -0,0 +1,84 @@ +from cliport.agents.transporter import OriginalTransporterAgent +from cliport.agents.transporter import ClipUNetTransporterAgent +from cliport.agents.transporter import TwoStreamClipWithoutSkipsTransporterAgent +from cliport.agents.transporter import TwoStreamRN50BertUNetTransporterAgent +from cliport.agents.transporter import TwoStreamClipUNetTransporterAgent + +from cliport.agents.transporter_lang_goal import TwoStreamClipLingUNetTransporterAgent +from cliport.agents.transporter_lang_goal import TwoStreamRN50BertLingUNetTransporterAgent +from cliport.agents.transporter_lang_goal import TwoStreamUntrainedRN50BertLingUNetTransporterAgent +from cliport.agents.transporter_lang_goal import OriginalTransporterLangFusionAgent +from cliport.agents.transporter_lang_goal import ClipLingUNetTransporterAgent +from cliport.agents.transporter_lang_goal import TwoStreamRN50BertLingUNetLatTransporterAgent + +from cliport.agents.transporter_image_goal import ImageGoalTransporterAgent + +from cliport.agents.transporter import TwoStreamClipUNetLatTransporterAgent +from cliport.agents.transporter_lang_goal import TwoStreamClipLingUNetLatTransporterAgent +from cliport.agents.transporter_lang_goal import TwoStreamClipFilmLingUNetLatTransporterAgent +from cliport.agents.transporter_lang_goal import TwoStreamClipFilmLingUNetLatTransporterAgent, TwoStreamClipLingUNetLatTransporterAgentReduce, TwoStreamClipLingUNetLatTransporterAgentReducePretrained +from cliport.agents.transporter_lang_goal import TwoStreamClipLingUNetLatTransporterAgentReduceOneStream +from cliport.agents.transporter_lang_goal import TwoStreamMdetrLingUNetLatTransporterAgent + +names = { + ################################ + ### CLIPort ### + 'cliport': TwoStreamClipLingUNetLatTransporterAgent, + 'cliport_reduce': TwoStreamClipLingUNetLatTransporterAgentReduce, + 'cliport_reduce_pretrain': TwoStreamClipLingUNetLatTransporterAgentReducePretrained, + 'cliport_reduce_onestream': TwoStreamClipLingUNetLatTransporterAgentReduceOneStream, + 'two_stream_clip_lingunet_lat_transporter': TwoStreamClipLingUNetLatTransporterAgent, + + ################################ + ### Two-Stream Architectures ### + # CLIPort without language + 'two_stream_clip_unet_lat_transporter': TwoStreamClipUNetLatTransporterAgent, + + # CLIPort without lateral connections + 'two_stream_clip_lingunet_transporter': TwoStreamClipLingUNetTransporterAgent, + + # CLIPort without language and lateral connections + 'two_stream_clip_unet_transporter': TwoStreamClipUNetTransporterAgent, + + # CLIPort without language, lateral, or skip connections + 'two_stream_clip_woskip_transporter': TwoStreamClipWithoutSkipsTransporterAgent, + + # RN50-BERT + 'two_stream_full_rn50_bert_lingunet_lat_transporter': TwoStreamRN50BertLingUNetLatTransporterAgent, + + # RN50-BERT without language + 'two_stream_full_rn50_bert_unet_transporter': TwoStreamRN50BertUNetTransporterAgent, + + # RN50-BERT without lateral connections + 'two_stream_full_rn50_bert_lingunet_transporter': TwoStreamRN50BertLingUNetTransporterAgent, + + # Untrained RN50-BERT (similar to untrained CLIP) + 'two_stream_full_untrained_rn50_bert_lingunet_transporter': TwoStreamUntrainedRN50BertLingUNetTransporterAgent, + + ################################### + ### Single-Stream Architectures ### + # Transporter-only + 'transporter': OriginalTransporterAgent, + + # CLIP-only without language + 'clip_unet_transporter': ClipUNetTransporterAgent, + + # CLIP-only + 'clip_lingunet_transporter': ClipLingUNetTransporterAgent, + + # Transporter with language (at bottleneck) + 'transporter_lang': OriginalTransporterLangFusionAgent, + + # Image-Goal Transporter + 'image_goal_transporter': ImageGoalTransporterAgent, + + ############################################## + ### New variants NOT reported in the paper ### + + # CLIPort with FiLM language fusion + 'two_stream_clip_film_lingunet_lat_transporter': TwoStreamClipFilmLingUNetLatTransporterAgent, + + # MDETR + 'mdetr': TwoStreamMdetrLingUNetLatTransporterAgent + + } \ No newline at end of file diff --git a/cliport/agents/transporter.py b/cliport/agents/transporter.py new file mode 100644 index 0000000000000000000000000000000000000000..d24967d1e7f2684176732a06bb9271676f43bbc3 --- /dev/null +++ b/cliport/agents/transporter.py @@ -0,0 +1,539 @@ +import os +import numpy as np + +import torch +import torch.nn.functional as F +from pytorch_lightning import LightningModule + +from cliport.tasks import cameras +from cliport.utils import utils +from cliport.models.core.attention import Attention +from cliport.models.core.transport import Transport +from cliport.models.streams.two_stream_attention import TwoStreamAttention +from cliport.models.streams.two_stream_transport import TwoStreamTransport + +from cliport.models.streams.two_stream_attention import TwoStreamAttentionLat +from cliport.models.streams.two_stream_transport import TwoStreamTransportLat +import time +import IPython + +class TransporterAgent(LightningModule): + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__() + utils.set_seed(0) + self.automatic_optimization=False + self.device_type = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # this is bad for PL :( + self.name = name + self.cfg = cfg + self.train_loader = train_ds + self.test_loader = test_ds + + self.train_ds = train_ds.dataset + self.test_ds = test_ds.dataset + + self.name = name + self.task = cfg['train']['task'] + self.total_steps = 0 + self.crop_size = 64 + self.n_rotations = cfg['train']['n_rotations'] + + self.pix_size = 0.003125 + self.in_shape = (320, 160, 6) + self.cam_config = cameras.RealSenseD415.CONFIG + self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.28]]) + + self.val_repeats = cfg['train']['val_repeats'] + self.save_steps = cfg['train']['save_steps'] + + self._build_model() + ## + # reduce the number of parameters here + ## + self._optimizers = { + 'attn': torch.optim.Adam(self.attention.parameters(), lr=self.cfg['train']['lr']), + 'trans': torch.optim.Adam(self.transport.parameters(), lr=self.cfg['train']['lr']) + } + print("Agent: {}, Logging: {}".format(name, cfg['train']['log'])) + + def configure_optimizers(self): + return self._optimizers + + def _build_model(self): + self.attention = None + self.transport = None + raise NotImplementedError() + + def forward(self, x): + raise NotImplementedError() + + def cross_entropy_with_logits(self, pred, labels, reduction='mean'): + # Lucas found that both sum and mean work equally well + x = (-labels.view(len(labels), -1) * F.log_softmax(pred.view(len(labels), -1), -1)) + if reduction == 'sum': + return x.sum() + elif reduction == 'mean': + return x.mean() + else: + raise NotImplementedError() + + def attn_forward(self, inp, softmax=True): + inp_img = inp['inp_img'] + output = self.attention.forward(inp_img, softmax=softmax) + return output + + def attn_training_step(self, frame, backprop=True, compute_err=False): + inp_img = frame['img'] + p0, p0_theta = frame['p0'], frame['p0_theta'] + + inp = {'inp_img': inp_img} + out = self.attn_forward(inp, softmax=False) + return self.attn_criterion(backprop, compute_err, inp, out, p0, p0_theta) + + def attn_criterion(self, backprop, compute_err, inp, out, p, theta): + # Get label. + if type(theta) is torch.Tensor: + theta = theta.detach().cpu().numpy() + + theta_i = theta / (2 * np.pi / self.attention.n_rotations) + theta_i = np.int32(np.round(theta_i)) % self.attention.n_rotations + inp_img = inp['inp_img'].float() + + label_size = inp_img.shape[:3] + (self.attention.n_rotations,) + label = torch.zeros(label_size, dtype=torch.float, device=out.device) + + # remove this for-loop laters + for idx, p_i in enumerate(p): + label[idx, int(p_i[0]), int(p_i[1]), theta_i[idx]] = 1 + label = label.permute((0, 3, 1, 2)).contiguous() + + # Get loss. + loss = self.cross_entropy_with_logits(out, label) + + # Backpropagate. + if backprop: + attn_optim = self._optimizers['attn'] + self.manual_backward(loss) + attn_optim.step() + attn_optim.zero_grad() + + # Pixel and Rotation error (not used anywhere). + err = {} + if compute_err: + with torch.no_grad(): + pick_conf = self.attn_forward(inp) + pick_conf = pick_conf[0].permute(1,2,0) + pick_conf = pick_conf.detach().cpu().numpy() + p = p[0] + theta = theta[0] + + # single batch + argmax = np.argmax(pick_conf) + argmax = np.unravel_index(argmax, shape=pick_conf.shape) + p0_pix = argmax[:2] + p0_theta = argmax[2] * (2 * np.pi / pick_conf.shape[2]) + + err = { + 'dist': np.linalg.norm(np.array(p.detach().cpu().numpy()) - p0_pix, ord=1), + 'theta': np.absolute((theta - p0_theta) % np.pi) + } + return loss, err + + def trans_forward(self, inp, softmax=True): + inp_img = inp['inp_img'] + p0 = inp['p0'] + + output = self.transport.forward(inp_img, p0, softmax=softmax) + return output + + def transport_training_step(self, frame, backprop=True, compute_err=False): + inp_img = frame['img'].float() + p0 = frame['p0'] + p1, p1_theta = frame['p1'], frame['p1_theta'] + + inp = {'inp_img': inp_img, 'p0': p0} + output = self.trans_forward(inp, softmax=False) + err, loss = self.transport_criterion(backprop, compute_err, inp, output, p0, p1, p1_theta) + return loss, err + + def transport_criterion(self, backprop, compute_err, inp, output, p, q, theta): + s = time.time() + if type(theta) is torch.Tensor: + theta = theta.detach().cpu().numpy() + + itheta = theta / (2 * np.pi / self.transport.n_rotations) + itheta = np.int32(np.round(itheta)) % self.transport.n_rotations + + # Get one-hot pixel label map. + inp_img = inp['inp_img'] + + # label_size = inp_img.shape[:2] + (self.transport.n_rotations,) + label_size = inp_img.shape[:3] + (self.transport.n_rotations,) + label = torch.zeros(label_size, dtype=torch.float, device=output.device) + + # remove this for-loop laters + q[:,0] = torch.clamp(q[:,0], 0, label.shape[1]-1) + q[:,1] = torch.clamp(q[:,1], 0, label.shape[2]-1) + + for idx, q_i in enumerate(q): + label[idx, int(q_i[0]), int(q_i[1]), itheta[idx]] = 1 + label = label.permute((0, 3, 1, 2)).contiguous() + + # Get loss. + loss = self.cross_entropy_with_logits(output, label) + + if backprop: + transport_optim = self._optimizers['trans'] + transport_optim.zero_grad() + self.manual_backward(loss) + transport_optim.step() + + # Pixel and Rotation error (not used anywhere). + err = {} + if compute_err: + with torch.no_grad(): + place_conf = self.trans_forward(inp) + # pick the first batch + place_conf = place_conf[0] + q = q[0] + theta = theta[0] + place_conf = place_conf.permute(1, 2, 0) + place_conf = place_conf.detach().cpu().numpy() + argmax = np.argmax(place_conf) + argmax = np.unravel_index(argmax, shape=place_conf.shape) + p1_pix = argmax[:2] + p1_theta = argmax[2] * (2 * np.pi / place_conf.shape[2]) + + err = { + 'dist': np.linalg.norm(np.array(q.detach().cpu().numpy()) - p1_pix, ord=1), + 'theta': np.absolute((theta - p1_theta) % np.pi) + } + + self.transport.iters += 1 + return err, loss + + def training_step(self, batch, batch_idx): + + self.attention.train() + self.transport.train() + + frame, _ = batch + self.start_time = time.time() + + # Get training losses. + step = self.total_steps + 1 + loss0, err0 = self.attn_training_step(frame) + + self.start_time = time.time() + + if isinstance(self.transport, Attention): + loss1, err1 = self.attn_training_step(frame) + else: + loss1, err1 = self.transport_training_step(frame) + + total_loss = loss0 + loss1 + self.total_steps = step + self.start_time = time.time() + self.log('tr/attn/loss', loss0) + self.log('tr/trans/loss', loss1) + self.log('tr/loss', total_loss) + self.check_save_iteration() + + return dict( + loss=total_loss, + ) + + def check_save_iteration(self): + global_step = self.total_steps + + if (global_step + 1) % 100 == 0: + # save lastest checkpoint + print(f"Saving last.ckpt Epoch: {self.trainer.current_epoch} | Global Step: {self.trainer.global_step}") + self.save_last_checkpoint() + + def save_last_checkpoint(self): + checkpoint_path = os.path.join(self.cfg['train']['train_dir'], 'checkpoints') + ckpt_path = os.path.join(checkpoint_path, 'last.ckpt') + self.trainer.save_checkpoint(ckpt_path) + + def validation_step(self, batch, batch_idx): + self.attention.eval() + self.transport.eval() + + loss0, loss1 = 0, 0 + assert self.val_repeats >= 1 + for i in range(self.val_repeats): + frame, _ = batch + l0, err0 = self.attn_training_step(frame, backprop=False, compute_err=True) + loss0 += l0 + if isinstance(self.transport, Attention): + l1, err1 = self.attn_training_step(frame, backprop=False, compute_err=True) + loss1 += l1 + else: + l1, err1 = self.transport_training_step(frame, backprop=False, compute_err=True) + loss1 += l1 + loss0 /= self.val_repeats + loss1 /= self.val_repeats + val_total_loss = loss0 + loss1 + + return dict( + val_loss=val_total_loss, + val_loss0=loss0, + val_loss1=loss1, + val_attn_dist_err=err0['dist'], + val_attn_theta_err=err0['theta'], + val_trans_dist_err=err1['dist'], + val_trans_theta_err=err1['theta'], + ) + + def training_epoch_end(self, all_outputs): + super().training_epoch_end(all_outputs) + utils.set_seed(self.trainer.current_epoch+1) + + def validation_epoch_end(self, all_outputs): + mean_val_total_loss = np.mean([v['val_loss'].item() for v in all_outputs]) + mean_val_loss0 = np.mean([v['val_loss0'].item() for v in all_outputs]) + mean_val_loss1 = np.mean([v['val_loss1'].item() for v in all_outputs]) + total_attn_dist_err = np.sum([v['val_attn_dist_err'].sum() for v in all_outputs]) + total_attn_theta_err = np.sum([v['val_attn_theta_err'].sum() for v in all_outputs]) + total_trans_dist_err = np.sum([v['val_trans_dist_err'].sum() for v in all_outputs]) + total_trans_theta_err = np.sum([v['val_trans_theta_err'].sum() for v in all_outputs]) + + + self.log('vl/attn/loss', mean_val_loss0) + self.log('vl/trans/loss', mean_val_loss1) + self.log('vl/loss', mean_val_total_loss) + self.log('vl/total_attn_dist_err', total_attn_dist_err) + self.log('vl/total_attn_theta_err', total_attn_theta_err) + self.log('vl/total_trans_dist_err', total_trans_dist_err) + self.log('vl/total_trans_theta_err', total_trans_theta_err) + + print("\nAttn Err - Dist: {:.2f}, Theta: {:.2f}".format(total_attn_dist_err, total_attn_theta_err)) + print("Transport Err - Dist: {:.2f}, Theta: {:.2f}".format(total_trans_dist_err, total_trans_theta_err)) + + return dict( + val_loss=mean_val_total_loss, + val_loss0=mean_val_loss0, + mean_val_loss1=mean_val_loss1, + total_attn_dist_err=total_attn_dist_err, + total_attn_theta_err=total_attn_theta_err, + total_trans_dist_err=total_trans_dist_err, + total_trans_theta_err=total_trans_theta_err, + ) + + def act(self, obs, info=None, goal=None): # pylint: disable=unused-argument + """Run inference and return best action given visual observations.""" + # Get heightmap from RGB-D images. + img = self.test_ds.get_image(obs) + + # Attention model forward pass. + pick_inp = {'inp_img': img} + pick_conf = self.attn_forward(pick_inp) + + + pick_conf = pick_conf.detach().cpu().numpy() + argmax = np.argmax(pick_conf) + argmax = np.unravel_index(argmax, shape=pick_conf.shape) + p0_pix = argmax[:2] + p0_theta = argmax[2] * (2 * np.pi / pick_conf.shape[2]) + + # Transport model forward pass. + place_inp = {'inp_img': img, 'p0': p0_pix} + place_conf = self.trans_forward(place_inp) + place_conf = place_conf.permute(1, 2, 0) + place_conf = place_conf.detach().cpu().numpy() + argmax = np.argmax(place_conf) + argmax = np.unravel_index(argmax, shape=place_conf.shape) + p1_pix = argmax[:2] + p1_theta = argmax[2] * (2 * np.pi / place_conf.shape[2]) + + # Pixels to end effector poses. + hmap = img[:, :, 3] + p0_xyz = utils.pix_to_xyz(p0_pix, hmap, self.bounds, self.pix_size) + p1_xyz = utils.pix_to_xyz(p1_pix, hmap, self.bounds, self.pix_size) + p0_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p0_theta)) + p1_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p1_theta)) + + return { + 'pose0': (np.asarray(p0_xyz), np.asarray(p0_xyzw)), + 'pose1': (np.asarray(p1_xyz), np.asarray(p1_xyzw)), + 'pick': p0_pix, + 'place': p1_pix, + } + + def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_i, second_order_closure, on_tpu, using_native_amp, using_lbfgs): + pass + + def configure_optimizers(self): + pass + + def train_dataloader(self): + return self.train_loader + + def val_dataloader(self): + return self.test_loader + + def load(self, model_path): + self.load_state_dict(torch.load(model_path)['state_dict']) + self.to(device=self.device_type) + + +class OriginalTransporterAgent(TransporterAgent): + + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_fcn = 'plain_resnet' + self.attention = Attention( + stream_fcn=(stream_fcn, None), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = Transport( + stream_fcn=(stream_fcn, None), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + +class ClipUNetTransporterAgent(TransporterAgent): + + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_fcn = 'clip_unet' + self.attention = Attention( + stream_fcn=(stream_fcn, None), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = Transport( + stream_fcn=(stream_fcn, None), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + +class TwoStreamClipUNetTransporterAgent(TransporterAgent): + + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_one_fcn = 'plain_resnet' + stream_two_fcn = 'clip_unet' + self.attention = TwoStreamAttention( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransport( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + +class TwoStreamClipUNetLatTransporterAgent(TransporterAgent): + + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_one_fcn = 'plain_resnet_lat' + stream_two_fcn = 'clip_unet_lat' + self.attention = TwoStreamAttentionLat( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransportLat( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + +class TwoStreamClipWithoutSkipsTransporterAgent(TransporterAgent): + + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + # TODO: lateral version + stream_one_fcn = 'plain_resnet' + stream_two_fcn = 'clip_woskip' + self.attention = TwoStreamAttention( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransport( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + +class TwoStreamRN50BertUNetTransporterAgent(TransporterAgent): + + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + # TODO: lateral version + stream_one_fcn = 'plain_resnet' + stream_two_fcn = 'rn50_bert_unet' + self.attention = TwoStreamAttention( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransport( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) diff --git a/cliport/agents/transporter_image_goal.py b/cliport/agents/transporter_image_goal.py new file mode 100644 index 0000000000000000000000000000000000000000..ea0cd9d993c9546abc9f51f635ac05a464c6bf48 --- /dev/null +++ b/cliport/agents/transporter_image_goal.py @@ -0,0 +1,161 @@ +import numpy as np + +from cliport.utils import utils +from cliport.agents.transporter import OriginalTransporterAgent +from cliport.models.core.attention import Attention +from cliport.models.core.attention_image_goal import AttentionImageGoal +from cliport.models.core.transport_image_goal import TransportImageGoal + + +class ImageGoalTransporterAgent(OriginalTransporterAgent): + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_fcn = 'plain_resnet' + self.attention = AttentionImageGoal( + stream_fcn=(stream_fcn, None), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TransportImageGoal( + stream_fcn=(stream_fcn, None), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + def attn_forward(self, inp, softmax=True): + inp_img = inp['inp_img'] + goal_img = inp['goal_img'] + + out = self.attention.forward(inp_img, goal_img, softmax=softmax) + return out + + def attn_training_step(self, frame, goal, backprop=True, compute_err=False): + inp_img = frame['img'] + goal_img = goal['img'] + p0, p0_theta = frame['p0'], frame['p0_theta'] + + inp = {'inp_img': inp_img, 'goal_img': goal_img} + out = self.attn_forward(inp, softmax=False) + return self.attn_criterion(backprop, compute_err, inp, out, p0, p0_theta) + + def trans_forward(self, inp, softmax=True): + inp_img = inp['inp_img'] + goal_img = inp['goal_img'] + p0 = inp['p0'] + + out = self.transport.forward(inp_img, goal_img, p0, softmax=softmax) + return out + + def transport_training_step(self, frame, goal, backprop=True, compute_err=False): + inp_img = frame['img'] + goal_img = goal['img'] + p0 = frame['p0'] + p1, p1_theta = frame['p1'], frame['p1_theta'] + + inp = {'inp_img': inp_img, 'goal_img': goal_img, 'p0': p0} + out = self.trans_forward(inp, softmax=False) + err, loss = self.transport_criterion(backprop, compute_err, inp, out, p0, p1, p1_theta) + return loss, err + + def training_step(self, batch, batch_idx): + self.attention.train() + self.transport.train() + frame, goal = batch + + # Get training losses. + step = self.total_steps + 1 + loss0, err0 = self.attn_training_step(frame, goal) + if isinstance(self.transport, Attention): + loss1, err1 = self.attn_training_step(frame, goal) + else: + loss1, err1 = self.transport_training_step(frame, goal) + total_loss = loss0 + loss1 + self.log('tr/attn/loss', loss0) + self.log('tr/trans/loss', loss1) + self.log('tr/loss', total_loss) + self.total_steps = step + + self.trainer.train_loop.running_loss.append(total_loss) + + self.check_save_iteration() + + return dict( + loss=total_loss, + ) + + def validation_step(self, batch, batch_idx): + self.attention.eval() + self.transport.eval() + + loss0, loss1 = 0, 0 + for i in range(self.val_repeats): + frame, goal = batch + l0, err0 = self.attn_training_step(frame, goal, backprop=False, compute_err=True) + loss0 += l0 + if isinstance(self.transport, Attention): + l1, err1 = self.attn_training_step(frame, goal, backprop=False, compute_err=True) + loss1 += l1 + else: + l1, err1 = self.transport_training_step(frame, goal, backprop=False, compute_err=True) + loss1 += l1 + loss0 /= self.val_repeats + loss1 /= self.val_repeats + val_total_loss = loss0 + loss1 + + self.trainer.evaluation_loop.trainer.train_loop.running_loss.append(val_total_loss) + + return dict( + val_loss=val_total_loss, + val_loss0=loss0, + val_loss1=loss1, + val_attn_dist_err=err0['dist'], + val_attn_theta_err=err0['theta'], + val_trans_dist_err=err1['dist'], + val_trans_theta_err=err1['theta'], + ) + + def act(self, obs, info=None, goal=None): # pylint: disable=unused-argument + """Run inference and return best action given visual observations.""" + # Get heightmap from RGB-D images. + img = self.test_ds.get_image(obs) + goal_img = self.test_ds.get_image(goal[0]) + + # Attention model forward pass. + pick_conf = self.attention.forward(img, goal_img) + pick_conf = pick_conf.detach().cpu().numpy() + argmax = np.argmax(pick_conf) + argmax = np.unravel_index(argmax, shape=pick_conf.shape) + p0_pix = argmax[:2] + p0_theta = argmax[2] * (2 * np.pi / pick_conf.shape[2]) + + # Transport model forward pass. + place_conf = self.transport.forward(img, goal_img, p0_pix) + place_conf = place_conf.permute(1, 2, 0) + place_conf = place_conf.detach().cpu().numpy() + argmax = np.argmax(place_conf) + argmax = np.unravel_index(argmax, shape=place_conf.shape) + p1_pix = argmax[:2] + p1_theta = argmax[2] * (2 * np.pi / place_conf.shape[2]) + + # Pixels to end effector poses. + hmap = img[:, :, 3] + p0_xyz = utils.pix_to_xyz(p0_pix, hmap, self.bounds, self.pix_size) + p1_xyz = utils.pix_to_xyz(p1_pix, hmap, self.bounds, self.pix_size) + p0_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p0_theta)) + p1_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p1_theta)) + + return { + 'pose0': (np.asarray(p0_xyz), np.asarray(p0_xyzw)), + 'pose1': (np.asarray(p1_xyz), np.asarray(p1_xyzw)), + 'pick': p0_pix, + 'place': p1_pix, + } diff --git a/cliport/agents/transporter_lang_goal.py b/cliport/agents/transporter_lang_goal.py new file mode 100644 index 0000000000000000000000000000000000000000..e91ad03bcc5abcd9198252d0ec31493e4c290200 --- /dev/null +++ b/cliport/agents/transporter_lang_goal.py @@ -0,0 +1,454 @@ +import numpy as np + +from cliport.utils import utils +from cliport.agents.transporter import TransporterAgent + +from cliport.models.streams.one_stream_attention_lang_fusion import OneStreamAttentionLangFusion +from cliport.models.streams.one_stream_transport_lang_fusion import OneStreamTransportLangFusion +from cliport.models.streams.two_stream_attention_lang_fusion import TwoStreamAttentionLangFusion +from cliport.models.streams.two_stream_transport_lang_fusion import TwoStreamTransportLangFusion, TwoStreamTransportLangFusionLatReduce, TwoStreamTransportLangFusionLatPretrained18 +from cliport.models.streams.two_stream_attention_lang_fusion import TwoStreamAttentionLangFusionLat, TwoStreamAttentionLangFusionLatReduce + +from cliport.models.streams.two_stream_transport_lang_fusion import TwoStreamTransportLangFusionLatReduceOneStream +from cliport.models.streams.two_stream_transport_lang_fusion import TwoStreamTransportLangFusionLat +import torch +import time + + +class TwoStreamClipLingUNetTransporterAgent(TransporterAgent): + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_one_fcn = 'plain_resnet' + stream_two_fcn = 'clip_lingunet' + self.attention = TwoStreamAttentionLangFusion( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransportLangFusion( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + def attn_forward(self, inp, softmax=True): + inp_img = inp['inp_img'] + if type(inp_img) is not torch.Tensor: + inp_img = torch.from_numpy(inp_img).to('cuda').float().contiguous() + lang_goal = inp['lang_goal'] + + out = self.attention.forward(inp_img.float(), lang_goal, softmax=softmax) + return out + + def attn_training_step(self, frame, backprop=True, compute_err=False): + inp_img = frame['img'] + if type(inp_img) is not torch.Tensor: + inp_img = torch.from_numpy(inp_img).to('cuda').float() + p0, p0_theta = frame['p0'], frame['p0_theta'] + lang_goal = frame['lang_goal'] + + inp = {'inp_img': inp_img, 'lang_goal': lang_goal} + out = self.attn_forward(inp, softmax=False) + return self.attn_criterion(backprop, compute_err, inp, out, p0, p0_theta) + + def trans_forward(self, inp, softmax=True): + inp_img = inp['inp_img'] + if type(inp_img) is not torch.Tensor: + inp_img = torch.from_numpy(inp_img).to('cuda').float() + p0 = inp['p0'] + lang_goal = inp['lang_goal'] + out = self.transport.forward(inp_img.float(), p0, lang_goal, softmax=softmax) + return out + + def transport_training_step(self, frame, backprop=True, compute_err=False): + inp_img = frame['img'] + p0 = frame['p0'] + p1, p1_theta = frame['p1'], frame['p1_theta'] + lang_goal = frame['lang_goal'] + + inp = {'inp_img': inp_img, 'p0': p0, 'lang_goal': lang_goal} + out = self.trans_forward(inp, softmax=False) + err, loss = self.transport_criterion(backprop, compute_err, inp, out, p0, p1, p1_theta) + return loss, err + + def act(self, obs, info, goal=None): # pylint: disable=unused-argument + """Run inference and return best action given visual observations.""" + # Get heightmap from RGB-D images. + img = self.test_ds.get_image(obs) + lang_goal = info['lang_goal'] + + # Attention model forward pass. + pick_inp = {'inp_img': img, 'lang_goal': lang_goal} + pick_conf = self.attn_forward(pick_inp) + pick_conf = pick_conf[0].permute(1, 2, 0).detach().cpu().numpy() + # + argmax = np.argmax(pick_conf) + # import IPython; IPython.embed() + argmax = np.unravel_index(argmax, shape=pick_conf.shape) + p0_pix = argmax[:2] + p0_theta = argmax[2] * (2 * np.pi / pick_conf.shape[2]) + + # Transport model forward pass. + place_inp = {'inp_img': img, 'p0': p0_pix, 'lang_goal': lang_goal} + place_conf = self.trans_forward(place_inp) + place_conf = place_conf.squeeze().permute(1, 2, 0) + place_conf = place_conf.detach().cpu().numpy() + argmax = np.argmax(place_conf) + argmax = np.unravel_index(argmax, shape=place_conf.shape) + p1_pix = argmax[:2] + p1_theta = argmax[2] * (2 * np.pi / place_conf.shape[2]) + + # Pixels to end effector poses. + hmap = img[:, :, 3] + p0_xyz = utils.pix_to_xyz(p0_pix, hmap, self.bounds, self.pix_size) + p1_xyz = utils.pix_to_xyz(p1_pix, hmap, self.bounds, self.pix_size) + p0_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p0_theta)) + p1_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p1_theta)) + + return { + 'pose0': (np.asarray(p0_xyz), np.asarray(p0_xyzw)), + 'pose1': (np.asarray(p1_xyz), np.asarray(p1_xyzw)), + 'pick': [p0_pix[0], p0_pix[1], p0_theta], + 'place': [p1_pix[0], p1_pix[1], p1_theta], + } + + + def real_act(self, obs, info, goal=None): + """Run inference and return best action given real images.""" + + img = obs + lang_goal = info['lang_goal'] + # Attention model forward pass. + pick_inp = {'inp_img': img, 'lang_goal': lang_goal} + pick_conf = self.attn_forward(pick_inp) + pick_conf = pick_conf[0].permute(1, 2, 0).detach().cpu().numpy() + # + argmax = np.argmax(pick_conf) + # import IPython; IPython.embed() + argmax = np.unravel_index(argmax, shape=pick_conf.shape) + p0_pix = argmax[:2] + p0_theta = argmax[2] * (2 * np.pi / pick_conf.shape[2]) + + # Transport model forward pass. + place_inp = {'inp_img': img, 'p0': p0_pix, 'lang_goal': lang_goal} + place_conf = self.trans_forward(place_inp) + place_conf = place_conf.squeeze().permute(1, 2, 0) + place_conf = place_conf.detach().cpu().numpy() + argmax = np.argmax(place_conf) + argmax = np.unravel_index(argmax, shape=place_conf.shape) + p1_pix = argmax[:2] + p1_theta = argmax[2] * (2 * np.pi / place_conf.shape[2]) + + # Pixels to end effector poses. + hmap = img[:, :, 3] + p0_xyz = utils.pix_to_xyz(p0_pix, hmap, self.bounds, self.pix_size) + p1_xyz = utils.pix_to_xyz(p1_pix, hmap, self.bounds, self.pix_size) + p0_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p0_theta)) + p1_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p1_theta)) + + return { + 'pose0': (np.asarray(p0_xyz), np.asarray(p0_xyzw)), + 'pose1': (np.asarray(p1_xyz), np.asarray(p1_xyzw)), + 'pick': [p0_pix[0], p0_pix[1], p0_theta], + 'place': [p1_pix[0], p1_pix[1], p1_theta], + } + + +class TwoStreamClipFilmLingUNetLatTransporterAgent(TwoStreamClipLingUNetTransporterAgent): + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_one_fcn = 'plain_resnet_lat' + stream_two_fcn = 'clip_film_lingunet_lat' + self.attention = TwoStreamAttentionLangFusionLat( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransportLangFusionLat( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + +class TwoStreamClipLingUNetLatTransporterAgent(TwoStreamClipLingUNetTransporterAgent): # This is our model + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_one_fcn = 'plain_resnet_lat' + stream_two_fcn = 'clip_lingunet_lat' + self.attention = TwoStreamAttentionLangFusionLat( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransportLangFusionLat( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + +class TwoStreamMdetrLingUNetLatTransporterAgent(TwoStreamClipLingUNetTransporterAgent): + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_one_fcn = 'plain_resnet_lat_origin' + stream_two_fcn = 'mdetr_lingunet_lat_fuse' + + self.attention = TwoStreamAttentionLangFusionLat( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransportLangFusionLat( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + + + +class TwoStreamClipLingUNetLatTransporterAgentReduce(TwoStreamClipLingUNetTransporterAgent): # This is our model + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_one_fcn = 'plain_resnet_lat' + stream_two_fcn = 'clip_lingunet_lat' + self.attention = TwoStreamAttentionLangFusionLat( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransportLangFusionLatReduce( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + + +class TwoStreamClipLingUNetLatTransporterAgentReduceOneStream(TwoStreamClipLingUNetTransporterAgent): # This is our model + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_one_fcn = 'plain_resnet_lat' + stream_two_fcn = 'clip_lingunet_lat' + self.attention = TwoStreamAttentionLangFusionLatReduce( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransportLangFusionLatReduceOneStream( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + +class TwoStreamClipLingUNetLatTransporterAgentReducePretrained(TwoStreamClipLingUNetTransporterAgent): # This is our model + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_one_fcn = 'plain_resnet_lat' + stream_two_fcn = 'clip_lingunet_lat' + self.attention = TwoStreamAttentionLangFusionLat( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransportLangFusionLatPretrained18( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + + +class TwoStreamRN50BertLingUNetTransporterAgent(TwoStreamClipLingUNetTransporterAgent): + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_one_fcn = 'plain_resnet' + stream_two_fcn = 'rn50_bert_lingunet' + self.attention = TwoStreamAttentionLangFusion( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransportLangFusion( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + +class TwoStreamUntrainedRN50BertLingUNetTransporterAgent(TwoStreamClipLingUNetTransporterAgent): + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_one_fcn = 'plain_resnet' + stream_two_fcn = 'untrained_rn50_bert_lingunet' + self.attention = TwoStreamAttentionLangFusion( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransportLangFusion( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + +class TwoStreamRN50BertLingUNetLatTransporterAgent(TwoStreamClipLingUNetTransporterAgent): + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_one_fcn = 'plain_resnet_lat' + stream_two_fcn = 'rn50_bert_lingunet_lat' + self.attention = TwoStreamAttentionLangFusionLat( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = TwoStreamTransportLangFusionLat( + stream_fcn=(stream_one_fcn, stream_two_fcn), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + +class OriginalTransporterLangFusionAgent(TwoStreamClipLingUNetTransporterAgent): + + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_fcn = 'plain_resnet_lang' + self.attention = OneStreamAttentionLangFusion( + stream_fcn=(stream_fcn, None), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = OneStreamTransportLangFusion( + stream_fcn=(stream_fcn, None), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + + + +class ClipLingUNetTransporterAgent(TwoStreamClipLingUNetTransporterAgent): + + def __init__(self, name, cfg, train_ds, test_ds): + super().__init__(name, cfg, train_ds, test_ds) + + def _build_model(self): + stream_fcn = 'clip_lingunet' + self.attention = OneStreamAttentionLangFusion( + stream_fcn=(stream_fcn, None), + in_shape=self.in_shape, + n_rotations=1, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) + self.transport = OneStreamTransportLangFusion( + stream_fcn=(stream_fcn, None), + in_shape=self.in_shape, + n_rotations=self.n_rotations, + crop_size=self.crop_size, + preprocess=utils.preprocess, + cfg=self.cfg, + device=self.device_type, + ) \ No newline at end of file diff --git a/cliport/cfg/config.yaml b/cliport/cfg/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..318d7b6be0d454730001e552bb2b020f94a73ef5 --- /dev/null +++ b/cliport/cfg/config.yaml @@ -0,0 +1,30 @@ +# @package _global_ +root_dir: ${oc.env:GENSIM_ROOT} # set this ENV variable if you didn't `python setup.py develop` + +tag: default +debug: False +gpt_temperature: 0.8 # GPT-4 response temperature. higher means more diversity +prompt_folder: vanilla_task_generation_prompt # the prompt folder that stores the prompt chain +max_env_run_cnt: 3 # maximum number of runs for each environment +trials: 10 # how many times of spawning each environment generated +output_folder: 'output/output_stats' +model_output_dir: '' # to be filled in with date +gpt_model: "gpt-4-0613" # which openai gpt model to use +openai_key: ${oc.env:OPENAI_KEY} + +# Advanced options +task_description_candidate_num: -1 # the number of sample task descriptions. -1 means all +task_asset_candidate_num: -1 # the number of sample task descriptions. -1 means all +task_code_candidate_num: 4 # the number of sample task code. -1 means all + + +# Save and Load Memory +prompt_data_path: prompts/data/ +save_memory: False # save the assets, task code, task descriptions generated offline +load_memory: False # load the assets, task code, task descriptions generated offline +use_template: False # use template when constructing prompts, better for scaling +reflection_agreement_num: 2 # how many models that need to agree to add a new task in reflection + +target_task_name: "" # specific desired task name +save_code_early: False # ignore test and save the code after implementation +load_task_num: -1 # how many tasks to load from offline \ No newline at end of file diff --git a/cliport/cfg/data.yaml b/cliport/cfg/data.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ca0db5b9c0b77d0389cf68632ba21cb8a48fa8eb --- /dev/null +++ b/cliport/cfg/data.yaml @@ -0,0 +1,34 @@ +# Data Generation + +defaults: + - config + +hydra: + run: + dir: ${root_dir} + +data_dir: ${root_dir}/data # where to store dataset +assets_root: ${root_dir}/cliport/environments/assets/ +disp: False # visualize PyBullet +shared_memory: False +task: packing-boxes-pairs-seen-colors +mode: train # 'train' or 'val' or 'test' +n: 1000 # number of demos to generate +save_data: True # write episodes to disk + +dataset: + type: 'single' # 'single' or 'multi' + images: True + cache: True # load episodes to memory instead of reading from disk + augment: + theta_sigma: 60 # rotation sigma in degrees; N(mu = 0, sigma = theta_sigma). + +# record videos (super slow) +record: + save_video: False + save_video_path: ${data_dir}/${task}-${mode}/videos/ + add_text: False + add_task_text: True + fps: 20 + video_height: 640 + video_width: 720 diff --git a/cliport/cfg/eval.yaml b/cliport/cfg/eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d3d352c0436364ea40c4683b3335d9c68981bd02 --- /dev/null +++ b/cliport/cfg/eval.yaml @@ -0,0 +1,50 @@ +# Evaluation + +defaults: + - config + +hydra: + run: + dir: ${root_dir} + +mode: val # 'val' or 'test' + +# eval settings +agent: cliport +n_demos: 100 # number of val instances +train_demos: 100 # training demos used to train model +n_repeats: 1 # number of repeats +gpu: [0] +save_results: True # write results to json +update_results: False # overwrite existing json results? +checkpoint_type: 'val_missing' +val_on_heldout: True + +disp: False +shared_memory: False +eval_task: packing-boxes-pairs-seen-colors # task to evaluate the model on +model_task: ${eval_task} # task the model was trained on (e.g. multi-language-conditioned or packing-boxes-pairs-seen-colors) +type: single # 'single' or 'multi' + +# paths +model_dir: ${root_dir} +exp_folder: exps +data_dir: ${root_dir}/data +assets_root: ${root_dir}/cliport/environments/assets/ + +model_path: ${model_dir}/${exp_folder}/${model_task}-${agent}-n${train_demos}-train/checkpoints/ # path to pre-trained models +train_config: ${model_dir}/${exp_folder}/${model_task}-${agent}-n${train_demos}-train/.hydra/config.yaml # path to train config +save_path: ${model_dir}/${exp_folder}/${eval_task}-${agent}-n${train_demos}-train/checkpoints/ # path to save results +results_path: ${model_dir}/${exp_folder}/${eval_task}-${agent}-n${train_demos}-train/checkpoints/ # path to existing results + + +# record videos (super slow) +record: + save_video: False + save_video_path: ${model_dir}/${exp_folder}/${eval_task}-${agent}-n${train_demos}-train/videos/ + add_text: True + fps: 20 + video_height: 640 + video_width: 720 + add_task_text: False + blender_render: False # new: use blender recorder for rendering \ No newline at end of file diff --git a/cliport/cfg/train.yaml b/cliport/cfg/train.yaml new file mode 100644 index 0000000000000000000000000000000000000000..adffa9cdb118c4133be1b3f9035b5f8586beb898 --- /dev/null +++ b/cliport/cfg/train.yaml @@ -0,0 +1,61 @@ +# Training + +defaults: + - config + +hydra: + run: + dir: ${train.train_dir} + +dataset: + type: 'single' # 'single' or 'multi' + images: True + cache: True # load episodes to memory instead of reading from disk + augment: + theta_sigma: 60 # rotation sigma in degrees; N(mu = 0, sigma = theta_sigma). + +train: + # folders + model_task: ${train.task} + exp_folder: exps + train_dir: ${root_dir}/${train.exp_folder}/${train.model_task}-${train.agent}-n${train.n_demos}-train + data_dir: ${root_dir}/data + + # task configs + task: packing-boxes-pairs-seen-colors + agent: two_stream_full_clip_lingunet_lat_transporter + n_demos: 100 + n_steps: 61000 # original paper use 200000 for single task and use 601000 for multi-task models + + # hyper params + n_rotations: 36 + batch_size: 8 + batchnorm: False # important: False because batch_size=1 + lr: 1e-4 + + attn_stream_fusion_type: 'add' + trans_stream_fusion_type: 'conv' + lang_fusion_type: 'mult' + training_step_scale: 200 # How many epochs are needed. 100 data sample requires 20000 steps. -1 means ignored. + + # script configs + gpu: -1 # -1 for all + log: False # log metrics and stats to wandb + n_val: 1 + val_repeats: 1 + save_steps: [1000, 2000, 3000, 4000, 5000, 7000, 10000, 20000, 40000, 80000, 120000, 160000, 200000, 300000, 400000, 500000, 600000, 800000, 1000000, 1200000] + load_from_last_ckpt: False # still change to True + + # sim to real + data_augmentation: False # additional data augmentation for simtoreal +wandb: + run_name: 'cliport0' + logger: + entity: cliport + project: cliport + tags: [] + group: train + offline: False + saver: + upload: False + monitor: 'val_loss' \ No newline at end of file diff --git a/cliport/dataset.py b/cliport/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..14ad15a48d09bf5b820b9e22e76b2b56036384db --- /dev/null +++ b/cliport/dataset.py @@ -0,0 +1,972 @@ +"""Image dataset.""" + +import os +import pickle +import warnings + +import numpy as np +from torch.utils.data import Dataset + +from cliport import tasks +from cliport.tasks import cameras +from cliport.utils import utils +import traceback + +# See transporter.py, regression.py, dummy.py, task.py, etc. +PIXEL_SIZE = 0.003125 +CAMERA_CONFIG = cameras.RealSenseD415.CONFIG +BOUNDS = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.28]]) + +# Names as strings, REVERSE-sorted so longer (more specific) names are first. +TASK_NAMES = (tasks.names).keys() +TASK_NAMES = sorted(TASK_NAMES)[::-1] + + +class RavensDataset(Dataset): + """A simple image dataset class.""" + + def __init__(self, path, cfg, n_demos=0, augment=False): + """A simple RGB-D image dataset.""" + self._path = path + + self.cfg = cfg + self.sample_set = [] + self.max_seed = -1 + self.n_episodes = 0 + self.images = self.cfg['dataset']['images'] + self.cache = self.cfg['dataset']['cache'] + self.n_demos = n_demos + self.augment = augment + + self.aug_theta_sigma = self.cfg['dataset']['augment']['theta_sigma'] if 'augment' in self.cfg['dataset'] else 60 # legacy code issue: theta_sigma was newly added + self.pix_size = 0.003125 + self.in_shape = (320, 160, 6) + self.cam_config = cameras.RealSenseD415.CONFIG + self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.28]]) + + # Track existing dataset if it exists. + color_path = os.path.join(self._path, 'action') + if os.path.exists(color_path): + for fname in sorted(os.listdir(color_path)): + if '.pkl' in fname: + seed = int(fname[(fname.find('-') + 1):-4]) + self.n_episodes += 1 + self.max_seed = max(self.max_seed, seed) + + self._cache = {} + + if self.n_demos > 0: + self.images = self.cfg['dataset']['images'] + self.cache = self.cfg['dataset']['cache'] + + # Check if there sufficient demos in the dataset + if self.n_demos > self.n_episodes: + # raise Exception(f"Requested training on {self.n_demos} demos, but only {self.n_episodes} demos exist in the dataset path: {self._path}.") + print(f"Requested training on {self.n_demos} demos, but only {self.n_episodes} demos exist in the dataset path: {self._path}.") + self.n_demos = self.n_episodes + + episodes = np.random.choice(range(self.n_episodes), self.n_demos, False) + self.set(episodes) + + + def add(self, seed, episode): + """Add an episode to the dataset. + + Args: + seed: random seed used to initialize the episode. + episode: list of (obs, act, reward, info) tuples. + """ + color, depth, action, reward, info = [], [], [], [], [] + for obs, act, r, i in episode: + color.append(obs['color']) + depth.append(obs['depth']) + action.append(act) + reward.append(r) + info.append(i) + + color = np.uint8(color) + depth = np.float32(depth) + + def dump(data, field): + field_path = os.path.join(self._path, field) + if not os.path.exists(field_path): + os.makedirs(field_path) + fname = f'{self.n_episodes:06d}-{seed}.pkl' # -{len(episode):06d} + with open(os.path.join(field_path, fname), 'wb') as f: + pickle.dump(data, f) + + dump(color, 'color') + dump(depth, 'depth') + dump(action, 'action') + dump(reward, 'reward') + dump(info, 'info') + + self.n_episodes += 1 + self.max_seed = max(self.max_seed, seed) + + def set(self, episodes): + """Limit random samples to specific fixed set.""" + self.sample_set = episodes + + def load(self, episode_id, images=True, cache=False): + # TODO(lirui): consider loading into memory + def load_field(episode_id, field, fname): + + # Check if sample is in cache. + if cache: + if episode_id in self._cache: + if field in self._cache[episode_id]: + return self._cache[episode_id][field] + else: + self._cache[episode_id] = {} + + # Load sample from files. + path = os.path.join(self._path, field) + data = pickle.load(open(os.path.join(path, fname), 'rb')) + if cache: + self._cache[episode_id][field] = data + return data + + # Get filename and random seed used to initialize episode. + seed = None + path = os.path.join(self._path, 'action') + for fname in sorted(os.listdir(path)): + if f'{episode_id:06d}' in fname: + seed = int(fname[(fname.find('-') + 1):-4]) + + # Load data. + color = load_field(episode_id, 'color', fname) + depth = load_field(episode_id, 'depth', fname) + action = load_field(episode_id, 'action', fname) + reward = load_field(episode_id, 'reward', fname) + info = load_field(episode_id, 'info', fname) + + # Reconstruct episode. + episode = [] + for i in range(len(action)): + obs = {'color': color[i], 'depth': depth[i]} if images else {} + episode.append((obs, action[i], reward[i], info[i])) + return episode, seed + + print(f'{episode_id:06d} not in ', path) + + def get_image(self, obs, cam_config=None): + """Stack color and height images image.""" + + # if self.use_goal_image: + # colormap_g, heightmap_g = utils.get_fused_heightmap(goal, configs) + # goal_image = self.concatenate_c_h(colormap_g, heightmap_g) + # input_image = np.concatenate((input_image, goal_image), axis=2) + # assert input_image.shape[2] == 12, input_image.shape + + if cam_config is None: + cam_config = self.cam_config + + # Get color and height maps from RGB-D images. + cmap, hmap = utils.get_fused_heightmap( + obs, cam_config, self.bounds, self.pix_size) + img = np.concatenate((cmap, + hmap[Ellipsis, None], + hmap[Ellipsis, None], + hmap[Ellipsis, None]), axis=2) + assert img.shape == self.in_shape, img.shape + return img + + def process_sample(self, datum, augment=True): + # Get training labels from data sample. + (obs, act, _, info) = datum + img = self.get_image(obs) + + # p0, p1 = None, None + # p0_theta, p1_theta = None, None + # perturb_params = None + p0, p1 = np.zeros(1), np.zeros(1) + p0_theta, p1_theta = np.zeros(1), np.zeros(1) + perturb_params = np.zeros(5) + + if act: + p0_xyz, p0_xyzw = act['pose0'] + p1_xyz, p1_xyzw = act['pose1'] + p0 = utils.xyz_to_pix(p0_xyz, self.bounds, self.pix_size) + p0_theta = -np.float32(utils.quatXYZW_to_eulerXYZ(p0_xyzw)[2]) + p1 = utils.xyz_to_pix(p1_xyz, self.bounds, self.pix_size) + p1_theta = -np.float32(utils.quatXYZW_to_eulerXYZ(p1_xyzw)[2]) + p1_theta = p1_theta - p0_theta + p0_theta = 0 + + # Data augmentation. + if augment: + img, _, (p0, p1), perturb_params = utils.perturb(img, [p0, p1], theta_sigma=self.aug_theta_sigma) + # print("augment:", self.cfg['train']['data_augmentation']) + if self.cfg['train']['data_augmentation']: + # visualize original color, depth and augmented color and depth + # import IPython + # IPython.embed() + color = img[...,:3] + depth = img[...,3:] + original_color = color.copy() + original_depth = depth.copy() + + from cliport.utils.dataaug import chromatic_transform, add_noise, add_noise_depth + if np.random.rand(1) > 0.1: + color = chromatic_transform(color.astype(np.uint8)) + if np.random.rand(1) > 0.1: + color = add_noise(color) + if np.random.rand(1) > 0.1: + depth = add_noise_depth(depth) + + # visualization + # import matplotlib.pyplot as plt + # fig = plt.figure(figsize=(32, 18)) + # ax = fig.add_subplot(2, 2, 1) + # plt.imshow(original_color.astype(np.uint8)) + # ax = fig.add_subplot(2, 2, 2) + # plt.imshow(color.astype(np.uint8)) + # ax = fig.add_subplot(2, 2, 3) + # plt.imshow(original_depth) + # ax = fig.add_subplot(2, 2, 4) + # plt.imshow(depth) + # plt.show() + + color = color.astype(np.float32) + im = np.concatenate((color, depth), axis=-1) + # print("sample", p0,p1,p0_theta,p1_theta,perturb_params) + sample = { + 'img': img.copy(), + 'p0': np.array(p0).copy(), 'p0_theta': np.array(p0_theta).copy(), + 'p1': np.array(p1).copy(), 'p1_theta': np.array(p1_theta).copy() , + 'perturb_params': np.array(perturb_params).copy() + } + + # Add language goal if available. + if 'lang_goal' not in info: + warnings.warn("No language goal. Defaulting to 'task completed.'") + + if info and 'lang_goal' in info: + sample['lang_goal'] = info['lang_goal'] + else: + sample['lang_goal'] = "task completed." + + return sample + + def process_goal(self, goal, perturb_params): + # Get goal sample. + (obs, act, _, info) = goal + img = self.get_image(obs) + + # p0, p1 = None, None + # p0_theta, p1_theta = None, None + + p0, p1 = np.zeros(1), np.zeros(1) + p0_theta, p1_theta = np.zeros(1), np.zeros(1) + + # Data augmentation with specific params. + # try: + if perturb_params is not None and len(perturb_params) > 1: + img = utils.apply_perturbation(img, perturb_params) + + sample = { + 'img': img.copy(), + 'p0': p0 , 'p0_theta': np.array(p0_theta).copy(), + 'p1': p1, 'p1_theta': np.array(p1_theta).copy(), + 'perturb_params': np.array(perturb_params).copy() + } + + # Add language goal if available. + if 'lang_goal' not in info: + warnings.warn("No language goal. Defaulting to 'task completed.'") + # print("goal",p0,p1,p0_theta,p1_theta,perturb_params) + + if info and 'lang_goal' in info: + sample['lang_goal'] = info['lang_goal'] + else: + sample['lang_goal'] = "task completed." + + return sample + + def __len__(self): + return len(self.sample_set) + + def __getitem__(self, idx): + # Choose random episode. + # if len(self.sample_set) > 0: + # episode_id = np.random.choice(self.sample_set) + # else: + # episode_id = np.random.choice(range(self.n_episodes)) + episode_id = self.sample_set[idx] + res = self.load(episode_id, self.images, self.cache) + if res is None: + print("in get item", episode_id, self._path) + print("load sample return None. Reload") + print("Exception:", str(traceback.format_exc())) + return self[0] # + + episode, _ = res + # Is the task sequential like stack-block-pyramid-seq? + is_sequential_task = '-seq' in self._path.split("/")[-1] + + # Return random observation action pair (and goal) from episode. + i = np.random.choice(range(len(episode)-1)) + g = i+1 if is_sequential_task else -1 + sample, goal = episode[i], episode[g] + + # Process sample. + sample = self.process_sample(sample, augment=self.augment) + goal = self.process_goal(goal, perturb_params=sample['perturb_params']) + return sample, goal + + +class RavensMultiTaskDataset(RavensDataset): + + + def __init__(self, path, cfg, group='multi-all', + mode='train', n_demos=100, augment=False): + """A multi-task dataset.""" + self.root_path = path + self.mode = mode + if group not in self.MULTI_TASKS: + # generate the groups on the fly + self.tasks = list(set(group)) # .split(" ") + else: + self.tasks = self.MULTI_TASKS[group][mode] + + print("self.tasks:", self.tasks) + self.attr_train_task = self.MULTI_TASKS[group]['attr_train_task'] if group in self.MULTI_TASKS and 'attr_train_task' in self.MULTI_TASKS[group] else None + + self.cfg = cfg + self.sample_set = {} + self.max_seed = -1 + self.n_episodes = 0 + self.images = self.cfg['dataset']['images'] + self.cache = self.cfg['dataset']['cache'] + self.n_demos = n_demos + self.augment = augment + + self.aug_theta_sigma = self.cfg['dataset']['augment']['theta_sigma'] if 'augment' in self.cfg['dataset'] else 60 # legacy code issue: theta_sigma was newly added + self.pix_size = 0.003125 + self.in_shape = (320, 160, 6) + self.cam_config = cameras.RealSenseD415.CONFIG + self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.28]]) + + self.n_episodes = {} + episodes = {} + + for task in self.tasks: + task_path = os.path.join(self.root_path, f'{task}-{mode}') + action_path = os.path.join(task_path, 'action') + n_episodes = 0 + if os.path.exists(action_path): + for fname in sorted(os.listdir(action_path)): + if '.pkl' in fname: + n_episodes += 1 + self.n_episodes[task] = n_episodes + + if n_episodes == 0: + raise Exception(f"{task}-{mode} has 0 episodes. Remove it from the list in dataset.py") + + # Select random episode depending on the size of the dataset. + episodes[task] = np.random.choice(range(self.n_demos), min(self.n_demos, n_episodes), False) + + if self.n_demos > 0: + self.images = self.cfg['dataset']['images'] + self.cache = False # TODO(mohit): fix caching for multi-task dataset + self.set(episodes) + + self._path = None + self._task = None + + def __len__(self): + # Average number of episodes across all tasks + total_episodes = 0 + for _, episode_ids in self.sample_set.items(): + total_episodes += len(episode_ids) + avg_episodes = total_episodes # // len(self.sample_set) + return avg_episodes + + def __getitem__(self, idx): + # Choose random task. + self._task = self.tasks[idx % len(self.tasks)] # np.random.choice(self.tasks) + self._path = os.path.join(self.root_path, f'{self._task}') + + # Choose random episode. + if len(self.sample_set[self._task]) > 0: + episode_id = np.random.choice(self.sample_set[self._task]) + else: + episode_id = np.random.choice(range(self.n_episodes[self._task])) + + res = self.load(episode_id, self.images, self.cache) + if res is None: + print("failed in get item", episode_id, self._task, self._path) + print("Exception:", str(traceback.format_exc())) + + return self[np.random.randint(len(self))] # + + episode, _ = res + + # Is the task sequential like stack-block-pyramid-seq? + is_sequential_task = '-seq' in self._path.split("/")[-1] + + # Return observation action pair (and goal) from episode. + if len(episode) > 1: + i = np.random.choice(range(len(episode)-1)) + g = i+1 if is_sequential_task else -1 + sample, goal = episode[i], episode[g] + else: + sample, goal = episode[0], episode[0] + + # Process sample + sample = self.process_sample(sample, augment=self.augment) + goal = self.process_goal(goal, perturb_params=sample['perturb_params']) + + return sample, goal + + def add(self, seed, episode): + raise Exception("Adding tasks not supported with multi-task dataset") + + def load(self, episode_id, images=True, cache=False): + # if self.attr_train_task is None or self.mode in ['val', 'test']: + # self._task = np.random.choice(self.tasks) + # else: + # all_other_tasks = list(self.tasks) + # all_other_tasks.remove(self.attr_train_task) + # all_tasks = [self.attr_train_task] + all_other_tasks # add seen task in the front + + # # 50% chance of sampling the main seen task and 50% chance of sampling any other seen-unseen task + # mult_attr_seen_sample_prob = 0.5 + # sampling_probs = [(1-mult_attr_seen_sample_prob) / (len(all_tasks)-1)] * len(all_tasks) + # sampling_probs[0] = mult_attr_seen_sample_prob + + # self._task = np.random.choice(all_tasks, p=sampling_probs) + + self._path = os.path.join(self.root_path, f'{self._task}-{self.mode}') + return super().load(episode_id, images, cache) + + def get_curr_task(self): + return self._task + + + MULTI_TASKS = { + # new expeeriments + 'multi-gpt-test': { + 'train': ['align-box-corner', 'rainbow-stack'], + 'val': ['align-box-corner', 'rainbow-stack'], + 'test': ['align-box-corner', 'rainbow-stack'] + }, + + # all tasks + 'multi-all': { + 'train': [ + 'align-box-corner', + 'assembling-kits', + 'block-insertion', + 'manipulating-rope', + 'packing-boxes', + 'palletizing-boxes', + 'place-red-in-green', + 'stack-block-pyramid', + 'sweeping-piles', + 'towers-of-hanoi', + 'align-rope', + 'assembling-kits-seq-unseen-colors', + 'packing-boxes-pairs-unseen-colors', + 'packing-shapes', + 'packing-unseen-google-objects-seq', + 'packing-unseen-google-objects-group', + 'put-block-in-bowl-unseen-colors', + 'stack-block-pyramid-seq-unseen-colors', + 'separating-piles-unseen-colors', + 'towers-of-hanoi-seq-unseen-colors', + ], + 'val': [ + 'align-box-corner', + 'assembling-kits', + 'block-insertion', + 'manipulating-rope', + 'packing-boxes', + 'palletizing-boxes', + 'place-red-in-green', + 'stack-block-pyramid', + 'sweeping-piles', + 'towers-of-hanoi', + 'align-rope', + 'assembling-kits-seq-seen-colors', + 'assembling-kits-seq-unseen-colors', + 'packing-boxes-pairs-seen-colors', + 'packing-boxes-pairs-unseen-colors', + 'packing-shapes', + 'packing-seen-google-objects-seq', + 'packing-unseen-google-objects-seq', + 'packing-seen-google-objects-group', + 'packing-unseen-google-objects-group', + 'put-block-in-bowl-seen-colors', + 'put-block-in-bowl-unseen-colors', + 'stack-block-pyramid-seq-seen-colors', + 'stack-block-pyramid-seq-unseen-colors', + 'separating-piles-seen-colors', + 'separating-piles-unseen-colors', + 'towers-of-hanoi-seq-seen-colors', + 'towers-of-hanoi-seq-unseen-colors', + ], + 'test': [ + 'align-box-corner', + 'assembling-kits', + 'block-insertion', + 'manipulating-rope', + 'packing-boxes', + 'palletizing-boxes', + 'place-red-in-green', + 'stack-block-pyramid', + 'sweeping-piles', + 'towers-of-hanoi', + 'align-rope', + 'assembling-kits-seq-seen-colors', + 'assembling-kits-seq-unseen-colors', + 'packing-boxes-pairs-seen-colors', + 'packing-boxes-pairs-unseen-colors', + 'packing-shapes', + 'packing-seen-google-objects-seq', + 'packing-unseen-google-objects-seq', + 'packing-seen-google-objects-group', + 'packing-unseen-google-objects-group', + 'put-block-in-bowl-seen-colors', + 'put-block-in-bowl-unseen-colors', + 'stack-block-pyramid-seq-seen-colors', + 'stack-block-pyramid-seq-unseen-colors', + 'separating-piles-seen-colors', + 'separating-piles-unseen-colors', + 'towers-of-hanoi-seq-seen-colors', + 'towers-of-hanoi-seq-unseen-colors', + ], + }, + + # demo-conditioned tasks + 'multi-demo-conditioned': { + 'train': [ + 'align-box-corner', + 'assembling-kits', + 'block-insertion', + 'manipulating-rope', + 'packing-boxes', + 'palletizing-boxes', + 'place-red-in-green', + 'stack-block-pyramid', + 'sweeping-piles', + 'towers-of-hanoi', + ], + 'val': [ + 'align-box-corner', + 'assembling-kits', + 'block-insertion', + 'manipulating-rope', + 'packing-boxes', + 'palletizing-boxes', + 'place-red-in-green', + 'stack-block-pyramid', + 'sweeping-piles', + 'towers-of-hanoi', + ], + 'test': [ + 'align-box-corner', + 'assembling-kits', + 'block-insertion', + 'manipulating-rope', + 'packing-boxes', + 'palletizing-boxes', + 'place-red-in-green', + 'stack-block-pyramid', + 'sweeping-piles', + 'towers-of-hanoi', + ], + }, + + # goal-conditioned tasks + 'multi-language-conditioned': { + 'train': [ + 'align-rope', + 'assembling-kits-seq-unseen-colors', # unseen here refers to training only seen splits to be consitent with single-task setting + 'packing-boxes-pairs-unseen-colors', + 'packing-shapes', + 'packing-unseen-google-objects-seq', + 'packing-unseen-google-objects-group', + 'put-block-in-bowl-unseen-colors', + 'stack-block-pyramid-seq-unseen-colors', + 'separating-piles-unseen-colors', + 'towers-of-hanoi-seq-unseen-colors', + ], + 'val': [ + 'align-rope', + 'assembling-kits-seq-seen-colors', + 'assembling-kits-seq-unseen-colors', + 'packing-boxes-pairs-seen-colors', + 'packing-boxes-pairs-unseen-colors', + 'packing-shapes', + 'packing-seen-google-objects-seq', + 'packing-unseen-google-objects-seq', + 'packing-seen-google-objects-group', + 'packing-unseen-google-objects-group', + 'put-block-in-bowl-seen-colors', + 'put-block-in-bowl-unseen-colors', + 'stack-block-pyramid-seq-seen-colors', + 'stack-block-pyramid-seq-unseen-colors', + 'separating-piles-seen-colors', + 'separating-piles-unseen-colors', + 'towers-of-hanoi-seq-seen-colors', + 'towers-of-hanoi-seq-unseen-colors', + ], + 'test': [ + 'align-rope', + 'assembling-kits-seq-seen-colors', + 'assembling-kits-seq-unseen-colors', + 'packing-boxes-pairs-seen-colors', + 'packing-boxes-pairs-unseen-colors', + 'packing-shapes', + 'packing-seen-google-objects-seq', + 'packing-unseen-google-objects-seq', + 'packing-seen-google-objects-group', + 'packing-unseen-google-objects-group', + 'put-block-in-bowl-seen-colors', + 'put-block-in-bowl-unseen-colors', + 'stack-block-pyramid-seq-seen-colors', + 'stack-block-pyramid-seq-unseen-colors', + 'separating-piles-seen-colors', + 'separating-piles-unseen-colors', + 'towers-of-hanoi-seq-seen-colors', + 'towers-of-hanoi-seq-unseen-colors', + ], + }, + + + ##### multi-attr tasks + 'multi-attr-align-rope': { + 'train': [ + 'assembling-kits-seq-full', + 'packing-boxes-pairs-full', + 'packing-shapes', + 'packing-seen-google-objects-seq', + 'packing-seen-google-objects-group', + 'put-block-in-bowl-full', + 'stack-block-pyramid-seq-full', + 'separating-piles-full', + 'towers-of-hanoi-seq-full', + ], + 'val': [ + 'align-rope', + ], + 'test': [ + 'align-rope', + ], + 'attr_train_task': None, + }, + + 'multi-attr-packing-shapes': { + 'train': [ + 'align-rope', + 'assembling-kits-seq-full', + 'packing-boxes-pairs-full', + 'packing-seen-google-objects-seq', + 'packing-seen-google-objects-group', + 'put-block-in-bowl-full', + 'stack-block-pyramid-seq-full', + 'separating-piles-full', + 'towers-of-hanoi-seq-full', + ], + 'val': [ + 'packing-shapes', + ], + 'test': [ + 'packing-shapes', + ], + 'attr_train_task': None, + }, + + 'multi-attr-assembling-kits-seq-unseen-colors': { + 'train': [ + 'align-rope', + 'assembling-kits-seq-seen-colors', # seen only + 'packing-boxes-pairs-full', + 'packing-shapes', + 'packing-seen-google-objects-seq', + 'packing-seen-google-objects-group', + 'put-block-in-bowl-full', + 'stack-block-pyramid-seq-full', + 'separating-piles-full', + 'towers-of-hanoi-seq-full', + ], + 'val': [ + 'assembling-kits-seq-unseen-colors', + ], + 'test': [ + 'assembling-kits-seq-unseen-colors', + ], + 'attr_train_task': 'assembling-kits-seq-seen-colors', + }, + + 'multi-attr-packing-boxes-pairs-unseen-colors': { + 'train': [ + 'align-rope', + 'assembling-kits-seq-full', + 'packing-boxes-pairs-seen-colors', # seen only + 'packing-shapes', + 'packing-seen-google-objects-seq', + 'packing-seen-google-objects-group', + 'put-block-in-bowl-full', + 'stack-block-pyramid-seq-full', + 'separating-piles-full', + 'towers-of-hanoi-seq-full', + ], + 'val': [ + 'packing-boxes-pairs-unseen-colors', + ], + 'test': [ + 'packing-boxes-pairs-unseen-colors', + ], + 'attr_train_task': 'packing-boxes-pairs-seen-colors', + }, + + 'multi-attr-packing-unseen-google-objects-seq': { + 'train': [ + 'align-rope', + 'assembling-kits-seq-full', + 'packing-boxes-pairs-full', + 'packing-shapes', + 'packing-seen-google-objects-group', + 'put-block-in-bowl-full', + 'stack-block-pyramid-seq-full', + 'separating-piles-full', + 'towers-of-hanoi-seq-full', + ], + 'val': [ + 'packing-unseen-google-objects-seq', + ], + 'test': [ + 'packing-unseen-google-objects-seq', + ], + 'attr_train_task': 'packing-seen-google-objects-group', + }, + + 'multi-attr-packing-unseen-google-objects-group': { + 'train': [ + 'align-rope', + 'assembling-kits-seq-full', + 'packing-boxes-pairs-full', + 'packing-shapes', + 'packing-seen-google-objects-seq', + 'put-block-in-bowl-full', + 'stack-block-pyramid-seq-full', + 'separating-piles-full', + 'towers-of-hanoi-seq-full', + ], + 'val': [ + 'packing-unseen-google-objects-group', + ], + 'test': [ + 'packing-unseen-google-objects-group', + ], + 'attr_train_task': 'packing-seen-google-objects-seq', + }, + + 'multi-attr-put-block-in-bowl-unseen-colors': { + 'train': [ + 'align-rope', + 'assembling-kits-seq-full', + 'packing-boxes-pairs-full', + 'packing-shapes', + 'packing-seen-google-objects-seq', + 'packing-seen-google-objects-group', + 'put-block-in-bowl-seen-colors', # seen only + 'stack-block-pyramid-seq-full', + 'separating-piles-full', + 'towers-of-hanoi-seq-full', + ], + 'val': [ + 'put-block-in-bowl-unseen-colors', + ], + 'test': [ + 'put-block-in-bowl-unseen-colors', + ], + 'attr_train_task': 'put-block-in-bowl-seen-colors', + }, + + 'multi-attr-stack-block-pyramid-seq-unseen-colors': { + 'train': [ + 'align-rope', + 'assembling-kits-seq-full', + 'packing-boxes-pairs-full', + 'packing-shapes', + 'packing-seen-google-objects-seq', + 'packing-seen-google-objects-group', + 'put-block-in-bowl-full', + 'stack-block-pyramid-seq-seen-colors', # seen only + 'separating-piles-full', + 'towers-of-hanoi-seq-full', + ], + 'val': [ + 'stack-block-pyramid-seq-unseen-colors', + ], + 'test': [ + 'stack-block-pyramid-seq-unseen-colors', + ], + 'attr_train_task': 'stack-block-pyramid-seq-seen-colors', + }, + + 'multi-attr-separating-piles-unseen-colors': { + 'train': [ + 'align-rope', + 'assembling-kits-seq-full', + 'packing-boxes-pairs-full', + 'packing-shapes', + 'packing-seen-google-objects-seq', + 'packing-seen-google-objects-group', + 'put-block-in-bowl-full', + 'stack-block-pyramid-seq-full', + 'separating-piles-seen-colors', # seen only + 'towers-of-hanoi-seq-full', + ], + 'val': [ + 'separating-piles-unseen-colors', + ], + 'test': [ + 'separating-piles-unseen-colors', + ], + 'attr_train_task': 'separating-piles-seen-colors', + }, + + 'multi-attr-towers-of-hanoi-seq-unseen-colors': { + 'train': [ + 'align-rope', + 'assembling-kits-seq-full', + 'packing-boxes-pairs-full', + 'packing-shapes', + 'packing-seen-google-objects-seq', + 'packing-seen-google-objects-group', + 'put-block-in-bowl-full', + 'stack-block-pyramid-seq-full', + 'separating-piles-full', + 'towers-of-hanoi-seq-seen-colors', # seen only + ], + 'val': [ + 'towers-of-hanoi-seq-unseen-colors', + ], + 'test': [ + 'towers-of-hanoi-seq-unseen-colors', + ], + 'attr_train_task': 'towers-of-hanoi-seq-seen-colors', + }, + + } + + + +class RavenMultiTaskDatasetBalance(RavensMultiTaskDataset): + def __init__(self, path, cfg, group='multi-all', + mode='train', n_demos=100, augment=False, balance_weight=0.1): + """A multi-task dataset for balancing data.""" + self.root_path = path + self.mode = mode + if group not in self.MULTI_TASKS: + # generate the groups on the fly + self.tasks = group# .split(" ") + else: + self.tasks = self.MULTI_TASKS[group][mode] + + print("self.tasks:", self.tasks) + self.attr_train_task = self.MULTI_TASKS[group]['attr_train_task'] if group in self.MULTI_TASKS and 'attr_train_task' in self.MULTI_TASKS[group] else None + + self.cfg = cfg + self.sample_set = {} + self.max_seed = -1 + self.n_episodes = 0 + self.images = self.cfg['dataset']['images'] + self.cache = self.cfg['dataset']['cache'] + self.n_demos = n_demos + self.augment = augment + + self.aug_theta_sigma = self.cfg['dataset']['augment']['theta_sigma'] if 'augment' in self.cfg['dataset'] else 60 # legacy code issue: theta_sigma was newly added + self.pix_size = 0.003125 + self.in_shape = (320, 160, 6) + self.cam_config = cameras.RealSenseD415.CONFIG + self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.28]]) + + self.n_episodes = {} + episodes = {} + + for task in self.tasks: + task_path = os.path.join(self.root_path, f'{task}-{mode}') + action_path = os.path.join(task_path, 'action') + n_episodes = 0 + if os.path.exists(action_path): + for fname in sorted(os.listdir(action_path)): + if '.pkl' in fname: + n_episodes += 1 + self.n_episodes[task] = n_episodes + + if n_episodes == 0: + raise Exception(f"{task}-{mode} has 0 episodes. Remove it from the list in dataset.py") + + # Select random episode depending on the size of the dataset. + if task in self.ORIGINAL_NAMES and self.mode == 'train': + assert self.n_demos < 200 # otherwise, we need to change the code below + episodes[task] = np.random.choice(range(n_episodes), min(int(self.n_demos*balance_weight), n_episodes), False) + else: + episodes[task] = np.random.choice(range(n_episodes), min(self.n_demos, n_episodes), False) + + if self.n_demos > 0: + self.images = self.cfg['dataset']['images'] + self.cache = False + self.set(episodes) + + self._path = None + self._task = None + + + + ORIGINAL_NAMES = [ + # demo conditioned + 'align-box-corner', + 'assembling-kits', + 'assembling-kits-easy', + 'block-insertion', + 'block-insertion-easy', + 'block-insertion-nofixture', + 'block-insertion-sixdof', + 'block-insertion-translation', + 'manipulating-rope', + 'packing-boxes', + 'palletizing-boxes', + 'place-red-in-green', + 'stack-block-pyramid', + 'sweeping-piles', + 'towers-of-hanoi', + 'gen-task', + # goal conditioned + 'align-rope', + 'assembling-kits-seq', + 'assembling-kits-seq-seen-colors', + 'assembling-kits-seq-unseen-colors', + 'assembling-kits-seq-full', + 'packing-shapes', + 'packing-boxes-pairs', + 'packing-boxes-pairs-seen-colors', + 'packing-boxes-pairs-unseen-colors', + 'packing-boxes-pairs-full', + 'packing-seen-google-objects-seq', + 'packing-unseen-google-objects-seq', + 'packing-seen-google-objects-group', + 'packing-unseen-google-objects-group', + 'put-block-in-bowl', + 'put-block-in-bowl-seen-colors', + 'put-block-in-bowl-unseen-colors', + 'put-block-in-bowl-full', + 'stack-block-pyramid-seq', + 'stack-block-pyramid-seq-seen-colors', + 'stack-block-pyramid-seq-unseen-colors', + 'stack-block-pyramid-seq-full', + 'separating-piles', + 'separating-piles-seen-colors', + 'separating-piles-unseen-colors', + 'separating-piles-full', + 'towers-of-hanoi-seq', + 'towers-of-hanoi-seq-seen-colors', + 'towers-of-hanoi-seq-unseen-colors', + 'towers-of-hanoi-seq-full', + ] \ No newline at end of file diff --git a/cliport/demos.py b/cliport/demos.py new file mode 100644 index 0000000000000000000000000000000000000000..8727658eb6e5d3af793fb8b1dbd55717c04031a6 --- /dev/null +++ b/cliport/demos.py @@ -0,0 +1,117 @@ +"""Data collection script.""" + +import os +import hydra +import numpy as np +import random + +from cliport import tasks +from cliport.dataset import RavensDataset +from cliport.environments.environment import Environment +import IPython +import random + +@hydra.main(config_path='./cfg', config_name='data') +def main(cfg): + # Initialize environment and task. + env = Environment( + cfg['assets_root'], + disp=cfg['disp'], + shared_memory=cfg['shared_memory'], + hz=480, + record_cfg=cfg['record'] + ) + cfg['task'] = cfg['task'].replace("_", "-") + task = tasks.names[cfg['task']]() + task.mode = cfg['mode'] + record = cfg['record']['save_video'] + save_data = cfg['save_data'] + + # Initialize scripted oracle agent and dataset. + agent = task.oracle(env) + data_path = os.path.join(cfg['data_dir'], "{}-{}".format(cfg['task'], task.mode)) + dataset = RavensDataset(data_path, cfg, n_demos=0, augment=False) + print(f"Saving to: {data_path}") + print(f"Mode: {task.mode}") + + # Train seeds are even and val/test seeds are odd. Test seeds are offset by 10000 + seed = dataset.max_seed + max_eps = 3 * cfg['n'] + + if seed < 0: + if task.mode == 'train': + seed = -2 + elif task.mode == 'val': # NOTE: beware of increasing val set to >100 + seed = -1 + elif task.mode == 'test': + seed = -1 + 10000 + else: + raise Exception("Invalid mode. Valid options: train, val, test") + + if 'regenerate_data' in cfg: + dataset.n_episodes = 0 + + curr_run_eps = 0 + # Collect training data from oracle demonstrations. + while dataset.n_episodes < cfg['n'] and curr_run_eps < max_eps: + # for epi_idx in range(cfg['n']): + episode, total_reward = [], 0 + seed += 2 + + # Set seeds. + np.random.seed(seed) + random.seed(seed) + print('Oracle demo: {}/{} | Seed: {}'.format(dataset.n_episodes + 1, cfg['n'], seed)) + try: + curr_run_eps += 1 # make sure exits the loop + env.set_task(task) + obs = env.reset() + info = env.info + reward = 0 + + # Unlikely, but a safety check to prevent leaks. + if task.mode == 'val' and seed > (-1 + 10000): + raise Exception("!!! Seeds for val set will overlap with the test set !!!") + + # Start video recording (NOTE: super slow) + if record: + env.start_rec(f'{dataset.n_episodes+1:06d}') + + + # Rollout expert policy + for _ in range(task.max_steps): + act = agent.act(obs, info) + episode.append((obs, act, reward, info)) + lang_goal = info['lang_goal'] + obs, reward, done, info = env.step(act) + total_reward += reward + print(f'Total Reward: {total_reward:.3f} | Done: {done} | Goal: {lang_goal}') + if done: + break + if record: + env.end_rec() + + except Exception as e: + from pygments import highlight + from pygments.lexers import PythonLexer + from pygments.formatters import TerminalFormatter + import traceback + + to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter()) + print(to_print) + if record: + env.end_rec() + continue + + episode.append((obs, None, reward, info)) + + # Only save completed demonstrations. + if save_data and total_reward > 0.99: + dataset.add(seed, episode) + if hasattr(env, 'blender_recorder'): + print("blender pickle saved to ", '{}/blender_demo_{}.pkl'.format(data_path, dataset.n_episodes)) + env.blender_recorder.save('{}/blender_demo_{}.pkl'.format(data_path, dataset.n_episodes)) + + +if __name__ == '__main__': + main() diff --git a/cliport/environments/__init__.py b/cliport/environments/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/cliport/environments/assets/bags/bl_sphere_bag_basic_000.mtl b/cliport/environments/assets/bags/bl_sphere_bag_basic_000.mtl new file mode 100644 index 0000000000000000000000000000000000000000..3653cb127c459a276be6dc7c715239b75e2ba1c4 --- /dev/null +++ b/cliport/environments/assets/bags/bl_sphere_bag_basic_000.mtl @@ -0,0 +1,12 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl CustomColor.001 +Ns 323.999994 +Ka 1.000000 1.000000 1.000000 +Kd 0.000000 0.000000 1.000000 +Ks 0.500000 0.500000 0.500000 +Ke 0.000000 0.000000 0.000000 +Ni 1.000000 +d 1.000000 +illum 2 diff --git a/cliport/environments/assets/bags/bl_sphere_bag_basic_000.obj b/cliport/environments/assets/bags/bl_sphere_bag_basic_000.obj new file mode 100644 index 0000000000000000000000000000000000000000..6b82795a3530b8b92fc53b9f022508a85ff76ee0 --- /dev/null +++ b/cliport/environments/assets/bags/bl_sphere_bag_basic_000.obj @@ -0,0 +1,1587 @@ +# Blender v2.82 (sub 7) OBJ File: '' +# www.blender.org +mtllib bl_sphere_bag_basic_000.mtl +o Sphere +v -4.000000 2.565686 -0.565685 +v -4.000000 2.444456 -0.665176 +v -4.000000 2.306147 -0.739104 +v -4.000000 2.156072 -0.784628 +v -4.000000 2.000000 -0.800000 +v -4.000000 1.555544 -0.665176 +v -4.000000 1.215372 -0.156072 +v -3.889640 2.565686 -0.554816 +v -3.870231 2.444456 -0.652395 +v -3.855808 2.306147 -0.724902 +v -3.846927 2.156072 -0.769552 +v -3.843928 2.000000 -0.784628 +v -3.846927 1.843928 -0.769552 +v -3.855808 1.693853 -0.724902 +v -3.870231 1.555544 -0.652395 +v 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a/cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.4_numV_321_top_ring.txt b/cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.4_numV_321_top_ring.txt new file mode 100644 index 0000000000000000000000000000000000000000..411ea24170c2cd6932c7b953ccf44d29331b8231 --- /dev/null +++ b/cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.4_numV_321_top_ring.txt @@ -0,0 +1,32 @@ +0 0.00000 0.92388 0.38268 +5 0.18024 0.90613 0.38268 +15 0.35355 0.85355 0.38268 +25 0.51328 0.76818 0.38268 +35 0.65328 0.65328 0.38268 +45 0.76818 0.51328 0.38268 +55 0.85355 0.35355 0.38268 +65 0.90613 0.18024 0.38268 +75 0.92388 -0.00000 0.38268 +85 0.90613 -0.18024 0.38268 +95 0.85355 -0.35355 0.38268 +105 0.76818 -0.51328 0.38268 +115 0.65328 -0.65328 0.38268 +125 0.51328 -0.76818 0.38268 +136 0.35355 -0.85355 0.38268 +146 0.18024 -0.90613 0.38268 +156 0.00000 -0.92388 0.38268 +166 -0.18024 -0.90613 0.38268 +176 -0.35355 -0.85355 0.38268 +186 -0.51328 -0.76818 0.38268 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4094/4094/4382 diff --git a/cliport/environments/assets/box/box-template.urdf b/cliport/environments/assets/box/box-template.urdf new file mode 100644 index 0000000000000000000000000000000000000000..9793c39ba8f50e5d7c9787c54a830778b4a3d5d1 --- /dev/null +++ b/cliport/environments/assets/box/box-template.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/box/box-template_DIM.urdf b/cliport/environments/assets/box/box-template_DIM.urdf new file mode 100644 index 0000000000000000000000000000000000000000..9793c39ba8f50e5d7c9787c54a830778b4a3d5d1 --- /dev/null +++ b/cliport/environments/assets/box/box-template_DIM.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/cable/cable.urdf b/cliport/environments/assets/cable/cable.urdf new file mode 100644 index 0000000000000000000000000000000000000000..df14d015fd611838ab36bc4931d7587cf2e08a71 --- /dev/null +++ b/cliport/environments/assets/cable/cable.urdf @@ -0,0 +1,66 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/cloth/bl_cloth_05_cuts.obj b/cliport/environments/assets/cloth/bl_cloth_05_cuts.obj new file mode 100644 index 0000000000000000000000000000000000000000..064c58f56da40ad7725957d6ead0eb94040d4a6a --- /dev/null +++ b/cliport/environments/assets/cloth/bl_cloth_05_cuts.obj @@ -0,0 +1,73 @@ +# Blender v2.82 (sub 7) OBJ File: '' +# www.blender.org +mtllib bl_cloth_05_cuts.mtl +o Grid +v -1.000000 0.000000 1.000000 +v -0.500000 0.000000 1.000000 +v 0.000000 0.000000 1.000000 +v 0.500000 0.000000 1.000000 +v 1.000000 0.000000 1.000000 +v -1.000000 0.000000 0.500000 +v -0.500000 0.000000 0.500000 +v 0.000000 0.000000 0.500000 +v 0.500000 0.000000 0.500000 +v 1.000000 0.000000 0.500000 +v -1.000000 0.000000 0.000000 +v -0.500000 0.000000 0.000000 +v 0.000000 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15//1 10//1 1//1 +f 6//1 23//1 15//1 +f 24//1 3//1 14//1 +f 20//1 14//1 5//1 +f 9//1 24//1 20//1 +f 25//1 8//1 18//1 +f 21//1 18//1 9//1 +f 7//1 25//1 21//1 +f 12//1 4//1 17//1 +f 18//1 8//1 13//1 +f 19//1 9//1 20//1 +f 16//1 7//1 21//1 +f 22//1 21//1 9//1 +f 11//1 22//1 19//1 +f 2//1 16//1 22//1 +f 23//1 20//1 5//1 +f 15//1 23//1 10//1 +f 6//1 19//1 23//1 +f 24//1 13//1 3//1 +f 20//1 24//1 14//1 +f 9//1 18//1 24//1 +f 25//1 17//1 8//1 +f 21//1 25//1 18//1 +f 7//1 12//1 25//1 diff --git a/cliport/environments/assets/cloth/cloth_z_up.obj b/cliport/environments/assets/cloth/cloth_z_up.obj new file mode 100644 index 0000000000000000000000000000000000000000..e2667a984d216c9a0e63b14c8fb61b03f2668e98 --- /dev/null +++ b/cliport/environments/assets/cloth/cloth_z_up.obj @@ -0,0 +1,64 @@ +# Blender v2.79 (sub 0) OBJ File: '' +# www.blender.org +mtllib cloth_z_up.mtl +o Plane_Plane.001 +v -1.000000 -1.000000 0.000000 +v 1.000000 -1.000000 0.000000 +v -1.000000 1.000000 -0.000000 +v 1.000000 1.000000 -0.000000 +v -1.000000 0.000000 0.000000 +v -0.000000 -1.000000 0.000000 +v 1.000000 -0.000000 -0.000000 +v 0.000000 1.000000 -0.000000 +v 0.000000 0.000000 0.000000 +v -1.000000 -0.500000 0.000000 +v 0.500000 -1.000000 0.000000 +v 1.000000 0.500000 -0.000000 +v -0.500000 1.000000 -0.000000 +v -1.000000 0.500000 -0.000000 +v -0.500000 -1.000000 0.000000 +v 1.000000 -0.500000 0.000000 +v 0.500000 1.000000 -0.000000 +v 0.000000 0.500000 -0.000000 +v -0.000000 -0.500000 0.000000 +v -0.500000 0.000000 0.000000 +v 0.500000 -0.000000 -0.000000 +v 0.500000 -0.500000 0.000000 +v -0.500000 -0.500000 0.000000 +v -0.500000 0.500000 -0.000000 +v 0.500000 0.500000 -0.000000 +vn 0.0000 0.0000 1.0000 +usemtl None +s off +f 12//1 17//1 25//1 +f 18//1 13//1 24//1 +f 19//1 20//1 23//1 +f 16//1 21//1 22//1 +f 22//1 9//1 19//1 +f 11//1 19//1 6//1 +f 2//1 22//1 11//1 +f 23//1 5//1 10//1 +f 15//1 10//1 1//1 +f 6//1 23//1 15//1 +f 24//1 3//1 14//1 +f 20//1 14//1 5//1 +f 9//1 24//1 20//1 +f 25//1 8//1 18//1 +f 21//1 18//1 9//1 +f 7//1 25//1 21//1 +f 12//1 4//1 17//1 +f 18//1 8//1 13//1 +f 19//1 9//1 20//1 +f 16//1 7//1 21//1 +f 22//1 21//1 9//1 +f 11//1 22//1 19//1 +f 2//1 16//1 22//1 +f 23//1 20//1 5//1 +f 15//1 23//1 10//1 +f 6//1 19//1 23//1 +f 24//1 13//1 3//1 +f 20//1 24//1 14//1 +f 9//1 18//1 24//1 +f 25//1 17//1 8//1 +f 21//1 25//1 18//1 +f 7//1 12//1 25//1 diff --git a/cliport/environments/assets/container/container-template.urdf b/cliport/environments/assets/container/container-template.urdf new file mode 100644 index 0000000000000000000000000000000000000000..8eeeca00e6b33d80b1418fe2f9c540e0859a3b75 --- /dev/null +++ b/cliport/environments/assets/container/container-template.urdf @@ -0,0 +1,98 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/container/container-template_DIM_HALF.urdf b/cliport/environments/assets/container/container-template_DIM_HALF.urdf new file mode 100644 index 0000000000000000000000000000000000000000..8eeeca00e6b33d80b1418fe2f9c540e0859a3b75 --- /dev/null +++ b/cliport/environments/assets/container/container-template_DIM_HALF.urdf @@ -0,0 +1,98 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/corner/corner-template.urdf b/cliport/environments/assets/corner/corner-template.urdf new file mode 100755 index 0000000000000000000000000000000000000000..27142a65a190cb88d0f2275a3c64b2d7df40ae4f --- /dev/null +++ b/cliport/environments/assets/corner/corner-template.urdf @@ -0,0 +1,49 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/corner/corner-template_DIMX_DIMY.urdf b/cliport/environments/assets/corner/corner-template_DIMX_DIMY.urdf new file mode 100755 index 0000000000000000000000000000000000000000..27142a65a190cb88d0f2275a3c64b2d7df40ae4f --- /dev/null +++ b/cliport/environments/assets/corner/corner-template_DIMX_DIMY.urdf @@ -0,0 +1,49 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/cylinder/cylinder-template.urdf b/cliport/environments/assets/cylinder/cylinder-template.urdf new file mode 100644 index 0000000000000000000000000000000000000000..3fb7bfdda25eb243d53e0dfc7400c3811611b08d --- /dev/null +++ b/cliport/environments/assets/cylinder/cylinder-template.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/cylinder/cylinder-template_DIMR_DIMH.urdf b/cliport/environments/assets/cylinder/cylinder-template_DIMR_DIMH.urdf new file mode 100644 index 0000000000000000000000000000000000000000..969d234ed6cc064cb3e81ec7bbc491e89daa5627 --- /dev/null +++ b/cliport/environments/assets/cylinder/cylinder-template_DIMR_DIMH.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/cylinder/cylinder-template_old.urdf b/cliport/environments/assets/cylinder/cylinder-template_old.urdf new file mode 100644 index 0000000000000000000000000000000000000000..3fb7bfdda25eb243d53e0dfc7400c3811611b08d --- /dev/null +++ b/cliport/environments/assets/cylinder/cylinder-template_old.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/hanoi/block.urdf b/cliport/environments/assets/hanoi/block.urdf new file mode 100644 index 0000000000000000000000000000000000000000..6047ef37b69d4a2898c86ecba47d8ef22055c95e --- /dev/null +++ b/cliport/environments/assets/hanoi/block.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/hanoi/disk0.obj b/cliport/environments/assets/hanoi/disk0.obj new file mode 100644 index 0000000000000000000000000000000000000000..1cfd6b96bac089dc13936a57ce4e94a8bb509e85 --- /dev/null +++ b/cliport/environments/assets/hanoi/disk0.obj @@ -0,0 +1,833 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 11.597 9.517 -5 +v 10.608 10.608 -5 +v 12.474 8.335 -5 +v -10.608 10.608 -5 +v -11.597 9.517 -5 +v 13.231 7.072 -5 +v -7.072 -13.231 -5 +v -5.741 -13.861 -5 +v 13.861 5.741 -5 +v -8.335 -12.474 -5 +v 13.861 -5.741 5 +v 13.231 -7.072 5 +v 29.424 5.853 5 +v -9.517 -11.597 -5 +v 29.424 5.853 -5 +v 12.474 -8.335 5 +v 28.708 8.709 -5 +v 14.356 4.355 -5 +v -10.608 -10.608 -5 +v 28.708 8.709 5 +v 11.597 -9.517 5 +v 10.608 -10.608 5 +v 26.458 14.142 5 +v 26.458 14.142 -5 +v 24.944 16.667 -5 +v 24.944 16.667 5 +v 14.714 2.927 -5 +v 14.93 1.471 -5 +v 23.19 19.032 -5 +v 23.19 19.032 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a/cliport/environments/assets/hanoi/stand.urdf b/cliport/environments/assets/hanoi/stand.urdf new file mode 100644 index 0000000000000000000000000000000000000000..77370ce44bdd7e2d0e4fd5af2186f7ec69cb9d8d --- /dev/null +++ b/cliport/environments/assets/hanoi/stand.urdf @@ -0,0 +1,141 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/insertion/ell.urdf b/cliport/environments/assets/insertion/ell.urdf new file mode 100755 index 0000000000000000000000000000000000000000..56d62e91740f7e102decf1e2033e98818dca1b68 --- /dev/null +++ b/cliport/environments/assets/insertion/ell.urdf @@ -0,0 +1,49 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/insertion/fixture.urdf b/cliport/environments/assets/insertion/fixture.urdf new file mode 100644 index 0000000000000000000000000000000000000000..6962d3d85de6ebb7629599054048f0e4dcbaf518 --- /dev/null +++ b/cliport/environments/assets/insertion/fixture.urdf @@ -0,0 +1,137 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/kitting/00.obj b/cliport/environments/assets/kitting/00.obj new file mode 100644 index 0000000000000000000000000000000000000000..7a2c0a5ac2abaa56a2e4c1c1273e1c04f1d4cfa5 --- /dev/null +++ b/cliport/environments/assets/kitting/00.obj @@ -0,0 +1,349 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v -2.837 1.23 0 +v -1.813 1.23 0 +v -0.696 1.311 20 +v 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@@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 0.2083 3.8632 0 +v 0.2675 4.067 0 +v -1.5145 -0.657 0 +v -2.5945 -4.174 0 +v -4.4995 -9.492 0 +v -8.3135 -9.328 0 +v 8.3135 -9.327 20 +v 1.5925 9.492 20 +v 4.4635 -9.492 20 +v 0.2015 3.84 20 +v 0.1415 3.6683 20 +v 0.0735 3.471 20 +v 0.0225 3.323 20 +v -1.6105 9.492 20 +v 2.5755 -4.174 20 +v -2.5945 -4.174 20 +v 0.2061 3.8557 20 +v -1.6105 9.492 0 +v 1.4955 -0.659 20 +v 1.5925 9.492 0 +v 0.4928 4.816 0 +v 0.5525 5.013 0 +v -4.4995 -9.492 20 +v 0.4063 4.5443 0 +v 0.4725 4.749 0 +v 0.6285 5.282 0 +v 0.6995 5.557 0 +v -1.5145 -0.657 20 +v 0.7645 5.817 0 +v 0.3955 4.511 0 +v -0.0165 5.045 0 +v -0.7975 5.816 0 +v 0.0688 3 0 +v -0.1655 3.655 20 +v -0.1115 3.493 20 +v -0.0785 3.3889 0 +v -0.0595 3.329 0 +v 0.5525 5.013 20 +v -0.0215 3.2091 0 +v 0.4928 4.816 20 +v -0.0785 3.0615 0 +v -0.5288 4.816 20 +v -0.1022 3 0 +v -0.5215 4.79 20 +v -0.4495 4.536 20 +v -0.3765 4.297 20 +v -0.0171 3.2204 0 +v -0.3045 4.069 20 +v 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91 88 +f 48 28 49 +f 35 77 91 +f 49 28 34 +f 34 28 35 +# 184 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/02.obj b/cliport/environments/assets/kitting/02.obj new file mode 100644 index 0000000000000000000000000000000000000000..05006a7962237f1dcc6acac5b2545f6a80753f4b --- /dev/null +++ b/cliport/environments/assets/kitting/02.obj @@ -0,0 +1,30 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 0 -10 0 +v -10 10 0 +v 10 10 0 +v -10 10 20 +v 0 -10 20 +v 10 10 20 +# 6 vertices + +g group_0_8273816 + +usemtl color_8273816 +s 0 + +f 1 2 3 +f 4 5 6 +f 5 1 3 +f 5 3 6 +f 3 2 4 +f 3 4 6 +f 2 1 5 +f 2 5 4 +# 8 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/03.obj b/cliport/environments/assets/kitting/03.obj new file mode 100644 index 0000000000000000000000000000000000000000..76e7b6c2f1867ebf909873b89741c84e4e3b1ef2 --- /dev/null +++ b/cliport/environments/assets/kitting/03.obj @@ -0,0 +1,36 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 10 10 20 +v 10 10 0 +v -10 10 0 +v -10 10 20 +v -10 -10 0 +v -10 -10 20 +v 10 -10 0 +v 10 -10 20 +# 8 vertices + +g group_0_16768282 + +usemtl color_16768282 +s 0 + +f 1 2 3 +f 1 3 4 +f 4 3 5 +f 4 5 6 +f 7 5 3 +f 7 3 2 +f 1 4 6 +f 1 6 8 +f 6 5 7 +f 6 7 8 +f 8 7 2 +f 8 2 1 +# 12 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/04.obj b/cliport/environments/assets/kitting/04.obj new file mode 100644 index 0000000000000000000000000000000000000000..32852bb7474c8cd6e92cc66a33a6b41497b5439f --- /dev/null +++ b/cliport/environments/assets/kitting/04.obj @@ -0,0 +1,84 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 10 -5 20 +v 10 -5 0 +v 10 5 0 +v 10 5 20 +v 5 -10 20 +v 5 -10 0 +v -5 -10 20 +v -5 -10 0 +v 5 -5 20 +v -5 -5 20 +v -10 5 20 +v -5 -5 0 +v -5 10 20 +v -5 10 0 +v 5 5 20 +v -5 5 0 +v 5 10 20 +v -10 5 0 +v -5 5 20 +v -10 -5 0 +v -10 -5 20 +v 5 -5 0 +v 5 10 0 +v 5 5 0 +# 24 vertices + +g group_0_15708628 + +usemtl color_15708628 +s 0 + +f 1 2 3 +f 1 3 4 +f 6 5 7 +f 6 7 8 +f 5 9 10 +f 5 10 7 +f 15 17 13 +f 15 13 19 +f 13 14 16 +f 13 16 19 +f 22 6 8 +f 22 8 12 +f 21 10 19 +f 11 21 19 +f 15 19 10 +f 10 9 15 +f 4 15 9 +f 4 9 1 +f 23 24 16 +f 23 16 14 +f 10 12 8 +f 10 8 7 +f 4 3 24 +f 4 24 15 +f 5 6 22 +f 5 22 9 +f 15 24 23 +f 15 23 17 +f 18 16 12 +f 20 18 12 +f 22 12 16 +f 16 24 22 +f 2 22 24 +f 2 24 3 +f 21 20 12 +f 21 12 10 +f 11 18 20 +f 11 20 21 +f 19 16 18 +f 19 18 11 +f 9 22 2 +f 9 2 1 +f 17 23 14 +f 17 14 13 +# 44 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/05.obj b/cliport/environments/assets/kitting/05.obj new file mode 100644 index 0000000000000000000000000000000000000000..308380e6176193081d70e9ed32eb50f697881fd0 --- /dev/null +++ b/cliport/environments/assets/kitting/05.obj @@ -0,0 +1,60 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 10 10 20 +v 10 10 0 +v 4 10 0 +v 4 10 20 +v -10 3 0 +v -10 3 20 +v 4 3 0 +v -10 -3 20 +v -10 -3 0 +v 4 -3 0 +v 4 -3 20 +v 4 3 20 +v 10 -10 0 +v 4 -10 0 +v 10 -10 20 +v 4 -10 20 +# 16 vertices + +g group_0_11593967 + +usemtl color_11593967 +s 0 + +f 1 2 3 +f 1 3 4 +f 2 7 3 +f 8 9 10 +f 8 10 11 +f 13 14 10 +f 15 1 12 +f 4 12 1 +f 15 13 2 +f 11 10 14 +f 11 14 16 +f 6 5 9 +f 6 9 8 +f 5 6 12 +f 5 12 7 +f 6 8 11 +f 15 12 11 +f 6 11 12 +f 15 2 1 +f 16 14 13 +f 16 13 15 +f 2 13 10 +f 9 5 10 +f 5 7 10 +f 2 10 7 +f 4 3 7 +f 4 7 12 +f 11 16 15 +# 28 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/06.obj b/cliport/environments/assets/kitting/06.obj new file mode 100644 index 0000000000000000000000000000000000000000..b881f82747299d5edc296887864c683a80e8fbd1 --- /dev/null +++ b/cliport/environments/assets/kitting/06.obj @@ -0,0 +1,36 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 0 -10 0 +v -12.5 0 0 +v 0 10 0 +v 12.5 0 0 +v -12.5 0 20 +v 0 -10 20 +v 12.5 0 20 +v 0 10 20 +# 8 vertices + +g group_0_14093196 + +usemtl color_14093196 +s 0 + +f 1 2 3 +f 1 3 4 +f 7 8 6 +f 5 6 8 +f 6 1 4 +f 6 4 7 +f 4 3 8 +f 4 8 7 +f 3 2 5 +f 3 5 8 +f 6 5 2 +f 6 2 1 +# 12 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/07.obj b/cliport/environments/assets/kitting/07.obj new file mode 100644 index 0000000000000000000000000000000000000000..2db64ff5104d1f0fbb4148058278a3c0cc539e00 --- /dev/null +++ b/cliport/environments/assets/kitting/07.obj @@ -0,0 +1,42 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 0 -9.06 0 +v -9.526 -2.139 0 +v -5.887 9.06 0 +v 5.887 9.06 0 +v 9.526 -2.139 0 +v -5.887 9.06 20.032 +v -9.526 -2.139 20.032 +v 0 -9.06 20.032 +v 9.526 -2.139 20.032 +v 5.887 9.06 20.032 +# 10 vertices + +g group_0_3888547 + +usemtl color_3888547 +s 0 + +f 1 2 3 +f 1 3 4 +f 1 4 5 +f 6 7 8 +f 9 10 8 +f 6 8 10 +f 8 1 5 +f 8 5 9 +f 4 10 9 +f 4 9 5 +f 4 3 6 +f 4 6 10 +f 3 2 7 +f 3 7 6 +f 8 7 2 +f 8 2 1 +# 16 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/08.obj b/cliport/environments/assets/kitting/08.obj new file mode 100644 index 0000000000000000000000000000000000000000..40d71c0a4d25ad15ad81eeb0ea9ed1e16644c446 --- /dev/null +++ b/cliport/environments/assets/kitting/08.obj @@ -0,0 +1,36 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 12.5 -7.5 20 +v 12.5 -7.5 0 +v 12.5 7.5 0 +v 12.5 7.5 20 +v -12.5 7.5 20 +v -12.5 7.5 0 +v -12.5 -7.5 0 +v -12.5 -7.5 20 +# 8 vertices + +g group_0_4634441 + +usemtl color_4634441 +s 0 + +f 1 2 3 +f 1 3 4 +f 5 6 7 +f 5 7 8 +f 4 5 8 +f 4 8 1 +f 2 7 6 +f 2 6 3 +f 4 3 6 +f 4 6 5 +f 8 7 2 +f 8 2 1 +# 12 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/09.obj b/cliport/environments/assets/kitting/09.obj new file mode 100644 index 0000000000000000000000000000000000000000..94042d1e73b2087af633067d948a19dc8d875484 --- /dev/null +++ b/cliport/environments/assets/kitting/09.obj @@ -0,0 +1,780 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v -5.975 4.904 0 +v -5.49 4.976 0 +v -5 5 0 +v 5.975 4.904 0 +v 3.865 -8.172 0 +v 4.157 -7.778 0 +v -10 0 0 +v 4.41 -7.357 0 +v -9.976 0.49 0 +v -9.904 0.975 0 +v -9.785 1.451 0 +v -9.619 1.913 0 +v -9.41 2.357 0 +v -9.157 2.778 0 +v -8.865 3.172 0 +v -8.536 3.536 0 +v -8.172 3.865 0 +v -7.778 4.157 0 +v 4.619 -6.913 0 +v -7.357 4.41 0 +v -6.913 4.619 0 +v -6.451 4.785 0 +v 4.785 -6.451 0 +v -5 -5 0 +v -5.49 -4.976 0 +v -5.975 -4.904 0 +v 3.536 -8.536 20 +v 3.172 -8.865 20 +v -6.451 -4.785 0 +v -6.913 -4.619 0 +v -7.357 -4.41 0 +v -7.778 -4.157 0 +v -8.172 -3.865 0 +v -8.536 -3.536 0 +v -8.865 -3.172 0 +v -9.157 -2.778 0 +v -9.41 -2.357 0 +v -9.619 -1.913 0 +v -9.785 -1.451 0 +v -9.904 -0.975 0 +v -9.976 -0.49 0 +v -5 -5 20 +v 9.976 0.49 0 +v 10 0 0 +v -5 5 20 +v -5.49 4.976 20 +v -5.975 4.904 20 +v 6.913 4.619 20 +v 6.451 4.785 20 +v 5.975 4.904 20 +v 9.904 0.975 0 +v 7.778 4.157 20 +v 7.357 4.41 20 +v 9.785 1.451 0 +v 9.619 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0 +v -9.59 -6.232 0 +v -6.314 0 0 +v -9.59 6.232 20 +v -2.651 5.042 20 +v 2.264 -10.084 0 +v -2.651 -5.042 0 +v -9.59 -6.232 20 +v -6.314 0 20 +v -2.651 -5.042 20 +v 9.59 0 0 +v 3.276 -3.116 0 +v 3.276 -3.116 20 +v 2.264 -10.084 20 +v 3.276 3.116 20 +v 2.264 10.084 20 +v 9.59 0 20 +# 20 vertices + +g group_0_15277357 + +usemtl color_15277357 +s 0 + +f 17 16 13 +f 18 12 16 +f 13 16 12 +f 8 12 18 +f 7 12 8 +f 11 13 12 +f 1 19 18 +f 1 18 2 +f 18 14 2 +f 1 8 19 +f 10 13 11 +f 10 11 5 +f 4 3 7 +f 4 7 8 +f 20 14 18 +f 6 5 11 +f 6 11 12 +f 18 16 20 +f 20 16 15 +f 20 15 14 +f 1 4 8 +f 15 16 17 +f 15 17 9 +f 9 10 15 +f 10 6 15 +f 5 6 10 +f 6 2 15 +f 3 4 6 +f 4 2 6 +f 1 2 4 +f 14 15 2 +f 18 19 8 +f 12 7 6 +f 3 6 7 +f 10 9 17 +f 10 17 13 +# 36 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/11.obj b/cliport/environments/assets/kitting/11.obj new file mode 100644 index 0000000000000000000000000000000000000000..52c2ed97a9485bd62e2f150d47cf7b23b4882b7a --- /dev/null +++ b/cliport/environments/assets/kitting/11.obj @@ -0,0 +1,396 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v -10 0 0 +v -9.952 -0.98 0 +v -9.808 -1.951 0 +v -9.569 -2.903 0 +v -9.239 -3.827 0 +v -8.819 -4.714 0 +v -8.315 -5.556 0 +v -7.73 -6.344 0 +v -7.071 -7.071 0 +v -6.344 -7.73 0 +v -5.556 -8.315 0 +v -4.714 -8.819 0 +v -3.827 -9.239 0 +v -2.903 -9.569 0 +v -1.951 -9.808 0 +v -0.98 -9.952 0 +v 0 -10 0 +v 0 -10 20 +v -0.98 -9.952 20 +v 5.556 8.315 0 +v 4.714 8.819 0 +v 3.827 9.239 0 +v 2.903 9.569 0 +v 1.951 9.808 0 +v 0.98 9.952 0 +v 0 10 0 +v 10 0 0 +v 9.952 0.98 0 +v 9.808 1.951 0 +v 9.569 2.903 0 +v 8.819 4.714 20 +v 9.239 3.827 20 +v 8.315 5.556 20 +v 7.73 6.344 20 +v 7.071 7.071 20 +v 6.344 7.73 20 +v 0 10 20 +v -0.98 9.952 20 +v -1.951 9.808 20 +v -2.903 9.569 20 +v -3.827 9.239 20 +v -4.714 8.819 20 +v -5.556 8.315 20 +v -6.344 7.73 20 +v -7.071 7.071 20 +v -7.73 6.344 20 +v -8.315 5.556 20 +v -8.819 4.714 20 +v -9.239 3.827 20 +v -9.569 2.903 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20 +v 9.808 -1.951 20 +v 1.951 -9.808 20 +v 2.903 -9.569 20 +v 3.827 -9.239 20 +v 4.714 -8.819 20 +v 5.556 -8.315 20 +v 0.98 -9.952 20 +v -9.952 -0.98 20 +v -9.808 -1.951 20 +v -9.569 -2.903 20 +v -9.239 -3.827 20 +v -8.819 -4.714 20 +v -8.315 -5.556 20 +v -7.73 -6.344 20 +v -7.071 -7.071 20 +v -6.344 -7.73 20 +v -5.556 -8.315 20 +v -4.714 -8.819 20 +v -3.827 -9.239 20 +v -2.903 -9.569 20 +v -1.951 -9.808 20 +# 128 vertices + +g group_0_16089887 + +usemtl color_16089887 +s 0 + +f 17 18 19 +f 125 11 12 +f 12 13 125 +f 19 15 16 +f 16 17 19 +f 77 33 31 +f 76 33 77 +f 100 23 24 +f 24 25 100 +f 37 25 26 +f 30 104 29 +f 31 32 79 +f 76 34 33 +f 35 34 76 +f 2 115 53 +f 2 53 1 +f 2 3 115 +f 3 4 117 +f 117 4 118 +f 5 118 4 +f 119 118 6 +f 5 6 118 +f 6 7 119 +f 7 8 121 +f 121 8 122 +f 9 122 8 +f 10 122 9 +f 23 1 24 +f 25 24 1 +f 22 1 23 +f 21 1 22 +f 20 1 21 +f 74 1 20 +f 75 1 74 +f 61 60 78 +f 77 78 17 +f 79 61 78 +f 30 61 79 +f 29 61 30 +f 28 61 29 +f 27 61 28 +f 83 1 82 +f 81 82 1 +f 84 1 83 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Server 2015 + +mtllib obj.mtl + +o obj_0 +v -1.7415 -9.457 20 +v 1.7055 -9.457 20 +v -0.7435 -5.451 0 +v -0.0415 -5.011 0 +v 0.6735 -5.441 0 +v -0.4125 -4.157 20 +v -0.2245 -3.463 20 +v -3.8955 9.457 20 +v 1.7055 -9.457 0 +v -0.5885 -4.812 20 +v -7.8265 9.308 20 +v -1.7415 -9.457 0 +v 0.5225 -4.822 0 +v -0.7435 -5.451 20 +v 0.4876 -4.6794 0 +v -0.0256 -2.742 20 +v 3.8615 9.457 0 +v 7.8265 9.307 0 +v -0.2395 -2.06 20 +v -7.8265 9.308 0 +v -3.8955 9.457 0 +v 0.3635 -4.172 0 +v 0.6735 -5.441 20 +v -0.0415 -5.011 20 +v -0.0206 -2.7581 0 +v -0.0256 -2.742 0 +v 0.5225 -4.822 20 +v 0.2215 -1.975 0 +v 0.4876 -4.6794 20 +v 0.1865 -3.48 0 +v -0.0125 -2.732 0 +v -0.2395 -2.06 0 +v -0.0165 -2.771 0 +v -0.0203 -2.7589 0 +v -0.0154 -2.742 0 +v -0.0201 -2.7583 0 +v 0.2215 -1.975 20 +v 0.3635 -4.172 20 +v -0.2245 -3.463 0 +v 0.1865 -3.48 20 +v -0.4125 -4.157 0 +v -0.0201 -2.7583 20 +v -0.0165 -2.771 20 +v -0.0125 -2.732 20 +v -0.5885 -4.812 0 +v -0.0154 -2.742 20 +v -0.0203 -2.7589 20 +v 3.8615 9.457 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24 6 +f 14 24 10 +f 29 6 24 +f 44 31 46 +f 35 46 31 +f 17 48 18 +f 49 18 48 +f 37 48 28 +f 9 49 2 +f 16 46 26 +f 16 26 19 +f 44 37 28 +f 31 44 28 +f 9 18 49 +f 12 1 20 +f 1 11 20 +f 26 32 19 +f 8 19 21 +f 32 21 19 +# 96 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/14.obj b/cliport/environments/assets/kitting/14.obj new file mode 100644 index 0000000000000000000000000000000000000000..0e7ea7565c73c723d9c51e3742b51ad89c334147 --- /dev/null +++ b/cliport/environments/assets/kitting/14.obj @@ -0,0 +1,84 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v -5.326 9.457 20 +v -5.326 9.457 0 +v -5.326 -9.457 0 +v -5.326 -9.457 20 +v 5.326 -9.457 0 +v 5.326 -9.457 20 +v -1.412 -5.802 20 +v -1.412 -1.455 20 +v -1.412 2.217 20 +v -1.412 5.785 20 +v 5.326 -5.802 20 +v 4.876 -1.455 0 +v 4.876 -1.455 20 +v -1.412 -1.455 0 +v 5.326 5.785 0 +v 5.326 5.785 20 +v 5.326 -5.802 0 +v -1.412 -5.802 0 +v 5.326 9.457 0 +v 5.326 9.457 20 +v 4.876 2.217 0 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20 +v 10 -10 0 +v 10 -4 0 +v 10 -4 20 +v -4 -4 20 +v -4 -4 0 +v -4 10 0 +v -4 10 20 +v -10 10 20 +v -10 10 0 +v -10 -10 0 +v -10 -10 20 +# 12 vertices + +g group_0_30377 + +usemtl color_30377 +s 0 + +f 1 2 3 +f 1 3 4 +f 4 5 1 +f 1 11 2 +f 2 11 6 +f 2 6 3 +f 7 8 5 +f 7 5 6 +f 9 10 11 +f 9 11 12 +f 12 11 1 +f 8 7 10 +f 8 10 9 +f 12 5 9 +f 1 5 12 +f 8 9 5 +f 7 6 10 +f 11 10 6 +f 4 3 6 +f 4 6 5 +# 20 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/16.obj b/cliport/environments/assets/kitting/16.obj new file mode 100644 index 0000000000000000000000000000000000000000..43e1b249c79f1a33c4d1c649c471c2b0717f2b5a --- /dev/null +++ b/cliport/environments/assets/kitting/16.obj @@ -0,0 +1,784 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 0 10 0 +v 0.98 9.952 0 +v 1.951 9.808 0 +v 2.903 9.569 0 +v 3.827 9.239 0 +v 4.714 8.819 0 +v 5.556 8.315 0 +v 4.047 1.228 20 +v 4.148 0.825 20 +v 4.209 0.415 20 +v 8.315 5.556 0 +v 8.819 4.714 0 +v 9.239 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94 +f 201 157 200 +f 149 200 157 +f 107 105 38 +f 201 159 157 +f 39 38 104 +f 105 104 38 +f 205 161 159 +f 104 34 39 +f 162 161 207 +f 205 207 161 +f 162 207 164 +f 165 164 211 +f 211 213 165 +f 167 165 213 +f 169 167 213 +f 169 213 214 +f 214 216 169 +f 170 169 216 +f 172 170 216 +f 172 216 237 +f 173 172 237 +f 173 237 238 +f 238 239 173 +f 175 173 239 +f 239 55 175 +f 240 180 189 +f 141 189 180 +f 240 241 180 +f 182 180 241 +f 32 250 242 +f 187 242 243 +f 243 188 187 +# 512 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/17.obj b/cliport/environments/assets/kitting/17.obj new file mode 100644 index 0000000000000000000000000000000000000000..785151888e9037dba5d66e400cd50a4ad7d2919f --- /dev/null +++ b/cliport/environments/assets/kitting/17.obj @@ -0,0 +1,48 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v -8.66 5 20 +v -8.66 5 0 +v -8.66 -5 0 +v -8.66 -5 20 +v 0 10 0 +v 8.66 5 0 +v 8.66 -5 0 +v 0 -10 0 +v 8.66 5 20 +v 0 10 20 +v 0 -10 20 +v 8.66 -5 20 +# 12 vertices + +g group_0_16500122 + +usemtl color_16500122 +s 0 + +f 1 2 3 +f 1 3 4 +f 2 5 6 +f 2 6 7 +f 8 3 7 +f 2 7 3 +f 9 10 1 +f 9 1 4 +f 11 12 4 +f 9 4 12 +f 6 5 10 +f 6 10 9 +f 11 4 3 +f 11 3 8 +f 5 2 1 +f 5 1 10 +f 11 8 7 +f 11 7 12 +f 6 9 12 +f 6 12 7 +# 20 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/18.obj b/cliport/environments/assets/kitting/18.obj new file mode 100644 index 0000000000000000000000000000000000000000..28286d5a63a976893f2da4302e57abece375de45 --- /dev/null +++ b/cliport/environments/assets/kitting/18.obj @@ -0,0 +1,192 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 0 -8.796 0 +v -3.764 -5.848 0 +v 0 6.089 0 +v -0.311 6.741 0 +v -6.235 8.505 0 +v -7.76 7.739 0 +v -8.746 6.655 0 +v -9.303 5.413 0 +v -9.578 3.087 0 +v -9.472 2.03 0 +v -9.287 0.799 0 +v -8.754 -0.461 0 +v -6.766 -3.079 0 +v 0.311 6.741 0 +v 1.167 7.646 0 +v 2.455 8.449 0 +v 4.06 8.796 0 +v 6.235 8.505 0 +v 7.76 7.739 0 +v 8.746 6.655 0 +v 9.303 5.413 0 +v 9.578 3.087 0 +v 9.472 2.03 0 +v 9.287 0.799 0 +v 8.754 -0.461 0 +v 6.766 -3.079 0 +v 3.764 -5.848 0 +v -4.06 8.796 0 +v -2.455 8.449 0 +v -1.167 7.646 0 +v 4.06 8.796 20 +v 2.455 8.449 20 +v 1.167 7.646 20 +v 0.311 6.741 20 +v 0 6.089 20 +v 0 -8.796 20 +v 3.764 -5.848 20 +v 6.766 -3.079 20 +v 8.754 -0.461 20 +v 9.287 0.799 20 +v 9.472 2.03 20 +v 9.578 3.087 20 +v 9.303 5.413 20 +v 8.746 6.655 20 +v 7.76 7.739 20 +v 6.235 8.505 20 +v -4.06 8.796 20 +v -6.235 8.505 20 +v -7.76 7.739 20 +v -8.746 6.655 20 +v -9.303 5.413 20 +v -9.578 3.087 20 +v -9.472 2.03 20 +v -9.287 0.799 20 +v -8.754 -0.461 20 +v -6.766 -3.079 20 +v -3.764 -5.848 20 +v -0.311 6.741 20 +v -1.167 7.646 20 +v -2.455 8.449 20 +# 60 vertices + +g group_0_11452141 + +usemtl color_11452141 +s 0 + +f 6 7 50 +f 7 8 50 +f 9 10 53 +f 10 11 53 +f 11 12 55 +f 12 13 55 +f 3 14 15 +f 3 15 16 +f 3 16 17 +f 3 17 18 +f 3 18 19 +f 3 19 20 +f 3 20 21 +f 3 21 22 +f 3 22 23 +f 3 23 24 +f 3 24 25 +f 3 25 26 +f 3 26 27 +f 1 2 27 +f 11 10 12 +f 13 12 10 +f 10 9 13 +f 9 8 13 +f 8 7 13 +f 7 6 13 +f 6 5 13 +f 5 28 13 +f 28 29 13 +f 29 30 13 +f 30 4 13 +f 4 3 13 +f 2 13 3 +f 3 27 2 +f 39 40 35 +f 38 39 35 +f 37 38 35 +f 35 57 37 +f 40 41 35 +f 41 42 35 +f 34 35 42 +f 33 34 42 +f 32 33 42 +f 31 32 42 +f 43 46 42 +f 44 46 43 +f 45 46 44 +f 31 42 46 +f 49 50 48 +f 50 51 48 +f 51 52 48 +f 47 48 52 +f 53 35 52 +f 54 35 53 +f 55 35 54 +f 56 35 55 +f 57 35 56 +f 36 37 57 +f 35 58 52 +f 58 59 52 +f 59 60 52 +f 47 52 60 +f 1 37 36 +f 25 39 38 +f 40 39 25 +f 34 3 35 +f 59 58 4 +f 52 51 9 +f 8 9 51 +f 47 5 48 +f 52 9 53 +f 5 6 49 +f 5 49 48 +f 53 11 54 +f 6 50 49 +f 11 55 54 +f 50 8 51 +f 55 13 56 +f 2 57 56 +f 2 56 13 +f 57 2 36 +f 1 36 2 +f 3 4 35 +f 58 35 4 +f 1 27 37 +f 38 37 26 +f 27 26 37 +f 26 25 38 +f 40 25 24 +f 21 43 42 +f 24 23 41 +f 24 41 40 +f 44 43 21 +f 42 41 22 +f 23 22 41 +f 22 21 42 +f 45 18 46 +f 44 21 20 +f 18 31 46 +f 20 19 45 +f 20 45 44 +f 19 18 45 +f 31 18 17 +f 17 16 32 +f 17 32 31 +f 33 32 15 +f 16 15 32 +f 34 33 14 +f 15 14 33 +f 14 3 34 +f 4 30 59 +f 30 29 60 +f 30 60 59 +f 47 60 28 +f 29 28 60 +f 28 5 47 +# 116 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/19.obj b/cliport/environments/assets/kitting/19.obj new file mode 100644 index 0000000000000000000000000000000000000000..383293435c4e2bd59862b5a70169a9dacc86fd71 --- /dev/null +++ b/cliport/environments/assets/kitting/19.obj @@ -0,0 +1,333 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v -4.113 9.457 0 +v -5.084 0.7238 0 +v -5.084 0.7238 16.384 +v -5.084 0.758 0 +v -5.0955 0.758 0 +v 5.066 -9.457 0 +v 1.368 -9.457 0 +v -0.042 -2.742 0 +v 0.0227 -2.742 0 +v -5.084 -9.457 0 +v -1.671 -9.457 0 +v -8.998 9.457 20 +v -8.998 -9.457 20 +v -8.998 9.457 0 +v -8.998 -9.457 0 +v 8.998 -9.457 20 +v 5.066 -9.457 20 +v -5.084 -9.457 20 +v 1.368 -9.457 20 +v -1.671 -9.457 20 +v 8.998 -9.457 0 +v 5.066 0.758 0 +v 4.9378 0.758 0 +v 4.9611 0.8246 0 +v 5.0227 1.0009 0 +v 5.0396 1.0493 0 +v 5.042 1.0559 0 +v 5.0595 1.1062 0 +v 5.066 1.1247 0 +v 6.616 5.56 0 +v 8.998 9.457 0 +v 5.221 5.579 0 +v 5.215 5.893 0 +v 5.1993 5.5793 0 +v 4.26 9.457 0 +v 5.187 5.334 0 +v 5.154 4.773 0 +v 5.132 4.279 0 +v 5.1277 4.1504 0 +v 5.215 5.893 20 +v 5.221 5.579 20 +v 6.616 5.56 20 +v 4.26 9.457 20 +v 5.1993 5.5793 20 +v 5.154 4.773 20 +v 5.187 5.334 20 +v 4.9611 0.8246 20 +v 5.132 4.279 20 +v 4.9378 0.758 20 +v 5.1277 4.1504 20 +v 5.0227 1.0009 20 +v 5.115 3.769 20 +v 5.082 2.747 20 +v 5.101 3.249 20 +v 5.0396 1.0493 20 +v 5.07 2.266 20 +v 5.042 1.0559 20 +v 5.0677 2.0126 20 +v 5.0595 1.1062 20 +v 5.066 1.823 20 +v 5.066 1.1247 20 +v -5.084 1.684 20 +v -5.084 0.758 20 +v -5.084 0.7238 20 +v -4.113 9.457 20 +v -5.088 2.15 20 +v -5.1 2.653 20 +v -5.119 3.187 20 +v -5.142 3.733 20 +v -5.189 4.759 20 +v -5.166 4.254 20 +v 5.115 3.769 0 +v 5.101 3.249 0 +v 5.082 2.747 0 +v 5.07 2.266 0 +v 5.0677 2.0126 0 +v 5.066 1.823 0 +v -5.084 1.684 0 +v -5.088 2.15 0 +v -5.1 2.653 0 +v -5.119 3.187 0 +v -5.142 3.733 0 +v -5.166 4.254 0 +v -5.189 4.759 0 +v -5.19 4.7745 0 +v -5.226 5.346 0 +v -5.267 5.929 0 +v -5.276 5.611 0 +v -6.711 5.577 0 +v -0.0103 -2.8371 0 +v -0.607 -4.555 0 +v 0.563 -4.555 0 +v 8.998 9.457 20 +v 5.066 0.758 20 +v -5.19 4.7745 20 +v -5.226 5.346 20 +v -5.267 5.929 20 +v -6.711 5.577 20 +v -5.276 5.611 20 +v -5.0955 0.758 20 +v 0.0227 -2.742 20 +v -0.042 -2.742 20 +v -0.0103 -2.8371 20 +v -0.607 -4.555 20 +v 0.563 -4.555 20 +# 105 vertices + +g group_0_2829873 + +usemtl color_2829873 +s 0 + +f 2 3 10 +f 3 63 4 +f 3 4 2 +f 5 3 2 +f 4 63 5 +f 2 11 3 +f 13 12 14 +f 13 14 15 +f 16 17 21 +f 10 18 13 +f 20 11 19 +f 7 19 11 +f 21 17 6 +f 10 13 15 +f 22 23 24 +f 22 24 25 +f 22 25 26 +f 22 26 27 +f 22 27 28 +f 22 28 29 +f 30 21 29 +f 31 21 30 +f 22 29 21 +f 30 32 33 +f 33 31 30 +f 34 9 33 +f 31 33 35 +f 34 33 32 +f 36 9 34 +f 37 9 36 +f 38 9 37 +f 39 9 38 +f 73 9 72 +f 73 74 9 +f 41 42 40 +f 42 93 40 +f 43 101 40 +f 40 44 41 +f 44 40 101 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8 +f 64 3 20 +f 11 20 3 +f 10 3 18 +f 101 43 9 +f 49 23 22 +f 6 17 22 +# 212 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/kitting/kit.urdf b/cliport/environments/assets/kitting/kit.urdf new file mode 100644 index 0000000000000000000000000000000000000000..eef707c08a583a1b025b39d68826e06e2aeb57dd --- /dev/null +++ b/cliport/environments/assets/kitting/kit.urdf @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/kitting/object-template-nocollision.urdf b/cliport/environments/assets/kitting/object-template-nocollision.urdf new file mode 100644 index 0000000000000000000000000000000000000000..f8561945c5abfcea2aa6b8007572fd5ce9df1828 --- /dev/null +++ b/cliport/environments/assets/kitting/object-template-nocollision.urdf @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/kitting/object-template.urdf b/cliport/environments/assets/kitting/object-template.urdf new file mode 100644 index 0000000000000000000000000000000000000000..925467fd542091b0a9c7719dcf31464e63cfaa13 --- /dev/null +++ b/cliport/environments/assets/kitting/object-template.urdf @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/kitting/object-template_FNAME_COLOR_SCALE.urdf b/cliport/environments/assets/kitting/object-template_FNAME_COLOR_SCALE.urdf new file mode 100644 index 0000000000000000000000000000000000000000..925467fd542091b0a9c7719dcf31464e63cfaa13 --- /dev/null +++ b/cliport/environments/assets/kitting/object-template_FNAME_COLOR_SCALE.urdf @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/line/line-template.urdf b/cliport/environments/assets/line/line-template.urdf new file mode 100644 index 0000000000000000000000000000000000000000..c028fd9286d872de99102615f6d312064e9e8ca6 --- /dev/null +++ b/cliport/environments/assets/line/line-template.urdf @@ -0,0 +1,23 @@ + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/line/line-template_DIM.urdf b/cliport/environments/assets/line/line-template_DIM.urdf new file mode 100644 index 0000000000000000000000000000000000000000..c028fd9286d872de99102615f6d312064e9e8ca6 --- /dev/null +++ b/cliport/environments/assets/line/line-template_DIM.urdf @@ -0,0 +1,23 @@ + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/line/line.obj b/cliport/environments/assets/line/line.obj new file mode 100644 index 0000000000000000000000000000000000000000..4766761c32178ad89a74799cb7466dfee4f3421d --- /dev/null +++ b/cliport/environments/assets/line/line.obj @@ -0,0 +1,64 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 10 -10 20 +v 10 -10 0 +v 10 10 0 +v 10 10 20 +v 9.002 9.003 20 +v 9.002 -9.002 20 +v -10 10 0 +v -10 10 20 +v -9.003 9.003 20 +v -9.003 9.003 0 +v 9.002 9.003 0 +v 9.002 -9.002 0 +v -9.003 -9.002 0 +v -9.003 -9.002 20 +v -10 -10 0 +v -10 -10 20 +# 16 vertices + +g group_0_15277357 + +usemtl color_15277357 +s 0 + +f 1 2 3 +f 1 3 4 +f 4 5 6 +f 4 6 1 +f 9 10 11 +f 9 11 5 +f 6 12 13 +f 6 13 14 +f 10 9 14 +f 10 14 13 +f 7 10 13 +f 7 13 15 +f 4 8 5 +f 9 5 8 +f 8 7 15 +f 8 15 16 +f 10 7 11 +f 3 11 7 +f 11 3 12 +f 2 12 3 +f 14 16 6 +f 1 6 16 +f 16 15 2 +f 16 2 1 +f 9 8 14 +f 16 14 8 +f 7 8 3 +f 4 3 8 +f 2 15 12 +f 13 12 15 +f 12 6 5 +f 12 5 11 +# 32 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/line/single-green-line-template.urdf b/cliport/environments/assets/line/single-green-line-template.urdf new file mode 100644 index 0000000000000000000000000000000000000000..c028fd9286d872de99102615f6d312064e9e8ca6 --- /dev/null +++ b/cliport/environments/assets/line/single-green-line-template.urdf @@ -0,0 +1,23 @@ + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/pallet/pallet.obj b/cliport/environments/assets/pallet/pallet.obj new file mode 100644 index 0000000000000000000000000000000000000000..2b4a0fbb27144de8d958e94f17b3eb0581ab27d4 --- /dev/null +++ b/cliport/environments/assets/pallet/pallet.obj @@ -0,0 +1,656 @@ +#### +# +# OBJ File Generated by Meshlab +# +#### +# Object pallet.obj +# +# Vertices: 184 +# Faces: 452 +# +#### +mtllib ./pallet.obj.mtl + +v 0.220000 0.500000 0.075000 0.749020 0.749020 0.749020 +v 0.220000 0.500000 0.055000 0.749020 0.749020 0.749020 +v 0.120000 0.500000 0.055000 0.749020 0.749020 0.749020 +v 0.120000 0.500000 0.075000 0.749020 0.749020 0.749020 +v 0.390000 0.500000 0.075000 0.749020 0.749020 0.749020 +v 0.390000 0.500000 0.055000 0.749020 0.749020 0.749020 +v 0.290000 0.500000 0.055000 0.749020 0.749020 0.749020 +v 0.290000 -0.500000 0.075000 0.749020 0.749020 0.749020 +v 0.290000 0.500000 0.075000 0.749020 0.749020 0.749020 +v -0.050000 0.020000 0.055000 0.749020 0.749020 0.749020 +v -0.120000 0.020000 0.055000 0.749020 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a/cliport/environments/assets/plane/plane.mtl b/cliport/environments/assets/plane/plane.mtl new file mode 100644 index 0000000000000000000000000000000000000000..cd101528d364fc8f6553c4312cd0f90757f2b6bb --- /dev/null +++ b/cliport/environments/assets/plane/plane.mtl @@ -0,0 +1,15 @@ +newmtl Material + Ns 10.0000 + Ni 1.5000 + d 1.0000 + Tr 0.0000 + Tf 1.0000 1.0000 1.0000 + illum 2 + Ka 0.0000 0.0000 0.0000 + Kd 0.5880 0.5880 0.5880 + Ks 0.0000 0.0000 0.0000 + Ke 0.0000 0.0000 0.0000 + map_Ka cube.tga + map_Kd checker_blue.png + + diff --git a/cliport/environments/assets/plane/plane.urdf b/cliport/environments/assets/plane/plane.urdf new file mode 100644 index 0000000000000000000000000000000000000000..102c6b323a7709acc2c9cf4907801f0c2379f032 --- /dev/null +++ b/cliport/environments/assets/plane/plane.urdf @@ -0,0 +1,20 @@ + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/plane/plane100.obj b/cliport/environments/assets/plane/plane100.obj new file mode 100644 index 0000000000000000000000000000000000000000..3a74f590b5548ed1531e80f90e24e56d2f9cc33a --- /dev/null +++ b/cliport/environments/assets/plane/plane100.obj @@ -0,0 +1,22 @@ +# Blender v2.66 (sub 1) OBJ File: '' +# www.blender.org +mtllib plane.mtl +o Plane +v 100.000000 -100.000000 0.000000 +v 100.000000 100.000000 0.000000 +v -100.000000 100.000000 0.000000 +v -100.000000 -100.000000 0.000000 + +vt 100.000000 0.000000 +vt 100.000000 100.000000 +vt 0.000000 100.000000 +vt 0.000000 0.000000 + + + +usemtl Material +s off +f 1/1 2/2 3/3 +f 1/1 3/3 4/4 + + diff --git a/cliport/environments/assets/rectangle/rectangle-template-sides-1.urdf b/cliport/environments/assets/rectangle/rectangle-template-sides-1.urdf new file mode 100644 index 0000000000000000000000000000000000000000..8861f731446bb4a02f7d29329571ea142a80b5ed --- /dev/null +++ b/cliport/environments/assets/rectangle/rectangle-template-sides-1.urdf @@ -0,0 +1,63 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/rectangle/rectangle-template-sides-1_test.urdf b/cliport/environments/assets/rectangle/rectangle-template-sides-1_test.urdf new file mode 100644 index 0000000000000000000000000000000000000000..dc82160e60dd3bf2bd2fbfe76108a80fee531176 --- /dev/null +++ b/cliport/environments/assets/rectangle/rectangle-template-sides-1_test.urdf @@ -0,0 +1,63 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/rectangle/rectangle-template-sides-2.urdf b/cliport/environments/assets/rectangle/rectangle-template-sides-2.urdf new file mode 100644 index 0000000000000000000000000000000000000000..65f11c8b25e1995f6f2b08c8582b0e9b789c1cd8 --- /dev/null +++ b/cliport/environments/assets/rectangle/rectangle-template-sides-2.urdf @@ -0,0 +1,63 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/rectangle/rectangle-template-sides-3.urdf b/cliport/environments/assets/rectangle/rectangle-template-sides-3.urdf new file mode 100644 index 0000000000000000000000000000000000000000..98483b28380dcc14a71b79720d533eef0204464a --- /dev/null +++ b/cliport/environments/assets/rectangle/rectangle-template-sides-3.urdf @@ -0,0 +1,60 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/rectangle/rectangle-template-sides-4.urdf b/cliport/environments/assets/rectangle/rectangle-template-sides-4.urdf new file mode 100644 index 0000000000000000000000000000000000000000..d66b36ef4dfa0fb7be7ed25f7f53d04ed4e68864 --- /dev/null +++ b/cliport/environments/assets/rectangle/rectangle-template-sides-4.urdf @@ -0,0 +1,57 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/sphere/sphere-template.urdf b/cliport/environments/assets/sphere/sphere-template.urdf new file mode 100644 index 0000000000000000000000000000000000000000..5a9836da647f8bc8ef24b6da537a820b9970c1dc --- /dev/null +++ b/cliport/environments/assets/sphere/sphere-template.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/sphere/sphere-template_DIM.urdf b/cliport/environments/assets/sphere/sphere-template_DIM.urdf new file mode 100644 index 0000000000000000000000000000000000000000..5a9836da647f8bc8ef24b6da537a820b9970c1dc --- /dev/null +++ b/cliport/environments/assets/sphere/sphere-template_DIM.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/sphere/sphere.urdf b/cliport/environments/assets/sphere/sphere.urdf new file mode 100644 index 0000000000000000000000000000000000000000..45f0b490e32574b605b769491cd5e63b17073383 --- /dev/null +++ b/cliport/environments/assets/sphere/sphere.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/sphere/sphere_rope.urdf b/cliport/environments/assets/sphere/sphere_rope.urdf new file mode 100644 index 0000000000000000000000000000000000000000..4ab3bb2a438345ea5e85801142815e8140a95463 --- /dev/null +++ b/cliport/environments/assets/sphere/sphere_rope.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/square/square-template-allsides-COLOR.urdf b/cliport/environments/assets/square/square-template-allsides-COLOR.urdf new file mode 100644 index 0000000000000000000000000000000000000000..bfdb13c0d48b5a2bb9bfc0ccf66c085fcd69522d --- /dev/null +++ b/cliport/environments/assets/square/square-template-allsides-COLOR.urdf @@ -0,0 +1,57 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/square/square-template-allsides-blue.urdf b/cliport/environments/assets/square/square-template-allsides-blue.urdf new file mode 100644 index 0000000000000000000000000000000000000000..0cc912875fd3f4e663dd04ef1d477bc95d793799 --- /dev/null +++ b/cliport/environments/assets/square/square-template-allsides-blue.urdf @@ -0,0 +1,57 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/square/square-template-allsides-green.urdf b/cliport/environments/assets/square/square-template-allsides-green.urdf new file mode 100644 index 0000000000000000000000000000000000000000..e3641a0965500164a92ea2121fc017448b64f4b9 --- /dev/null +++ b/cliport/environments/assets/square/square-template-allsides-green.urdf @@ -0,0 +1,57 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/square/square-template.urdf b/cliport/environments/assets/square/square-template.urdf new file mode 100644 index 0000000000000000000000000000000000000000..e51e1973158d91ce5fa14c25d324b6b783af2cf7 --- /dev/null +++ b/cliport/environments/assets/square/square-template.urdf @@ -0,0 +1,45 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/square/square-template_HALF_DIM.urdf b/cliport/environments/assets/square/square-template_HALF_DIM.urdf new file mode 100644 index 0000000000000000000000000000000000000000..e51e1973158d91ce5fa14c25d324b6b783af2cf7 --- /dev/null +++ b/cliport/environments/assets/square/square-template_HALF_DIM.urdf @@ -0,0 +1,45 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/stacking/block.urdf b/cliport/environments/assets/stacking/block.urdf new file mode 100644 index 0000000000000000000000000000000000000000..938423cb8040b9b45022a434e1a001fe995c779f --- /dev/null +++ b/cliport/environments/assets/stacking/block.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/stacking/stand.urdf b/cliport/environments/assets/stacking/stand.urdf new file mode 100644 index 0000000000000000000000000000000000000000..16c5d70fa2e05b59943d221a1768f5d8eede1837 --- /dev/null +++ b/cliport/environments/assets/stacking/stand.urdf @@ -0,0 +1,89 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/ur5/collision/base.stl b/cliport/environments/assets/ur5/collision/base.stl new file mode 100644 index 0000000000000000000000000000000000000000..339a8164007954c38541f204af1354c8b991828e --- /dev/null +++ b/cliport/environments/assets/ur5/collision/base.stl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7af57d109de6a73f57943613bb000346518df06bb5ae119a2e80cf00bcbebde +size 28984 diff --git a/cliport/environments/assets/ur5/collision/forearm.stl b/cliport/environments/assets/ur5/collision/forearm.stl new file mode 100644 index 0000000000000000000000000000000000000000..5ab694eba4c13bad33e513a31f4fa594aed1e8f6 --- /dev/null +++ b/cliport/environments/assets/ur5/collision/forearm.stl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8e79aa80a532494ba5320c30b40363ff63d228e3d59e2904703dca341dcba2ac +size 52584 diff --git a/cliport/environments/assets/ur5/collision/shoulder.stl b/cliport/environments/assets/ur5/collision/shoulder.stl new file mode 100644 index 0000000000000000000000000000000000000000..d10054cd74b1e1e00509dcbdcb11599fdbd7decb --- /dev/null +++ b/cliport/environments/assets/ur5/collision/shoulder.stl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1d62e23a8c3ff9e6334fb157c805e6202037bcfca65bc6a36c5c9ae866751b3b +size 33784 diff --git a/cliport/environments/assets/ur5/collision/upperarm.stl b/cliport/environments/assets/ur5/collision/upperarm.stl new file mode 100644 index 0000000000000000000000000000000000000000..a445b087e1dadad4ea0f3cd289cf25c8383281e3 --- /dev/null +++ b/cliport/environments/assets/ur5/collision/upperarm.stl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:55c11ac0ad1e144f230849b2033532e8b140b2e818ea497296d08dfb88757440 +size 58884 diff --git a/cliport/environments/assets/ur5/collision/wrist1.stl b/cliport/environments/assets/ur5/collision/wrist1.stl new file mode 100644 index 0000000000000000000000000000000000000000..9b73783ed4086f61809fd5883380296e1b5344a7 --- /dev/null +++ b/cliport/environments/assets/ur5/collision/wrist1.stl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0a1fd12150140efdee91650dcb4c2d0443065551a9207a2d2dbde58ff1fb4eb7 +size 35184 diff --git a/cliport/environments/assets/ur5/collision/wrist2.stl b/cliport/environments/assets/ur5/collision/wrist2.stl new file mode 100644 index 0000000000000000000000000000000000000000..fa66a3e3e4e719832c9fadc9b240f59a9ff77b31 --- /dev/null +++ b/cliport/environments/assets/ur5/collision/wrist2.stl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1d9f7843366068cb331f3f5c43fa716d3eceeb8c80216eccbbc878905dab123 +size 35184 diff --git a/cliport/environments/assets/ur5/collision/wrist3.stl b/cliport/environments/assets/ur5/collision/wrist3.stl new file mode 100644 index 0000000000000000000000000000000000000000..c7bed14cb60de2bc786c7871733ae16e8ae2ee65 --- /dev/null +++ b/cliport/environments/assets/ur5/collision/wrist3.stl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf7220397b56f1d3047be9465eea1181d471b40fc82728052e31c97588585b4b +size 22384 diff --git a/cliport/environments/assets/ur5/gripper/README.md b/cliport/environments/assets/ur5/gripper/README.md new file mode 100644 index 0000000000000000000000000000000000000000..aa41fefe70e51225486449b011c2a954972f3b2c --- /dev/null +++ b/cliport/environments/assets/ur5/gripper/README.md @@ -0,0 +1,52 @@ +## Robotiq 2F 85 gripper +For this gripper, the following Github repo can be used as a reference: https://github.com/Shreeyak/robotiq.git + +### mimic tag in URDF +This gripper is developed for ROS and uses the `mimic` tag within the URDF files to make the gripper move. From our research `mimic` tag within URDF is not supported by pybullet. To overcome this, one can use the `createConstraint` function. Please refer to [this](https://github.com/bulletphysics/bullet3/blob/master/examples/pybullet/examples/mimicJointConstraint.py) example from the bullet3 repo to see how to replicate a `mimic` joint: + +```python +#a mimic joint can act as a gear between two joints +#you can control the gear ratio in magnitude and sign (>0 reverses direction) + +import pybullet as p +import time +p.connect(p.GUI) +p.loadURDF("plane.urdf",0,0,-2) +wheelA = p.loadURDF("differential/diff_ring.urdf",[0,0,0]) +for i in range(p.getNumJoints(wheelA)): + print(p.getJointInfo(wheelA,i)) + p.setJointMotorControl2(wheelA,i,p.VELOCITY_CONTROL,targetVelocity=0,force=0) + + +c = p.createConstraint(wheelA,1,wheelA,3,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) +p.changeConstraint(c,gearRatio=1, maxForce=10000) + +c = p.createConstraint(wheelA,2,wheelA,4,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) +p.changeConstraint(c,gearRatio=-1, maxForce=10000) + +c = p.createConstraint(wheelA,1,wheelA,4,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) +p.changeConstraint(c,gearRatio=-1, maxForce=10000) + + +p.setRealTimeSimulation(1) +while(1): + p.setGravity(0,0,-10) + time.sleep(0.01) +#p.removeConstraint(c) + +``` + + +Details on `createConstraint` can be found in the pybullet [getting started](https://docs.google.com/document/d/10sXEhzFRSnvFcl3XxNGhnD4N2SedqwdAvK3dsihxVUA/edit#heading=h.fq749wu22x4c) guide. + +### Files in folder +Since parameters like gear ratio and direction are required, one can find the `robotiq_2f_85_mimic_joints.urdf` which contains the mimic tags as in original URDF, which can be used as a reference. It was generated from `robotiq/robotiq_2f_robot/robot/simple_rq2f85_pybullet.urdf.xacro` as so: +``` +rosrun xacro xacro --inorder simple_rq2f85_pybullet.urdf.xacro +adaptive_transmission:="true" > robotiq_2f_85_mimic_joints.urdf +``` + +The URDF meant for use in pybullet is `robotiq_2f_85.urdf` and it is generated in a similar manner as above by running: +``` +rosrun xacro xacro --inorder simple_rq2f85_pybullet.urdf.xacro > robotiq_2f_85.urdf +``` diff --git a/cliport/environments/assets/ur5/gripper/robotiq-2f-base.mtl b/cliport/environments/assets/ur5/gripper/robotiq-2f-base.mtl new file mode 100644 index 0000000000000000000000000000000000000000..35d36cfef2bea7c53cb427fdfdc6a639abfa9110 --- /dev/null +++ b/cliport/environments/assets/ur5/gripper/robotiq-2f-base.mtl @@ -0,0 +1,13 @@ +# Blender MTL File: 'gripper-2f.blend' +# Material Count: 1 + +newmtl Default +Ns 96.078431 +Ka 1.000000 1.000000 1.000000 +Kd 0.640000 0.640000 0.640000 +Ks 0.500000 0.500000 0.500000 +Ke 0.000000 0.000000 0.000000 +Ni 1.000000 +d 1.000000 +illum 2 +map_Kd textures/gripper-2f_BaseColor.jpg diff --git a/cliport/environments/assets/ur5/gripper/robotiq-2f-base.obj b/cliport/environments/assets/ur5/gripper/robotiq-2f-base.obj new file mode 100644 index 0000000000000000000000000000000000000000..200622647ea851b45d264e20333ff37d40d25031 --- /dev/null +++ b/cliport/environments/assets/ur5/gripper/robotiq-2f-base.obj @@ -0,0 +1,88905 @@ +# Blender v2.79 (sub 5) OBJ File: 'gripper-2f.blend' +# www.blender.org +mtllib robotiq-2f-base.mtl +o robotiq-2f-base_Part__Feature +v -0.055000 -0.371734 0.585177 +v -0.047000 -0.369228 0.589402 +v -0.047000 -0.378005 0.570066 +v 0.055000 -0.371734 0.585177 +v 0.047000 -0.378005 0.570066 +v 0.047000 -0.369228 0.589402 +v 0.055000 -0.360684 0.600386 +v 0.047000 -0.355383 0.605503 +v 0.055000 -0.346198 0.612370 +v 0.047000 -0.337579 0.617078 +v 0.055000 -0.329188 0.620374 +v 0.047000 -0.317246 0.623199 +v 0.055000 -0.310721 0.623897 +v 0.047000 -0.296011 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b/cliport/environments/assets/ur5/license.txt @@ -0,0 +1,32 @@ +Original work Copyright 2018 ROS Industrial (https://rosindustrial.org/) +Modified work Copyright 2018 Virtana, Inc (www.virtanatech.com) + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express +or implied. See the License for the specific language governing +permissions and limitations under the License. + +Changenotes: + +2018-05-08: Vijay Pradeep (vijay@virtanatech.com) +The visual/ mesh files were generated from the dae files in the +ros-industrial universal robot repo [1]. Since the collada pyBullet +parser is somewhat limited, it is unable to parse the UR collada mesh +files. Thus, we imported these collada files into blender and +converted them into STL files. We lost material definitions during +the conversion, but that's ok. + +The URDF was generated by running the xacro xml preprocessor on the +URDF included in the ur_description repo already mentioned here. +Additional manual tweaking was required to update resource paths and +to remove errors caused by missing inertia elements. Varios Gazebo +plugin tags were also removed. + +[1] - https://github.com/ros-industrial/universal_robot/tree/kinetic-devel/ur_description/meshes/ur5/visual diff --git a/cliport/environments/assets/ur5/plane.obj b/cliport/environments/assets/ur5/plane.obj new file mode 100644 index 0000000000000000000000000000000000000000..6062095314e3d3f5b9da26a580ca2e6ee14623e6 --- /dev/null +++ b/cliport/environments/assets/ur5/plane.obj @@ -0,0 +1,18 @@ +# Blender v2.66 (sub 1) OBJ File: '' +# www.blender.org +mtllib plane.mtl +o Plane +v 15.000000 -15.000000 0.000000 +v 15.000000 15.000000 0.000000 +v -15.000000 15.000000 0.000000 +v -15.000000 -15.000000 0.000000 + +vt 15.000000 0.000000 +vt 15.000000 15.000000 +vt 0.000000 15.000000 +vt 0.000000 0.000000 + +usemtl Material +s off +f 1/1 2/2 3/3 +f 1/1 3/3 4/4 diff --git a/cliport/environments/assets/ur5/spatula/base.obj b/cliport/environments/assets/ur5/spatula/base.obj new file mode 100644 index 0000000000000000000000000000000000000000..e3fc8e71a08372d30e27a12a3a4b272262b9b802 --- /dev/null +++ b/cliport/environments/assets/ur5/spatula/base.obj @@ -0,0 +1,396 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 7.413 37.27 25 +v 7.413 37.27 0 +v 33.513 17.913 25 +v 35.107 14.542 25 +v 35.107 14.542 0 +v 33.513 17.913 0 +v -31.596 21.112 25 +v -31.596 21.112 0 +v -33.513 17.913 0 +v -33.513 17.913 25 +v 3.725 37.817 0 +v 3.725 37.817 25 +v -29.374 24.107 25 +v -29.374 24.107 0 +v 0 38 0 +v 11.031 36.364 25 +v 11.031 36.364 0 +v 14.542 35.107 25 +v 14.542 35.107 0 +v -37.27 -7.413 25 +v 17.913 33.513 25 +v -37.27 -7.413 0 +v -11.031 -36.364 25 +v 17.913 33.513 0 +v -36.364 -11.031 0 +v -14.542 -35.107 25 +v -26.87 26.87 25 +v -36.364 -11.031 25 +v -14.542 -35.107 0 +v -26.87 26.87 0 +v -3.725 37.817 0 +v -11.031 -36.364 0 +v 24.107 -29.374 25 +v -17.913 -33.513 25 +v -17.913 -33.513 0 +v 21.112 -31.596 25 +v 21.112 -31.596 0 +v 24.107 -29.374 0 +v 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36.364 25 +v -11.031 36.364 0 +v -7.413 37.27 25 +v 26.87 26.87 25 +v -7.413 37.27 0 +v -26.87 -26.87 25 +v -26.87 -26.87 0 +v 37.817 3.725 25 +v 0 -38 25 +v 31.596 -21.112 25 +v 0 -38 0 +v 31.596 -21.112 0 +v 29.374 24.107 25 +v -29.374 -24.107 25 +v -29.374 -24.107 0 +v 29.374 -24.107 25 +v 31.596 21.112 25 +v 29.374 -24.107 0 +v -3.725 -37.817 25 +v -31.596 -21.112 25 +v -3.725 -37.817 0 +v -31.596 -21.112 0 +v -37.817 3.725 25 +v -37.817 3.725 0 +v 26.87 -26.87 25 +v 26.87 -26.87 0 +v -37.27 7.413 0 +v -37.27 7.413 25 +v -7.413 -37.27 25 +v -33.513 -17.913 25 +v -33.513 -17.913 0 +v -7.413 -37.27 0 +v -36.364 11.031 25 +v -36.364 11.031 0 +v -35.107 -14.542 25 +v -35.107 -14.542 0 +v -35.107 14.542 25 +v -35.107 14.542 0 +# 128 vertices + +g group_0_2829873 + +usemtl color_2829873 +s 0 + +f 3 4 5 +f 3 5 6 +f 7 8 9 +f 7 9 10 +f 1 2 11 +f 1 11 12 +f 16 17 2 +f 16 2 1 +f 13 14 8 +f 13 8 7 +f 18 19 17 +f 18 17 16 +f 23 26 29 +f 23 29 32 +f 26 34 35 +f 26 35 29 +f 33 36 37 +f 33 37 38 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a/cliport/environments/assets/ur5/spatula/spatula-base.urdf b/cliport/environments/assets/ur5/spatula/spatula-base.urdf new file mode 100644 index 0000000000000000000000000000000000000000..5c81d543ee08e6de5964a59c49d8398c3e9a0993 --- /dev/null +++ b/cliport/environments/assets/ur5/spatula/spatula-base.urdf @@ -0,0 +1,73 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/ur5/spatula/suction/base.obj b/cliport/environments/assets/ur5/spatula/suction/base.obj new file mode 100644 index 0000000000000000000000000000000000000000..e3fc8e71a08372d30e27a12a3a4b272262b9b802 --- /dev/null +++ b/cliport/environments/assets/ur5/spatula/suction/base.obj @@ -0,0 +1,396 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 7.413 37.27 25 +v 7.413 37.27 0 +v 33.513 17.913 25 +v 35.107 14.542 25 +v 35.107 14.542 0 +v 33.513 17.913 0 +v -31.596 21.112 25 +v -31.596 21.112 0 +v -33.513 17.913 0 +v -33.513 17.913 25 +v 3.725 37.817 0 +v 3.725 37.817 25 +v -29.374 24.107 25 +v -29.374 24.107 0 +v 0 38 0 +v 11.031 36.364 25 +v 11.031 36.364 0 +v 14.542 35.107 25 +v 14.542 35.107 0 +v -37.27 -7.413 25 +v 17.913 33.513 25 +v -37.27 -7.413 0 +v -11.031 -36.364 25 +v 17.913 33.513 0 +v -36.364 -11.031 0 +v -14.542 -35.107 25 +v -26.87 26.87 25 +v -36.364 -11.031 25 +v -14.542 -35.107 0 +v -26.87 26.87 0 +v -3.725 37.817 0 +v -11.031 -36.364 0 +v 24.107 -29.374 25 +v -17.913 -33.513 25 +v -17.913 -33.513 0 +v 21.112 -31.596 25 +v 21.112 -31.596 0 +v 24.107 -29.374 0 +v -3.725 37.817 25 +v 0 38 25 +v 17.913 -33.513 25 +v 17.913 -33.513 0 +v 37.817 -3.725 25 +v -37.817 -3.725 25 +v -37.817 -3.725 0 +v 37.817 -3.725 0 +v 38 0 0 +v -21.112 -31.596 25 +v 38 0 25 +v 14.542 -35.107 25 +v -21.112 -31.596 0 +v 36.364 11.031 25 +v 14.542 -35.107 0 +v 36.364 11.031 0 +v 37.27 -7.413 25 +v 37.27 -7.413 0 +v -24.107 29.374 25 +v 21.112 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-21.112 25 +v -3.725 -37.817 0 +v -31.596 -21.112 0 +v -37.817 3.725 25 +v -37.817 3.725 0 +v 26.87 -26.87 25 +v 26.87 -26.87 0 +v -37.27 7.413 0 +v -37.27 7.413 25 +v -7.413 -37.27 25 +v -33.513 -17.913 25 +v -33.513 -17.913 0 +v -7.413 -37.27 0 +v -36.364 11.031 25 +v -36.364 11.031 0 +v -35.107 -14.542 25 +v -35.107 -14.542 0 +v -35.107 14.542 25 +v -35.107 14.542 0 +# 128 vertices + +g group_0_2829873 + +usemtl color_2829873 +s 0 + +f 3 4 5 +f 3 5 6 +f 7 8 9 +f 7 9 10 +f 1 2 11 +f 1 11 12 +f 16 17 2 +f 16 2 1 +f 13 14 8 +f 13 8 7 +f 18 19 17 +f 18 17 16 +f 23 26 29 +f 23 29 32 +f 26 34 35 +f 26 35 29 +f 33 36 37 +f 33 37 38 +f 15 31 39 +f 15 39 40 +f 20 22 25 +f 20 25 28 +f 36 41 42 +f 36 42 37 +f 44 45 22 +f 44 22 20 +f 21 24 19 +f 21 19 18 +f 43 46 47 +f 43 47 49 +f 27 30 14 +f 27 14 13 +f 41 50 53 +f 41 53 42 +f 43 55 56 +f 43 56 46 +f 34 48 51 +f 34 51 35 +f 4 52 54 +f 4 54 5 +f 50 64 65 +f 50 65 53 +f 57 59 30 +f 57 30 27 +f 55 60 62 +f 55 62 56 +f 66 67 59 +f 66 59 57 +f 64 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sha256:48573c6d123207610d979d63b6a95a14a3dba00e950d40a7bac7e7cf4f4035d9 +size 82984 diff --git a/cliport/environments/assets/ur5/workspace.urdf b/cliport/environments/assets/ur5/workspace.urdf new file mode 100644 index 0000000000000000000000000000000000000000..018b3eb3fdad6c6c92e332a94bc7e300aa05716f --- /dev/null +++ b/cliport/environments/assets/ur5/workspace.urdf @@ -0,0 +1,30 @@ + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/assets/zone/zone.obj b/cliport/environments/assets/zone/zone.obj new file mode 100644 index 0000000000000000000000000000000000000000..4766761c32178ad89a74799cb7466dfee4f3421d --- /dev/null +++ b/cliport/environments/assets/zone/zone.obj @@ -0,0 +1,64 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 10 -10 20 +v 10 -10 0 +v 10 10 0 +v 10 10 20 +v 9.002 9.003 20 +v 9.002 -9.002 20 +v -10 10 0 +v -10 10 20 +v -9.003 9.003 20 +v -9.003 9.003 0 +v 9.002 9.003 0 +v 9.002 -9.002 0 +v -9.003 -9.002 0 +v -9.003 -9.002 20 +v -10 -10 0 +v -10 -10 20 +# 16 vertices + +g group_0_15277357 + +usemtl color_15277357 +s 0 + +f 1 2 3 +f 1 3 4 +f 4 5 6 +f 4 6 1 +f 9 10 11 +f 9 11 5 +f 6 12 13 +f 6 13 14 +f 10 9 14 +f 10 14 13 +f 7 10 13 +f 7 13 15 +f 4 8 5 +f 9 5 8 +f 8 7 15 +f 8 15 16 +f 10 7 11 +f 3 11 7 +f 11 3 12 +f 2 12 3 +f 14 16 6 +f 1 6 16 +f 16 15 2 +f 16 2 1 +f 9 8 14 +f 16 14 8 +f 7 8 3 +f 4 3 8 +f 2 15 12 +f 13 12 15 +f 12 6 5 +f 12 5 11 +# 32 faces + + #end of obj_0 + diff --git a/cliport/environments/assets/zone/zone.urdf b/cliport/environments/assets/zone/zone.urdf new file mode 100644 index 0000000000000000000000000000000000000000..bf43e852362a2bad0ccf48244b80e801b3fd8a34 --- /dev/null +++ b/cliport/environments/assets/zone/zone.urdf @@ -0,0 +1,23 @@ + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cliport/environments/environment.py b/cliport/environments/environment.py new file mode 100644 index 0000000000000000000000000000000000000000..9305684d6e9596cac47c990fb81cdf903a12c818 --- /dev/null +++ b/cliport/environments/environment.py @@ -0,0 +1,676 @@ +"""Environment class.""" + +import os +import tempfile +import time +import cv2 +import imageio + +import gym +import numpy as np +from cliport.tasks import cameras +from cliport.utils import pybullet_utils +from cliport.utils import utils +import string +import pybullet as p +import tempfile +import random +import sys + +PLACE_STEP = 0.0003 +PLACE_DELTA_THRESHOLD = 0.005 + +UR5_URDF_PATH = 'ur5/ur5.urdf' +UR5_WORKSPACE_URDF_PATH = 'ur5/workspace.urdf' +PLANE_URDF_PATH = 'plane/plane.urdf' + + +class Environment(gym.Env): + """OpenAI Gym-style environment class.""" + + def __init__(self, + assets_root, + task=None, + disp=False, + shared_memory=False, + hz=240, + record_cfg=None): + """Creates OpenAI Gym-style environment with PyBullet. + + Args: + assets_root: root directory of assets. + task: the task to use. If None, the user must call set_task for the + environment to work properly. + disp: show environment with PyBullet's built-in display viewer. + shared_memory: run with shared memory. + hz: PyBullet physics simulation step speed. Set to 480 for deformables. + + Raises: + RuntimeError: if pybullet cannot load fileIOPlugin. + """ + self.curr_video = [] + self.pix_size = 0.003125 + self.obj_ids = {'fixed': [], 'rigid': [], 'deformable': []} + self.objects = self.obj_ids # make a copy + + self.homej = np.array([-1, -0.5, 0.5, -0.5, -0.5, 0]) * np.pi + self.agent_cams = cameras.RealSenseD415.CONFIG + self.record_cfg = record_cfg + self.save_video = True + self.step_counter = 0 + + self.assets_root = assets_root + + color_tuple = [ + gym.spaces.Box(0, 255, config['image_size'] + (3,), dtype=np.uint8) + for config in self.agent_cams + ] + depth_tuple = [ + gym.spaces.Box(0.0, 20.0, config['image_size'], dtype=np.float32) + for config in self.agent_cams + ] + self.observation_space = gym.spaces.Dict({ + 'color': gym.spaces.Tuple(color_tuple), + 'depth': gym.spaces.Tuple(depth_tuple), + }) + self.position_bounds = gym.spaces.Box( + low=np.array([0.25, -0.5, 0.], dtype=np.float32), + high=np.array([0.75, 0.5, 0.28], dtype=np.float32), + shape=(3,), + dtype=np.float32) + self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]]) + + self.action_space = gym.spaces.Dict({ + 'pose0': + gym.spaces.Tuple( + (self.position_bounds, + gym.spaces.Box(-1.0, 1.0, shape=(4,), dtype=np.float32))), + 'pose1': + gym.spaces.Tuple( + (self.position_bounds, + gym.spaces.Box(-1.0, 1.0, shape=(4,), dtype=np.float32))) + }) + + # Start PyBullet. + disp_option = p.DIRECT + if disp: + disp_option = p.GUI + if shared_memory: + disp_option = p.SHARED_MEMORY + client = p.connect(disp_option) + file_io = p.loadPlugin('fileIOPlugin', physicsClientId=client) + if file_io < 0: + raise RuntimeError('pybullet: cannot load FileIO!') + if file_io >= 0: + p.executePluginCommand( + file_io, + textArgument=assets_root, + intArgs=[p.AddFileIOAction], + physicsClientId=client) + + p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0) + p.setPhysicsEngineParameter(enableFileCaching=0) + p.setAdditionalSearchPath(assets_root) + p.setAdditionalSearchPath(tempfile.gettempdir()) + p.setTimeStep(1. / hz) + + # If using --disp, move default camera closer to the scene. + if disp: + target = p.getDebugVisualizerCamera()[11] + p.resetDebugVisualizerCamera( + cameraDistance=1.1, + cameraYaw=90, + cameraPitch=-25, + cameraTargetPosition=target) + + if task: + self.set_task(task) + + def __del__(self): + if hasattr(self, 'video_writer'): + self.video_writer.close() + + @property + def is_static(self): + """Return true if objects are no longer moving.""" + v = [np.linalg.norm(p.getBaseVelocity(i)[0]) + for i in self.obj_ids['rigid']] + return all(np.array(v) < 5e-3) + + def fill_dummy_template(self, template): + """check if there are empty templates that haven't been fulfilled yet. if so. fill in dummy numbers """ + full_template_path = os.path.join(self.assets_root, template) + with open(full_template_path, 'r') as file: + fdata = file.read() + + fill = False + for field in ['DIMH', 'DIMR', 'DIMX', 'DIMY', 'DIMZ', 'DIM']: + # usually 3 should be enough + if field in fdata: + default_replace_vals = np.random.uniform(0.03, 0.05, size=(3,)).tolist() # [0.03,0.03,0.03] + for i in range(len(default_replace_vals)): + fdata = fdata.replace(f'{field}{i}', str(default_replace_vals[i])) + fill = True + + for field in ['HALF']: + # usually 3 should be enough + if field in fdata: + default_replace_vals = np.random.uniform(0.01, 0.03, size=(3,)).tolist() # [0.015,0.015,0.015] + for i in range(len(default_replace_vals)): + fdata = fdata.replace(f'{field}{i}', str(default_replace_vals[i])) + fill = True + + if fill: + alphabet = string.ascii_lowercase + string.digits + rname = ''.join(random.choices(alphabet, k=16)) + tmpdir = tempfile.gettempdir() + template_filename = os.path.split(template)[-1] + fname = os.path.join(tmpdir, f'{template_filename}.{rname}') + with open(fname, 'w') as file: + file.write(fdata) + # print("fill-in dummys") + + return fname + else: + return template + + def add_object(self, urdf, pose, category='rigid', color=None, **kwargs): + """List of (fixed, rigid, or deformable) objects in env.""" + fixed_base = 1 if category == 'fixed' else 0 + + if 'template' in urdf: + if not os.path.exists(os.path.join(self.assets_root, urdf)): + urdf = urdf.replace("-template", "") + + urdf = self.fill_dummy_template(urdf) + + if not os.path.exists(os.path.join(self.assets_root, urdf)): + print(f"missing urdf error: {os.path.join(self.assets_root, urdf)}. use dummy block.") + urdf = 'stacking/block.urdf' + + obj_id = pybullet_utils.load_urdf( + p, + os.path.join(self.assets_root, urdf), + pose[0], + pose[1], + useFixedBase=fixed_base) + + if not obj_id is None: + self.obj_ids[category].append(obj_id) + + if color is not None: + if type(color) is str: + color = utils.COLORS[color] + color = color + [1.] + p.changeVisualShape(obj_id, -1, rgbaColor=color) + + if hasattr(self, 'record_cfg') and 'blender_render' in self.record_cfg and self.record_cfg['blender_render']: + # print("urdf:", os.path.join(self.assets_root, urdf)) + # if color is None: + # color = (0.5,0.5,0.5,1) # by default + print("color:", color) + + self.blender_recorder.register_object(obj_id, os.path.join(self.assets_root, urdf), color=color) + + return obj_id + + def set_color(self, obj_id, color): + p.changeVisualShape(obj_id, -1, rgbaColor=color + [1]) + + def set_object_color(self, *args, **kwargs): + return self.set_color(*args, **kwargs) + + # --------------------------------------------------------------------------- + # Standard Gym Functions + # --------------------------------------------------------------------------- + + def seed(self, seed=None): + self._random = np.random.RandomState(seed) + return seed + + def reset(self): + """Performs common reset functionality for all supported tasks.""" + if not self.task: + raise ValueError('environment task must be set. Call set_task or pass ' + 'the task arg in the environment constructor.') + self.obj_ids = {'fixed': [], 'rigid': [], 'deformable': []} + p.resetSimulation(p.RESET_USE_DEFORMABLE_WORLD) + p.setGravity(0, 0, -9.8) + + # Temporarily disable rendering to load scene faster. + p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) + + plane = pybullet_utils.load_urdf(p, os.path.join(self.assets_root, PLANE_URDF_PATH), + [0, 0, -0.001]) + workspace = pybullet_utils.load_urdf( + p, os.path.join(self.assets_root, UR5_WORKSPACE_URDF_PATH), [0.5, 0, 0]) + + # Load UR5 robot arm equipped with suction end effector. + # TODO(andyzeng): add back parallel-jaw grippers. + self.ur5 = pybullet_utils.load_urdf( + p, os.path.join(self.assets_root, UR5_URDF_PATH)) + self.ee = self.task.ee(self.assets_root, self.ur5, 9, self.obj_ids) + self.ee_tip = 10 # Link ID of suction cup. + + if hasattr(self, 'record_cfg') and 'blender_render' in self.record_cfg and self.record_cfg['blender_render']: + from misc.pyBulletSimRecorder import PyBulletRecorder + self.blender_recorder = PyBulletRecorder() + + self.blender_recorder.register_object(plane, os.path.join(self.assets_root, PLANE_URDF_PATH)) + self.blender_recorder.register_object(workspace, os.path.join(self.assets_root, UR5_WORKSPACE_URDF_PATH)) + self.blender_recorder.register_object(self.ur5, os.path.join(self.assets_root, UR5_URDF_PATH)) + + self.blender_recorder.register_object(self.ee.base, self.ee.base_urdf_path) + if hasattr(self.ee, 'body'): + self.blender_recorder.register_object(self.ee.body, self.ee.urdf_path) + + + # Get revolute joint indices of robot (skip fixed joints). + n_joints = p.getNumJoints(self.ur5) + joints = [p.getJointInfo(self.ur5, i) for i in range(n_joints)] + self.joints = [j[0] for j in joints if j[2] == p.JOINT_REVOLUTE] + + # Move robot to home joint configuration. + for i in range(len(self.joints)): + p.resetJointState(self.ur5, self.joints[i], self.homej[i]) + + # Reset end effector. + self.ee.release() + + # Reset task. + self.task.reset(self) + + # Re-enable rendering. + p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1) + + self.step() + # obs, _, _, _ = self.step() + # return obs + + def step(self, action=None): + """Execute action with specified primitive. + + Args: + action: action to execute. + + Returns: + (obs, reward, done, info) tuple containing MDP step data. + """ + if action is not None: + timeout = self.task.primitive(self.movej, self.movep, self.ee, action['pose0'], action['pose1']) + + # Exit early if action times out. We still return an observation + # so that we don't break the Gym API contract. + if timeout: + obs = {'color': (), 'depth': ()} + for config in self.agent_cams: + color, depth, _ = self.render_camera(config) + obs['color'] += (color,) + obs['depth'] += (depth,) + return obs, 0.0, True, self.info + + start_time = time.time() + # Step simulator asynchronously until objects settle. + while not self.is_static: + self.step_simulation() + if time.time() - start_time > 5: # timeout + break + + # Get task rewards. + reward, info = self.task.reward() if action is not None else (0, {}) + done = self.task.done() + + # Add ground truth robot state into info. + info.update(self.info) + + obs = self._get_obs() + + if not os.path.exists(self.record_cfg['save_video_path']): + os.mkdir(self.record_cfg['save_video_path']) + self.video_path = os.path.join(self.record_cfg['save_video_path'], "123.mp4") + video_writer = imageio.get_writer(self.video_path, + fps=self.record_cfg['fps'], + format='FFMPEG', + codec='h264', ) + print(f"has {len(self.curr_video)} frames to save") + for color in self.curr_video: + video_writer.append_data(color) + print("save video to ", self.video_path) + video_writer.close() + self.cur_obs = obs + self.cur_reward = reward + self.cur_done = done + self.cur_info = info + yield "Task Generated ==> Asset Generated ==> API Reviewed ==> Error Reviewed ==> Code Generated ==> Running Simulation", self.generated_code, self.video_path + + + def step_simulation(self): + p.stepSimulation() + self.step_counter += 1 + + if self.save_video and self.step_counter % 5 == 0: + self.add_video_frame() + + def render(self, mode='rgb_array'): + # Render only the color image from the first camera. + # Only support rgb_array for now. + if mode != 'rgb_array': + raise NotImplementedError('Only rgb_array implemented') + color, _, _ = self.render_camera(self.agent_cams[0]) + + return color + + def render_camera(self, config, image_size=None, shadow=1): + """Render RGB-D image with specified camera configuration.""" + if not image_size: + image_size = config['image_size'] + + # OpenGL camera settings. + lookdir = np.float32([0, 0, 1]).reshape(3, 1) + updir = np.float32([0, -1, 0]).reshape(3, 1) + rotation = p.getMatrixFromQuaternion(config['rotation']) + rotm = np.float32(rotation).reshape(3, 3) + lookdir = (rotm @ lookdir).reshape(-1) + updir = (rotm @ updir).reshape(-1) + lookat = config['position'] + lookdir + focal_len = config['intrinsics'][0] + znear, zfar = config['zrange'] + viewm = p.computeViewMatrix(config['position'], lookat, updir) + fovh = (image_size[0] / 2) / focal_len + fovh = 180 * np.arctan(fovh) * 2 / np.pi + + # Notes: 1) FOV is vertical FOV 2) aspect must be float + aspect_ratio = image_size[1] / image_size[0] + projm = p.computeProjectionMatrixFOV(fovh, aspect_ratio, znear, zfar) + + # Render with OpenGL camera settings. + _, _, color, depth, segm = p.getCameraImage( + width=image_size[1], + height=image_size[0], + viewMatrix=viewm, + projectionMatrix=projm, + shadow=shadow, + flags=p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX, + renderer=p.ER_BULLET_HARDWARE_OPENGL) + + # Get color image. + color_image_size = (image_size[0], image_size[1], 4) + color = np.array(color, dtype=np.uint8).reshape(color_image_size) + color = color[:, :, :3] # remove alpha channel + if config['noise']: + color = np.int32(color) + color += np.int32(self._random.normal(0, 3, image_size)) + color = np.uint8(np.clip(color, 0, 255)) + + # Get depth image. + depth_image_size = (image_size[0], image_size[1]) + zbuffer = np.array(depth).reshape(depth_image_size) + depth = (zfar + znear - (2. * zbuffer - 1.) * (zfar - znear)) + depth = (2. * znear * zfar) / depth + if config['noise']: + depth += self._random.normal(0, 0.003, depth_image_size) + + # Get segmentation image. + segm = np.uint8(segm).reshape(depth_image_size) + + return color, depth, segm + + @property + def info(self): + """Environment info variable with object poses, dimensions, and colors.""" + + # Some tasks create and remove zones, so ignore those IDs. + # removed_ids = [] + # if (isinstance(self.task, tasks.names['cloth-flat-notarget']) or + # isinstance(self.task, tasks.names['bag-alone-open'])): + # removed_ids.append(self.task.zone_id) + + info = {} # object id : (position, rotation, dimensions) + for obj_ids in self.obj_ids.values(): + for obj_id in obj_ids: + pos, rot = p.getBasePositionAndOrientation(obj_id) + dim = p.getVisualShapeData(obj_id)[0][3] + info[obj_id] = (pos, rot, dim) + + info['lang_goal'] = self.get_lang_goal() + return info + + def set_task(self, task): + task.set_assets_root(self.assets_root) + self.task = task + + def get_task_name(self): + return type(self.task).__name__ + + def get_lang_goal(self): + if self.task: + return self.task.get_lang_goal() + else: + raise Exception("No task for was set") + + # --------------------------------------------------------------------------- + # Robot Movement Functions + # --------------------------------------------------------------------------- + + def movej(self, targj, speed=0.01, timeout=5): + """Move UR5 to target joint configuration.""" + if self.save_video: + timeout = timeout * 5 # 50? + + t0 = time.time() + while (time.time() - t0) < timeout: + currj = [p.getJointState(self.ur5, i)[0] for i in self.joints] + currj = np.array(currj) + diffj = targj - currj + if all(np.abs(diffj) < 1e-2): + return False + + # Move with constant velocity + norm = np.linalg.norm(diffj) + v = diffj / norm if norm > 0 else 0 + stepj = currj + v * speed + gains = np.ones(len(self.joints)) + p.setJointMotorControlArray( + bodyIndex=self.ur5, + jointIndices=self.joints, + controlMode=p.POSITION_CONTROL, + targetPositions=stepj, + positionGains=gains) + self.step_counter += 1 + self.step_simulation() + + print(f'Warning: movej exceeded {timeout} second timeout. Skipping.') + return True + + def start_rec(self, video_filename): + assert self.record_cfg + + # make video directory + if not os.path.exists(self.record_cfg['save_video_path']): + os.makedirs(self.record_cfg['save_video_path']) + + # close and save existing writer + if hasattr(self, 'video_writer'): + self.video_writer.close() + + # initialize writer + self.video_writer = imageio.get_writer(os.path.join(self.record_cfg['save_video_path'], + f"{video_filename}.mp4"), + fps=self.record_cfg['fps'], + format='FFMPEG', + codec='h264',) + p.setRealTimeSimulation(False) + self.save_video = True + + def end_rec(self): + if hasattr(self, 'video_writer'): + self.video_writer.close() + + p.setRealTimeSimulation(True) + self.save_video = False + + def add_video_frame(self): + # Render frame. + config = self.agent_cams[0] + image_size = (self.record_cfg['video_height'], self.record_cfg['video_width']) + color, depth, _ = self.render_camera(config, image_size, shadow=0) + color = np.array(color) + + if hasattr(self.record_cfg, 'blender_render') and self.record_cfg['blender_render']: + # print("add blender key frame") + self.blender_recorder.add_keyframe() + + # Add language instruction to video. + if self.record_cfg['add_text']: + lang_goal = self.get_lang_goal() + reward = f"Success: {self.task.get_reward():.3f}" + + font = cv2.FONT_HERSHEY_DUPLEX + font_scale = 0.65 + font_thickness = 1 + + # Write language goal. + line_length = 60 + for i in range(len(lang_goal) // line_length + 1): + lang_textsize = cv2.getTextSize(lang_goal[i*line_length:(i+1)*line_length], font, font_scale, font_thickness)[0] + lang_textX = (image_size[1] - lang_textsize[0]) // 2 + color = cv2.putText(color, lang_goal[i*line_length:(i+1)*line_length], org=(lang_textX, 570+i*30), # 600 + fontScale=font_scale, + fontFace=font, + color=(0, 0, 0), + thickness=font_thickness, lineType=cv2.LINE_AA) + + ## Write Reward. + # reward_textsize = cv2.getTextSize(reward, font, font_scale, font_thickness)[0] + # reward_textX = (image_size[1] - reward_textsize[0]) // 2 + # + # color = cv2.putText(color, reward, org=(reward_textX, 634), + # fontScale=font_scale, + # fontFace=font, + # color=(0, 0, 0), + # thickness=font_thickness, lineType=cv2.LINE_AA) + + color = np.array(color) + + if 'add_task_text' in self.record_cfg and self.record_cfg['add_task_text']: + lang_goal = self.get_task_name() + reward = f"Success: {self.task.get_reward():.3f}" + + font = cv2.FONT_HERSHEY_DUPLEX + font_scale = 1 + font_thickness = 2 + + # Write language goal. + lang_textsize = cv2.getTextSize(lang_goal, font, font_scale, font_thickness)[0] + lang_textX = (image_size[1] - lang_textsize[0]) // 2 + + color = cv2.putText(color, lang_goal, org=(lang_textX, 600), + fontScale=font_scale, + fontFace=font, + color=(255, 0, 0), + thickness=font_thickness, lineType=cv2.LINE_AA) + + color = np.array(color) + + self.curr_video.append(color) + self.video_writer.append_data(color) + + def movep(self, pose, speed=0.01): + """Move UR5 to target end effector pose.""" + targj = self.solve_ik(pose) + return self.movej(targj, speed) + + def solve_ik(self, pose): + """Calculate joint configuration with inverse kinematics.""" + joints = p.calculateInverseKinematics( + bodyUniqueId=self.ur5, + endEffectorLinkIndex=self.ee_tip, + targetPosition=pose[0], + targetOrientation=pose[1], + lowerLimits=[-3 * np.pi / 2, -2.3562, -17, -17, -17, -17], + upperLimits=[-np.pi / 2, 0, 17, 17, 17, 17], + jointRanges=[np.pi, 2.3562, 34, 34, 34, 34], # * 6, + restPoses=np.float32(self.homej).tolist(), + maxNumIterations=100, + residualThreshold=1e-5) + joints = np.float32(joints) + joints[2:] = (joints[2:] + np.pi) % (2 * np.pi) - np.pi + return joints + + def _get_obs(self): + # Get RGB-D camera image observations. + obs = {'color': (), 'depth': ()} + for config in self.agent_cams: + color, depth, _ = self.render_camera(config) + obs['color'] += (color,) + obs['depth'] += (depth,) + + return obs + + def get_object_pose(self, obj_id): + return p.getBasePositionAndOrientation(obj_id) + + def get_object_size(self, obj_id): + """ approximate object's size using AABB """ + aabb_min, aabb_max = p.getAABB(obj_id) + + size_x = aabb_max[0] - aabb_min[0] + size_y = aabb_max[1] - aabb_min[1] + size_z = aabb_max[2] - aabb_min[2] + return size_z * size_y * size_x + + + +class EnvironmentNoRotationsWithHeightmap(Environment): + """Environment that disables any rotations and always passes [0, 0, 0, 1].""" + + def __init__(self, + assets_root, + task=None, + disp=False, + shared_memory=False, + hz=240): + super(EnvironmentNoRotationsWithHeightmap, + self).__init__(assets_root, task, disp, shared_memory, hz) + + heightmap_tuple = [ + gym.spaces.Box(0.0, 20.0, (320, 160, 3), dtype=np.float32), + gym.spaces.Box(0.0, 20.0, (320, 160), dtype=np.float32), + ] + self.observation_space = gym.spaces.Dict({ + 'heightmap': gym.spaces.Tuple(heightmap_tuple), + }) + self.action_space = gym.spaces.Dict({ + 'pose0': gym.spaces.Tuple((self.position_bounds,)), + 'pose1': gym.spaces.Tuple((self.position_bounds,)) + }) + + def step(self, action=None): + """Execute action with specified primitive. + + Args: + action: action to execute. + + Returns: + (obs, reward, done, info) tuple containing MDP step data. + """ + if action is not None: + action = { + 'pose0': (action['pose0'][0], [0., 0., 0., 1.]), + 'pose1': (action['pose1'][0], [0., 0., 0., 1.]), + } + return super(EnvironmentNoRotationsWithHeightmap, self).step(action) + + def _get_obs(self): + obs = {} + + color_depth_obs = {'color': (), 'depth': ()} + for config in self.agent_cams: + color, depth, _ = self.render_camera(config) + color_depth_obs['color'] += (color,) + color_depth_obs['depth'] += (depth,) + cmap, hmap = utils.get_fused_heightmap(color_depth_obs, self.agent_cams, + self.task.bounds, pix_size=0.003125) + obs['heightmap'] = (cmap, hmap) + return obs + diff --git a/cliport/environments/environment_test.py b/cliport/environments/environment_test.py new file mode 100644 index 0000000000000000000000000000000000000000..23f194905087414d1015e72d72734d5d587de9fd --- /dev/null +++ b/cliport/environments/environment_test.py @@ -0,0 +1,31 @@ +"""Tests for dvnets.environments.environment.""" + +from absl.testing import absltest + +from cliport import tasks +from cliport.environments import environment + +ASSETS_PATH = 'dvnets/environments/assets/' + + +class EnvironmentTest(absltest.TestCase): + + def test_environment_action(self): + env = environment.Environment(ASSETS_PATH) + task = tasks.BlockInsertion() + env.set_task(task) + env.seed(0) + agent = task.oracle(env) + obs = env.reset() + info = None + done = False + for _ in range(10): + act = agent.act(obs, info) + self.assertTrue(env.action_space.contains(act)) + obs, _, done, info = env.step(act) + if done: + break + + +if __name__ == '__main__': + absltest.main() diff --git a/cliport/eval.py b/cliport/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..6c01c06f2e8ede349d10a2d2c659e64b8383fef0 --- /dev/null +++ b/cliport/eval.py @@ -0,0 +1,231 @@ +"""Ravens main training script.""" + +import os +import pickle +import json + +import numpy as np +import hydra +from cliport import agents +from cliport import dataset +from cliport import tasks +from cliport.utils import utils +from cliport.environments.environment import Environment +from torch.utils.data import DataLoader + + +@hydra.main(config_path='./cfg', config_name='eval', version_base="1.2") +def main(vcfg): + # Load train cfg + tcfg = utils.load_hydra_config(vcfg['train_config']) + + # Initialize environment and task. + env = Environment( + vcfg['assets_root'], + disp=vcfg['disp'], + shared_memory=vcfg['shared_memory'], + hz=480, + record_cfg=vcfg['record'] + ) + + # Choose eval mode and task. + mode = vcfg['mode'] + eval_task = vcfg['eval_task'] + print("eval_task!!!", eval_task) + + if mode not in {'train', 'val', 'test'}: + raise Exception("Invalid mode. Valid options: train, val, test") + + # Load eval dataset. + dataset_type = vcfg['type'] + if 'multi' in dataset_type: + ds = dataset.RavensMultiTaskDataset(vcfg['data_dir'], + tcfg, + group=eval_task, + mode=mode, + n_demos=vcfg['n_demos'], + augment=False) + else: + ds = dataset.RavensDataset(os.path.join(vcfg['data_dir'], f"{eval_task}-{mode}"), + tcfg, + n_demos=vcfg['n_demos'], + augment=False) + + all_results = {} + name = '{}-{}-n{}'.format(eval_task, vcfg['agent'], vcfg['n_demos']) + + # Save path for results. + json_name = f"multi-results-{mode}.json" if 'multi' in vcfg['model_path'] else f"results-{mode}.json" + save_path = vcfg['save_path'] + print(f"Save path for results: {save_path}") + if not os.path.exists(save_path): + os.makedirs(save_path) + save_json = os.path.join(save_path, f'{name}-{json_name}') + + # Load existing results. + existing_results = {} + if os.path.exists(save_json): + with open(save_json, 'r') as f: + existing_results = json.load(f) + + # Make a list of checkpoints to eval. + ckpts_to_eval = list_ckpts_to_eval(vcfg, existing_results) + data_loader = DataLoader(ds, shuffle=False, + pin_memory=False, + num_workers=1 ) + + # Evaluation loop + print(f"Evaluating: {str(ckpts_to_eval)}") + for ckpt in ckpts_to_eval: + model_file = os.path.join(vcfg['model_path'], ckpt) + + if not os.path.exists(model_file) or not os.path.isfile(model_file): + print(f"Checkpoint not found: {model_file}") + continue + elif not vcfg['update_results'] and ckpt in existing_results: + print(f"Skipping because of existing results for {model_file}.") + continue + + results = [] + mean_reward = 0.0 + + # Run testing for each training run. + for train_run in range(vcfg['n_repeats']): + + # Initialize agent. + utils.set_seed(train_run, torch=True) + agent = agents.names[vcfg['agent']](name, tcfg, data_loader, data_loader) + + # Load checkpoint + agent.load(model_file) + print(f"Loaded: {model_file}") + + record = vcfg['record']['save_video'] + n_demos = vcfg['n_demos'] + + # Run testing and save total rewards with last transition info. + for i in range(0, n_demos): + print(f'Test: {i + 1}/{n_demos}') + try: + episode, seed = ds.load(i) + except: + print(f"skip bad example {i}") + continue + goal = episode[-1] + total_reward = 0 + np.random.seed(seed) + + # set task + if 'multi' in dataset_type: + task_name = ds.get_curr_task() + task = tasks.names[task_name]() + print(f'Evaluating on {task_name}') + else: + task_name = vcfg['eval_task'] + task = tasks.names[task_name]() + + task.mode = mode + env.seed(seed) + env.set_task(task) + obs = env.reset() + info = env.info + reward = 0 + + # Start recording video (NOTE: super slow) + if record: + video_name = f'{task_name}-{i+1:06d}' + if 'multi' in vcfg['model_task']: + video_name = f"{vcfg['model_task']}-{video_name}" + env.start_rec(video_name) + + for _ in range(task.max_steps): + act = agent.act(obs, info, goal) + lang_goal = info['lang_goal'] + + # print(f'Lang Goal: {lang_goal}') + obs, reward, done, info = env.step(act) + total_reward += reward + # print(f'Total Reward: {total_reward:.3f} | Done: {done}\n') + if done: + break + + results.append((total_reward, info)) + mean_reward = np.mean([r for r, i in results]) + print(f'Mean: {mean_reward} | Task: {task_name} | Ckpt: {ckpt}') + + # End recording video + if record: + env.end_rec() + + all_results[ckpt] = { + 'episodes': results, + 'mean_reward': mean_reward, + } + + # Save results in a json file. + if vcfg['save_results']: + print("save results to:", save_json) + # Load existing results + if os.path.exists(save_json): + with open(save_json, 'r') as f: + existing_results = json.load(f) + existing_results.update(all_results) + all_results = existing_results + + with open(save_json, 'w') as f: + json.dump(all_results, f, indent=4) + + +def list_ckpts_to_eval(vcfg, existing_results): + ckpts_to_eval = [] + + # Just the last.ckpt + if vcfg['checkpoint_type'] == 'last': + last_ckpt = 'last.ckpt' + ckpts_to_eval.append(last_ckpt) + + # Validation checkpoints that haven't been already evaluated. + elif vcfg['checkpoint_type'] == 'val_missing': + checkpoints = sorted([c for c in os.listdir(vcfg['model_path']) if "steps=" in c]) + ckpts_to_eval = [c for c in checkpoints if c not in existing_results] + + # Find the best checkpoint from validation and run eval on the test set. + elif vcfg['checkpoint_type'] == 'test_best': + result_jsons = [c for c in os.listdir(vcfg['results_path']) if "results-val" in c] + if 'multi' in vcfg['model_task']: + result_jsons = [r for r in result_jsons if "multi" in r] + else: + result_jsons = [r for r in result_jsons if "multi" not in r] + + if len(result_jsons) > 0: + result_json = result_jsons[0] + with open(os.path.join(vcfg['results_path'], result_json), 'r') as f: + eval_res = json.load(f) + best_checkpoint = 'last.ckpt' + best_success = -1.0 + for ckpt, res in eval_res.items(): + if res['mean_reward'] > best_success: + best_checkpoint = ckpt + best_success = res['mean_reward'] + print(best_checkpoint) + ckpt = best_checkpoint + ckpts_to_eval.append(ckpt) + else: + print("No best val ckpt found. Using last.ckpt") + ckpt = 'last.ckpt' + ckpts_to_eval.append(ckpt) + + # Load a specific checkpoint with a substring e.g: 'steps=10000' + else: + print(f"Looking for: {vcfg['checkpoint_type']}") + checkpoints = [c for c in os.listdir(vcfg['model_path']) if vcfg['checkpoint_type'] in c] + checkpoint = checkpoints[0] if len(checkpoints) > 0 else "" + ckpt = checkpoint + ckpts_to_eval.append(ckpt) + + print("ckpts_to_eval:", ckpts_to_eval) + return ckpts_to_eval + + +if __name__ == '__main__': + main() diff --git a/cliport/generated_tasks/Four_corner_pyramid_challenge.py b/cliport/generated_tasks/Four_corner_pyramid_challenge.py new file mode 100644 index 0000000000000000000000000000000000000000..6997d4a30cb6c985518752e92fb9452b11fa4708 --- /dev/null +++ b/cliport/generated_tasks/Four_corner_pyramid_challenge.py @@ -0,0 +1,61 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class FourCornerPyramidChallenge(Task): + """Construct a pyramid of blocks in each zone with a specific color sequence.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "build a pyramid of blocks in each zone with the sequence red, blue, green, and yellow from bottom to top" + self.task_completed_desc = "done building pyramids." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for _ in range(4): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + for _ in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(zone_pose, i), zone_pose[1]) for i in place_pos for zone_pose in zone_poses] + + # Goal: blocks are stacked in a pyramid in each zone. + for i in range(4): + self.add_goal(objs=blocks[i*4:(i+1)*4], matches=np.ones((4, 4)), targ_poses=targs[i*4:(i+1)*4], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*4, + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/__init__.py b/cliport/generated_tasks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..85478fc1c1413e7f737e6a636563290bf32cc327 --- /dev/null +++ b/cliport/generated_tasks/__init__.py @@ -0,0 +1,19 @@ +import os +from pprint import pprint + +# automatically import all defined task classes in this directory +new_names = {} +dir_path = os.path.dirname(os.path.realpath(__file__)) +for file in os.listdir(dir_path): + if 'init' not in file and 'cache' not in file: + code_file = open(f"{dir_path}/{file}").read() + code_lines = code_file.split("\n") + class_def = [line for line in code_lines if line.startswith('class')] + task_name = class_def[0] + task_name = task_name[task_name.find("class "): task_name.rfind("(Task)")][6:] + file_name = file.replace('.py','') + exec(f"from cliport.generated_tasks.{file_name} import {task_name}") + new_names[file_name.replace("_", "-")] = eval(task_name) + + +# pprint(new_names) diff --git a/cliport/generated_tasks/align_balls_in_colored_boxes.py b/cliport/generated_tasks/align_balls_in_colored_boxes.py new file mode 100644 index 0000000000000000000000000000000000000000..492c8e902c18b5718e2df1647f117f65a4fa565c --- /dev/null +++ b/cliport/generated_tasks/align_balls_in_colored_boxes.py @@ -0,0 +1,54 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class AlignBallsInColoredBoxes(Task): + """Align balls in colored boxes according to the color and sequence.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "put the {color} ball in the {color} box" + self.task_completed_desc = "done aligning balls in boxes." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their sequence + colors = ['red', 'blue', 'green', 'yellow'] + + # Add boxes. + box_size = (0.12, 0.12, 0.12) + box_urdf = 'box/box-template.urdf' + box_poses = [] + boxes = [] + for i in range(4): + box_pose = self.get_random_pose(env, box_size) + box_id = env.add_object(box_urdf, box_pose, color=utils.COLORS[colors[i]]) + boxes.append(box_id) + box_poses.append(box_pose) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball.urdf' + balls = [] + for i in range(4): + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[colors[i]]) + balls.append(ball_id) + + # Goal: each ball is in the box of the same color. + for i in range(4): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[box_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/align_balls_in_colored_zones.py b/cliport/generated_tasks/align_balls_in_colored_zones.py new file mode 100644 index 0000000000000000000000000000000000000000..670de13f0fab453df145e818608a794e0603ca28 --- /dev/null +++ b/cliport/generated_tasks/align_balls_in_colored_zones.py @@ -0,0 +1,54 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class AlignBallsInColoredZones(Task): + """Align balls of different colors in correspondingly colored zones.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the {color} ball in the {color} zone" + self.task_completed_desc = "done aligning balls in colored zones." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for balls and zones + colors = ['red', 'blue', 'green', 'yellow', 'orange', 'purple'] + color_names = ['Red', 'Blue', 'Green', 'Yellow', 'Orange', 'Purple'] + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for i in range(6): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[colors[i]]) + zone_poses.append(zone_pose) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for i in range(6): + ball_pose = self.get_random_pose(env, ball_size) + replace = {'DIM': ball_size, 'HALF': (ball_size[0] / 2, ball_size[1] / 2, ball_size[2] / 2), 'COLOR': colors[i]} + ball_urdf = self.fill_template(ball_urdf, replace) + ball_id = env.add_object(ball_urdf, ball_pose) + balls.append(ball_id) + + # Goal: each ball is in a different colored zone. + for i in range(6): + self.add_goal(objs=[balls[i]], matches=np.int32([[1]]), targ_poses=[zone_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, + language_goal=self.lang_template.format(color=color_names[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/align_cylinders_in_zones.py b/cliport/generated_tasks/align_cylinders_in_zones.py new file mode 100644 index 0000000000000000000000000000000000000000..d6d0c9b2ee1a314a3af57c24a6095d7705c4931f --- /dev/null +++ b/cliport/generated_tasks/align_cylinders_in_zones.py @@ -0,0 +1,67 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class AlignCylindersInZones(Task): + """Place four differently colored cylinders each into a matching colored zone. + The zones are surrounded by small blocks, which the robot needs to move out of the way + without knocking them out of their respective zones.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the {color} cylinder in the {color} zone" + self.task_completed_desc = "done aligning cylinders." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Cylinder colors. + colors = ['red', 'blue', 'green', 'yellow'] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=utils.COLORS[colors[i]]) + cylinders.append(cylinder_id) + + # Add zones. + # x, y, z dimensions for the asset size + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + + zones = [] + for i in range(4): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, color=utils.COLORS[colors[i]], category='fixed') + zones.append(zone_pose) + + # Add small blocks around the zones. + # x, y, z dimensions for the asset size + block_size = (0.02, 0.02, 0.02) + block_urdf = 'block/small.urdf' + + for _ in range(16): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose) + + # Goal: each cylinder is in a matching colored zone. + for i in range(4): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[zones[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/align_pair_colored_blocks_along_line.py b/cliport/generated_tasks/align_pair_colored_blocks_along_line.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b41a6351110bb7bfaad95093f1aa46ed146778 --- /dev/null +++ b/cliport/generated_tasks/align_pair_colored_blocks_along_line.py @@ -0,0 +1,47 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class AlignPairColoredBlocksAlongLine(Task): + """Align two pairs of blocks, each pair painted a different color (red and blue), along a marked line on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "Place two pairs of blocks, each pair painted a different color (red and blue), along a marked line on the tabletop." + self.task_completed_desc = "done aligning blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add line. + line_size = (0.3, 0.01, 0.01) + line_pose = self.get_random_pose(env, line_size) + line_template = 'line/line-template.urdf' + replace = {'DIM': line_size} + line_urdf = self.fill_template(line_template, replace) + env.add_object(line_urdf, line_pose, 'fixed') + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_template = 'block/block-template.urdf' + colors = [utils.COLORS['red'], utils.COLORS['blue']] + blocks = [] + anchor_base_poses = [(utils.apply(line_pose, (0.04, 0, 0.001)), line_pose[1]), + (utils.apply(line_pose, (0.04 * 2, 0, 0.001)), line_pose[1]), + (utils.apply(line_pose, (-0.04, 0, 0.041)), line_pose[1]), + (utils.apply(line_pose, (-0.04 * 2, 0, 0.041)), line_pose[1])] + + for color in colors: + for _ in range(2): + block_pose = self.get_random_pose(env, block_size) + replace = {'DIM': block_size} + block_urdf = self.fill_template(block_template, replace) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Goal: each pair of similarly colored blocks are touching and both pairs are aligned along the line. + self.add_goal(objs=blocks, matches=np.ones((4, 4)), targ_poses=anchor_base_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/align_rope_along_line.py b/cliport/generated_tasks/align_rope_along_line.py new file mode 100644 index 0000000000000000000000000000000000000000..bab4c7a326cc97c490e5b4cfba2531cd2823e967 --- /dev/null +++ b/cliport/generated_tasks/align_rope_along_line.py @@ -0,0 +1,38 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula + +class AlignRopeAlongLine(Task): + """Align a deformable rope along a straight line marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "align the rope along the line" + self.task_completed_desc = "done aligning." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add line. + length = np.random.uniform(0.18, 0.25) + line_size = (length, 0.01, 0.01) + line_pose = self.get_random_pose(env, line_size) + line_template = 'line/line-template.urdf' + replace = {'DIM': line_size, 'HALF': (line_size[0] / 2, line_size[1] / 2, line_size[2] / 2)} + line_urdf = self.fill_template(line_template, replace) + env.add_object(line_urdf, line_pose, 'fixed') + + # Add rope. + rope_size = (length, 0.01, 0.01) + rope_pose = self.get_random_pose(env, rope_size) + corner1_pose = utils.apply(line_pose, (length / 2, 0.01, 0.01)) + corner2_pose = utils.apply(line_pose, (-length / 2, 0.01, 0.01)) + rope_id, targets, matches = self.make_rope(env, (corner1_pose, corner2_pose), n_parts=15) + + # Goal: rope is aligned with the line. + self.add_goal(objs=rope_id, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) diff --git a/cliport/generated_tasks/align_rope_cross_zone.py b/cliport/generated_tasks/align_rope_cross_zone.py new file mode 100644 index 0000000000000000000000000000000000000000..32edbe32ec8624fa7076da67a16df20a985237e9 --- /dev/null +++ b/cliport/generated_tasks/align_rope_cross_zone.py @@ -0,0 +1,36 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula + +class AlignRopeCrossZone(Task): + """Align a deformable rope across the diagonal of a zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "align the rope across the diagonal of a zone" + self.task_completed_desc = "done aligning." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zone. + length = 0.12 + zone_size = (length, length, 0.01) + zone_pose = self.get_random_pose(env, zone_size) + zone_urdf = 'zone/zone.urdf' + env.add_object(zone_urdf, zone_pose, 'fixed') + + # Add rope. + rope_size = (length, 0.01, 0.01) + rope_pose = self.get_random_pose(env, rope_size) + corner1_pose = utils.apply(zone_pose, (length / 2, length / 2, 0.01)) + corner2_pose = utils.apply(zone_pose, (-length / 2, -length / 2, 0.01)) + rope_id, targets, matches = self.make_rope(env, (corner1_pose, corner2_pose), n_parts=10) + + # Goal: rope is aligned with the diagonal of the zone. + self.add_goal(objs=rope_id, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) diff --git a/cliport/generated_tasks/align_spheres_in_colored_zones.py b/cliport/generated_tasks/align_spheres_in_colored_zones.py new file mode 100644 index 0000000000000000000000000000000000000000..b1a78e12855ebf28e6d25f2455a08e6822bc3dc8 --- /dev/null +++ b/cliport/generated_tasks/align_spheres_in_colored_zones.py @@ -0,0 +1,54 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class AlignSpheresInColoredZones(Task): + """Align spheres of different colors in the matching colored zones.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the {color} sphere in the {color} zone" + self.task_completed_desc = "done aligning spheres in colored zones." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors + colors = ['red', 'blue', 'green', 'yellow'] + color_names = ['red', 'blue', 'green', 'yellow'] + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for color in colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add spheres. + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere-template.urdf' + spheres = [] + for i, color in enumerate(colors): + sphere_pose = self.get_random_pose(env, sphere_size) + replace = {'DIM': sphere_size, 'HALF': (sphere_size[0] / 2, sphere_size[1] / 2, sphere_size[2] / 2)} + sphere_urdf = self.fill_template(sphere_urdf, replace) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS[color]) + spheres.append(sphere_id) + + # Add goal + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[zone_poses[i]], replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1, + language_goal=self.lang_template.format(color=color_names[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/assemble_single_car.py b/cliport/generated_tasks/assemble_single_car.py new file mode 100644 index 0000000000000000000000000000000000000000..bdd1acc19165bc57ded91d45715e0fbba35f59b4 --- /dev/null +++ b/cliport/generated_tasks/assemble_single_car.py @@ -0,0 +1,61 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class AssembleSingleCar(Task): + """Assemble a mini car using a large blue box as the body, a smaller red box on top as the roof, and two tiny green boxes on the sides as wheels.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "build a mini car using a large blue box as the body, a smaller red box on top as the roof, and two tiny green boxes on the sides as wheels" + self.task_completed_desc = "done assembling the car." + + def reset(self, env): + super().reset(env) + + # Add car body (large blue box). + body_size = (0.1, 0.05, 0.02) # x, y, z dimensions + body_pose = self.get_random_pose(env, body_size) + body_urdf = 'box/box-template.urdf' + body_color = utils.COLORS['blue'] + body_id = env.add_object(body_urdf, body_pose, color=body_color) + + # Add car roof (smaller red box). + roof_size = (0.08, 0.04, 0.02) # x, y, z dimensions + roof_pose = self.get_random_pose(env, roof_size) + roof_urdf = 'box/box-template.urdf' + roof_color = utils.COLORS['red'] + roof_id = env.add_object(roof_urdf, roof_pose, color=roof_color) + + # Add car wheels (two tiny green boxes). + wheel_size = (0.02, 0.02, 0.01) # x, y, z dimensions + wheel_urdf = 'box/box-template.urdf' + wheel_color = utils.COLORS['green'] + wheel_ids = [] + + for _ in range(2): + wheel_pose = self.get_random_pose(env, wheel_size) + wheel_id = env.add_object(wheel_urdf, wheel_pose, color=wheel_color) + wheel_ids.append(wheel_id) + + # Goal: assemble the car by placing the roof on the body and the wheels on the sides. + # The target poses are calculated based on the body pose. + roof_targ_pose = (body_pose[0] + np.array([0, 0, body_size[2] + roof_size[2]/2]), body_pose[1]) + wheel_targ_poses = [(body_pose[0] + np.array([0, body_size[1]/2 + wheel_size[1]/2, -body_size[2]/2]), body_pose[1]), + (body_pose[0] + np.array([0, -body_size[1]/2 - wheel_size[1]/2, -body_size[2]/2]), body_pose[1])] + + # Add the goals. + self.add_goal(objs=[roof_id], matches=np.ones((1, 1)), targ_poses=[roof_targ_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/3, language_goal=self.lang_template) + + self.add_goal(objs=wheel_ids, matches=np.ones((2, 2)), targ_poses=wheel_targ_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=2/3, language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/ball_in_bowl_obstacle_course.py b/cliport/generated_tasks/ball_in_bowl_obstacle_course.py new file mode 100644 index 0000000000000000000000000000000000000000..2349767e3e8e7682c198175b949be9a3e8762ca8 --- /dev/null +++ b/cliport/generated_tasks/ball_in_bowl_obstacle_course.py @@ -0,0 +1,57 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class BallInBowlObstacleCourse(Task): + """Navigate through a maze of blocks, pick up balls of different colors and place them in the corresponding colored bowls.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "navigate through the maze and place the {color} ball in the {color} bowl" + self.task_completed_desc = "done placing balls in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks to form a maze. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/small.urdf' + for _ in range(10): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose, category='fixed') + + # Add balls of different colors. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + colors = ['red', 'blue', 'green', 'yellow'] + balls = [] + for color in colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add bowls of different colors at different corners of the maze. + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowls = [] + for color in colors: + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_id = env.add_object(bowl_urdf, bowl_pose, color=color, category='fixed') + bowls.append(bowl_id) + + # Goal: each ball is in the bowl of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(bowls[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/ball_in_bowl_obstacle_course_new.py b/cliport/generated_tasks/ball_in_bowl_obstacle_course_new.py new file mode 100644 index 0000000000000000000000000000000000000000..a2ccaecbe2f6d3d1381f9a89c2610bcf38c9132b --- /dev/null +++ b/cliport/generated_tasks/ball_in_bowl_obstacle_course_new.py @@ -0,0 +1,57 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class BallInBowlObstacleCourseNew(Task): + """Navigate through a maze of blocks, pick up balls of different colors and place them in the corresponding colored bowls.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "put the {color} ball in the {color} bowl" + self.task_completed_desc = "done placing balls in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks to form a maze. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/small.urdf' + for _ in range(10): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose, category='fixed') + + # Add balls of different colors. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + colors = ['red', 'blue', 'green', 'yellow'] + balls = [] + for color in colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add bowls of different colors at different corners of the maze. + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowls = [] + for color in colors: + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_id = env.add_object(bowl_urdf, bowl_pose, color=color, category='fixed') + bowls.append(bowl_id) + + # Goal: each ball is in the bowl of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(bowls[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/ball_on_box_on_container.py b/cliport/generated_tasks/ball_on_box_on_container.py new file mode 100644 index 0000000000000000000000000000000000000000..b78da3ab0a4399a188aba8c00439d85c41f45915 --- /dev/null +++ b/cliport/generated_tasks/ball_on_box_on_container.py @@ -0,0 +1,61 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BallOnBoxOnContainer(Task): + """Pick up each ball and place it on the corresponding colored box, which are located in specific positions on a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "place the {} box on the container" + self.lang_template_2 = "place the {} ball on the {} box" + + self.task_completed_desc = "done placing balls on boxs and box on container." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + container_size = (0.2, 0.2, 0.06) + container_pose = self.get_random_pose(env, container_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_template, replace) + + env.add_object(container_urdf, container_pose, 'fixed') + + # Define colors. + ball_colors = ['red'] + box_colors = ['blue'] + + # Add boxs. + box_size = (0.04, 0.04, 0.06) + box_template = 'box/box-template.urdf' + boxs = [] + + + replace = {'DIM': box_size, 'HALF': (box_size[0] / 2, box_size[1] / 2, box_size[2] / 2), 'COLOR': ball_colors[0]} + box_urdf = self.fill_template(box_template, replace) + box_pose = self.get_random_pose(env, box_size) + box_id = env.add_object(box_urdf, box_pose) + boxs.append(box_id) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball.urdf' + balls = [] + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=box_colors[0]) + balls.append(ball_id) + + # Goal: place the box on top of the container + self.add_goal(objs=[boxs[0]], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=self.lang_template.format(box_colors[0])) + + + # Goal: place the ball on top of the box + language_goal = self.lang_template_2.format(ball_colors[0], box_colors[0]) + self.add_goal(objs=[balls[0]], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=language_goal) diff --git a/cliport/generated_tasks/ball_sorting_with_blocks_barrier.py b/cliport/generated_tasks/ball_sorting_with_blocks_barrier.py new file mode 100644 index 0000000000000000000000000000000000000000..b1e7ea17e2252bbebe87f49b1ba1327f8b698406 --- /dev/null +++ b/cliport/generated_tasks/ball_sorting_with_blocks_barrier.py @@ -0,0 +1,62 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class BallSortingWithBlocksBarrier(Task): + """Pick up each ball and place it into the zone of the same color, but without knocking over the blocks.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the {color} ball in the {color} zone without knocking over the blocks" + self.task_completed_desc = "done sorting balls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for the balls and zones + colors = ['red', 'blue', 'green', 'yellow'] + + # Add zones and blocks. + zone_size = (0.12, 0.12, 0) + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/small.urdf' + zone_urdf = 'zone/zone.urdf' + zones = [] + blocks = [] + for color in colors: + # Add zone of specific color + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zones.append(zone_pose) + + # Add line of blocks of the same color + for _ in range(5): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for color in colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[color]) + balls.append(ball_id) + + # Goal: each ball is in a zone of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[zones[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/block_on_cylinder_on_pallet.py b/cliport/generated_tasks/block_on_cylinder_on_pallet.py new file mode 100644 index 0000000000000000000000000000000000000000..d29f6d6de4c60bd0e6a5a30261ea09bc6ed05b9d --- /dev/null +++ b/cliport/generated_tasks/block_on_cylinder_on_pallet.py @@ -0,0 +1,58 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BlockOnCylinderOnPallet(Task): + """Pick up each block and place it on the corresponding colored cylinder, which are located in specific positions on a pallet.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "place the {} cylinder on the pallet" + self.lang_template_2 = "place the {} block on the {} cylinder" + + self.task_completed_desc = "done placing blocks on cylinders and cylinder on pallet." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.35, 0.35, 0.01) + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_urdf = 'pallet/pallet.urdf' + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Define colors. + block_colors = ['red'] + cylinder_colors = ['blue'] + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.06) + cylinder_template = 'cylinder/cylinder-template.urdf' + cylinders = [] + + + replace = {'DIM': cylinder_size, 'HALF': (cylinder_size[0] / 2, cylinder_size[1] / 2, cylinder_size[2] / 2), 'COLOR': block_colors[0]} + cylinder_urdf = self.fill_template(cylinder_template, replace) + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose) + cylinders.append(cylinder_id) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=cylinder_colors[0]) + blocks.append(block_id) + + # Goal: place the cylinder on top of the pallet + self.add_goal(objs=[cylinders[0]], matches=np.ones((1, 1)), targ_poses=[pallet_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=self.lang_template.format(cylinder_colors[0])) + + + # Goal: place the block on top of the cylinder + language_goal = self.lang_template_2.format(block_colors[0], cylinder_colors[0]) + self.add_goal(objs=[blocks[0]], matches=np.ones((1, 1)), targ_poses=[pallet_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=language_goal) diff --git a/cliport/generated_tasks/block_pyramid_with_limited_space.py b/cliport/generated_tasks/block_pyramid_with_limited_space.py new file mode 100644 index 0000000000000000000000000000000000000000..2e60d7b1b24d3fc628e6292fcef52d035fdbf17f --- /dev/null +++ b/cliport/generated_tasks/block_pyramid_with_limited_space.py @@ -0,0 +1,60 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BlockPyramidWithLimitedSpace(Task): + """Sort blocks according to color into three zones on the tabletop and construct a pyramid in each zone.""" + + def __init__(self): + super().__init__() + self.max_steps = 50 + self.lang_template = "sort the blocks according to color into three zones and construct a pyramid in each zone" + self.task_completed_desc = "done sorting and constructing pyramids." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for _ in range(3): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['yellow'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for color in colors: + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(zone_pose, i), zone_pose[1]) for zone_pose in zone_poses for i in place_pos] + + # Goal: blocks are sorted and stacked in a pyramid in each zone. + for i in range(3): + self.add_goal(objs=blocks[i*3:(i+1)*3], matches=np.ones((3, 3)), targ_poses=targs[i*3:(i+1)*3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/build_bridge.py b/cliport/generated_tasks/build_bridge.py new file mode 100644 index 0000000000000000000000000000000000000000..e7153210fa5682badfbf73836823169411fb87fc --- /dev/null +++ b/cliport/generated_tasks/build_bridge.py @@ -0,0 +1,81 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildBridge(Task): + """Construct a bridge using two yellow blocks and three blue blocks. + Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between. + Then, place the blue block horizontally on top of the yellow blocks.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "build a bridge using four yellow blocks and one long blue block" + self.task_completed_desc = "done building bridge." + + def reset(self, env): + super().reset(env) + + # Add yellow blocks. + base_length = 0.04 + base_size = (base_length, base_length, base_length) + base_block_urdf = "box/box-template.urdf" + bridge_pose = ((0.5, 0.0, 0.0), (0, 0, 0, 1)) # fixed pose + self.add_corner_anchor_for_pose(env, bridge_pose) + + base_block_urdf = self.fill_template(base_block_urdf, {'DIM': base_size}) + anchor_base_poses = [(utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.041)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.041)), bridge_pose[1])] + base_blocks = [] + + for idx in range(4): + base_block_pose = self.get_random_pose(env, base_size) + base_block_id = env.add_object(base_block_urdf, base_block_pose, color=utils.COLORS['yellow']) + base_blocks.append(base_block_id) + + # Add car body block. + body_size = (0.12, 0.04, 0.02) # x, y, z dimensions for the asset size + body_block_urdf = "box/box-template.urdf" + body_block_urdf = self.fill_template(body_block_urdf, {'DIM': body_size}) + body_block_pose = self.get_random_pose(env, body_size) + body_block_id = env.add_object(body_block_urdf, body_block_pose, color=utils.COLORS['blue']) + anchor_body_poses = [bridge_pose] + + # Goal: Firstly, create the base of the car by positioning two red blocks side by side. + self.add_goal(objs=base_blocks[:2], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal="Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between.") + + self.add_goal(objs=base_blocks[2:], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./2, + language_goal="Place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between.") + + # Then, add the car body by stacking a blue block on top of the base. + self.add_goal(objs=[body_block_id], + matches=np.ones((1, 1)), + targ_poses=anchor_body_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal="Then, place the blue block horizontally on top of the yellow blocks.") \ No newline at end of file diff --git a/cliport/generated_tasks/build_car.py b/cliport/generated_tasks/build_car.py new file mode 100644 index 0000000000000000000000000000000000000000..cf8ae73ce67ed67b69bdf716244ae6b2a7ade004 --- /dev/null +++ b/cliport/generated_tasks/build_car.py @@ -0,0 +1,94 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildCar(Task): + """Construct a simple car structure using blocks and cylinders.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "Construct a simple car structure using blocks and cylinders. " \ + "Firstly, create the base of the car by positioning two red blocks side by side. " \ + "Then, add the car body by stacking a blue block on top of the base. " \ + "For the wheels, place a black cylinder on each side of the base blocks." + self.task_completed_desc = "done building car." + self.additional_reset() + + def reset(self, env): + super().reset(env) + car_pose = ((0.5, 0.0, 0.0), (0,0,0,1)) # fixed pose + base_length = 0.04 + self.add_corner_anchor_for_pose(env, car_pose) + + # Add base blocks. Use box template so that we can change its size. + base_size = (0.02, 0.04, 0.02) + base_block_urdf = "box/box-template.urdf" + base_block_urdf = self.fill_template(base_block_urdf, {'DIM': base_size}) + anchor_base_poses = [(utils.apply(car_pose, (base_length / 2, base_length / 2, 0.001)), car_pose[1]), + (utils.apply(car_pose, (-base_length / 2, base_length / 2, 0.001)), car_pose[1])] + base_blocks = [] + + for idx in range(2): + base_block_pose = self.get_random_pose(env, base_size) + base_block_id = env.add_object(base_block_urdf, base_block_pose, color=utils.COLORS['red']) + base_blocks.append(base_block_id) + + # Add car body block. + body_size = (0.04, 0.02, 0.02) # x, y, z dimensions for the asset size + body_block_urdf = "box/box-template.urdf" + body_block_urdf = self.fill_template(body_block_urdf, {'DIM': body_size}) + body_block_pose = self.get_random_pose(env, body_size) + body_block_id = env.add_object(body_block_urdf, body_block_pose, color=utils.COLORS['blue']) + anchor_body_poses = [car_pose] + + wheel_length = 0.12 + anchor_wheel_poses = [(utils.apply(car_pose, ( wheel_length / 2, wheel_length / 2, 0.001)), car_pose[1]), + (utils.apply(car_pose, (-wheel_length / 2, wheel_length / 2, 0.001)), car_pose[1]), + (utils.apply(car_pose, ( wheel_length / 2, -wheel_length / 2, 0.001)), car_pose[1]), + (utils.apply(car_pose, (-wheel_length / 2, -wheel_length / 2, 0.001)), car_pose[1])] + + # Add wheels. + wheel_size = (0.02, 0.02, 0.02) # x, y, z dimensions for the asset size + wheel_urdf = 'cylinder/cylinder-template.urdf' + wheel_urdf = self.fill_template(wheel_urdf, {'DIM': wheel_size}) + + wheels = [] + for idx in range(4): + wheel_pose = self.get_random_pose(env, wheel_size) + wheel_id = env.add_object(wheel_urdf, wheel_pose, color=utils.COLORS['black']) + wheels.append(wheel_id) + + # Goal: Firstly, create the base of the car by positioning two red blocks side by side. + self.add_goal(objs=base_blocks, + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./3, + language_goal="Firstly, create the base of the car by positioning two red blocks side by side.") + + # Then, add the car body by stacking a blue block on top of the base. + self.add_goal(objs=[body_block_id], + matches=np.ones((1, 1)), + targ_poses=anchor_body_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./3, + language_goal="Then, add the car body by stacking a blue block on top of the base.") + + # For the wheels, place a black cylinder on each side of the base blocks. + self.add_goal(objs=wheels, + matches=np.ones((4, 4)), + targ_poses=anchor_wheel_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./3, + language_goal="For the wheels, place a black cylinder on each side of the base blocks.") + diff --git a/cliport/generated_tasks/build_cylinder_structure.py b/cliport/generated_tasks/build_cylinder_structure.py new file mode 100644 index 0000000000000000000000000000000000000000..8e454f45f88dd8b7d331da4d047aec971efb63a1 --- /dev/null +++ b/cliport/generated_tasks/build_cylinder_structure.py @@ -0,0 +1,67 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildCylinderStructure(Task): + """Construct a structure using four colored cylinders (red, blue, green, yellow) on a square base.""" + + def __init__(self): + super().__init__() + self.max_steps = 5 + self.lang_template = "construct a structure using four colored cylinders on a square base" + self.task_completed_desc = "done building the cylinder structure." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add square base. + # x, y, z dimensions for the asset size + base_size = (0.15, 0.15, 0.005) + base_urdf = 'square/square-template.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Cylinder colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow'] + ] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.08) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + + objs = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=colors[i]) + objs.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.04), (0, 0.05, 0.04), + (0, 0.05, 0.12), (0, -0.05, 0.12)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: red and blue cylinders are placed side by side on the base. + self.add_goal(objs=objs[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, + language_goal="place the red and blue cylinders side by side on the base") + + # Goal: green cylinder is placed on top of the blue cylinder. + self.add_goal(objs=[objs[2]], matches=np.ones((1, 1)), targ_poses=[targs[2]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2], + language_goal="place the green cylinder on top of the blue cylinder") + + # Goal: yellow cylinder is placed on top of the red cylinder. + self.add_goal(objs=[objs[3]], matches=np.ones((1, 1)), targ_poses=[targs[3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2], + language_goal="place the yellow cylinder on top of the red cylinder") \ No newline at end of file diff --git a/cliport/generated_tasks/build_house.py b/cliport/generated_tasks/build_house.py new file mode 100644 index 0000000000000000000000000000000000000000..794564c9c277d8668a6bea7cb3478821d94de897 --- /dev/null +++ b/cliport/generated_tasks/build_house.py @@ -0,0 +1,84 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildHouse(Task): + """Construct a house structure using blocks and a cylinder.""" + + def __init__(self): + super().__init__() + self.max_steps = 30 + self.lang_template = "Construct a house structure using blocks and a cylinder. Begin by forming the base of the house with four red blocks arranged in a square shape. Then build the walls by stacking two blue blocks on top of each base block. Create a roof by placing two yellow blocks on the uppermost blue blocks, angled to form an apex. Finally, position a green cylinder in the center of the square created by the base blocks to represent a chimney." + self.task_completed_desc = "done building house." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks for the base. + base_blocks = [] + block_size = (0.04, 0.04, 0.04) # x, y, z dimensions for the block size + block_urdf = 'box/box-template.urdf' + for _ in range(4): + block_pose = self.get_random_pose(env, block_size) + base_block_urdf = self.fill_template(block_urdf, {'DIM': (0.06, 0.06, 0.04)}) + + block_id = env.add_object(base_block_urdf, block_pose, color=utils.COLORS['red']) + base_blocks.append(block_id) + + # Add blocks for the walls. + wall_blocks = [] + for _ in range(4): + block_pose = self.get_random_pose(env, block_size) + wall_block_urdf = self.fill_template(block_urdf, {'DIM': (0.04, 0.04, 0.04)}) + + block_id = env.add_object(wall_block_urdf, block_pose, color=utils.COLORS['blue']) + wall_blocks.append(block_id) + + # Add blocks for the roof. + roof_blocks = [] + for _ in range(2): + block_pose = self.get_random_pose(env, block_size) + roof_block_urdf = self.fill_template(block_urdf, {'DIM': (0.04, 0.1, 0.04)}) + + block_id = env.add_object(roof_block_urdf, block_pose, color=utils.COLORS['yellow']) + roof_blocks.append(block_id) + + # Add cylinder for the chimney. + cylinder_template = 'cylinder/cylinder-template.urdf' + cylinder_size = (0.04,0.04,0.02) + replace = {'DIM': cylinder_size} # radius and height dimensions for the cylinder size + cylinder_urdf = self.fill_template(cylinder_template, replace) + cylinder_pose = self.get_random_pose(env, cylinder_size) + chimney_id = env.add_object(cylinder_urdf, cylinder_pose, color=utils.COLORS['green']) + + # Define the target poses for the base, walls, roof, and chimney. + base_target_poses = [(0.7, -0.3, 0.02), (0.7, -0.2, 0.02), (0.6, -0.3, 0.02), (0.6, -0.2, 0.02)] + wall_target_poses = [(0.7, -0.3, 0.06), (0.7, -0.2, 0.06), (0.6, -0.3, 0.06), (0.6, -0.2, 0.06) ] + roof_target_poses = [(0.7, -0.25, 0.1), (0.6, -0.25, 0.1)] + chimney_target_pose = [(0.65, -0.2, 0.12)] + self.add_corner_anchor_for_pose(env, base_target_poses[0]) + + + # Add goals for each step of the house construction. + # Break the language prompt step-by-step + self.add_goal(objs=base_blocks, matches=np.ones((4, 4)), targ_poses=base_target_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, + language_goal="Construct a house structure using blocks and a cylinder. Begin by forming the base of the house with four red blocks arranged in a square shape.") + + self.add_goal(objs=wall_blocks, matches=np.ones((4, 4)), targ_poses=wall_target_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, + language_goal="Then build the walls by stacking two blue blocks on top of each base block. ") + + self.add_goal(objs=roof_blocks, matches=np.ones((2, 2)), targ_poses=roof_target_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, + language_goal="Create a roof by placing two yellow blocks on the uppermost blue blocks, angled to form an apex. ") + + self.add_goal(objs=[chimney_id], matches=np.ones((1, 1)), targ_poses=chimney_target_pose, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, + language_goal="Finally, position a green cylinder in the center of the square created by the base blocks to represent a chimney.") diff --git a/cliport/generated_tasks/build_two_circles.py b/cliport/generated_tasks/build_two_circles.py new file mode 100644 index 0000000000000000000000000000000000000000..3ee8c0ecc905f82504f17ff3b062dc3de3c5e501 --- /dev/null +++ b/cliport/generated_tasks/build_two_circles.py @@ -0,0 +1,63 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildTwoCircles(Task): + """Construct two distinct circles on the tabletop using 10 red and 10 blue blocks. + Each circle should consist of blocks of the same color, with the blue circle larger and surrounding the red circle.""" + + def __init__(self): + super().__init__() + self.max_steps = 30 + self.lang_template = "construct two distinct circles on the tabletop using 6 red and 6 blue blocks" + self.task_completed_desc = "done building two circles." + + def reset(self, env): + super().reset(env) + + # Add blocks. + block_urdf = 'block/block.urdf' + block_size = (0.04, 0.04, 0.04) + + # Add 6 red blocks. + red_blocks = [] + red_circle_poses = [] + circle_radius = 0.1 + circle_center = (0, 0, block_size[2] / 2) + angles = np.linspace(0, 2 * np.pi, 6, endpoint=False) + circle_pose = ((0.4, 0.3, 0.0), (0, 0, 0, 1)) # fixed pose + self.add_corner_anchor_for_pose(env, circle_pose) + + # Define initial and target poses for the red and blue circles. + for angle in angles: + pos = (circle_center[0] + circle_radius * np.cos(angle), + circle_center[1] + circle_radius * np.sin(angle), + circle_center[2]) + block_pose = (utils.apply(circle_pose, pos), circle_pose[1]) + block_id = env.add_object(block_urdf, self.get_random_pose(env, block_size), color=utils.COLORS['red']) + red_circle_poses.append(block_pose) + red_blocks.append(block_id) + + # Add 6 blue blocks. + blue_blocks = [] + blue_circle_poses = [] + circle_radius = 0.1 + circle_center = (0, 0, block_size[2] / 2) + circle_pose = ((0.4, -0.3, 0.0), (0,0,0,1)) # fixed pose + self.add_corner_anchor_for_pose(env, circle_pose) + + for angle in angles: + pos = (circle_center[0] + circle_radius * np.cos(angle), + circle_center[1] + circle_radius * np.sin(angle), + circle_center[2]) + block_pose = (utils.apply(circle_pose, pos), circle_pose[1]) + block_id = env.add_object(block_urdf, self.get_random_pose(env, block_size), color=utils.COLORS['blue']) + blue_circle_poses.append(block_pose) + blue_blocks.append(block_id) + + + # Goal: each red block is in the red circle, each blue block is in the blue circle. + self.add_goal(objs=red_blocks, matches=np.ones((6, 6)), targ_poses=red_circle_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, language_goal=self.lang_template) + self.add_goal(objs=blue_blocks, matches=np.ones((6, 6)), targ_poses=blue_circle_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/build_wheel.py b/cliport/generated_tasks/build_wheel.py new file mode 100644 index 0000000000000000000000000000000000000000..fca85bf43650e70fc2959cc41f3b4515e914930e --- /dev/null +++ b/cliport/generated_tasks/build_wheel.py @@ -0,0 +1,56 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildWheel(Task): + """Construct a wheel using blocks and a sphere.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "Construct a wheel using blocks and a sphere. First, position eight blocks in a circular layout on the tabletop. Each block should be touching its two neighbors and colored in alternating red and blue. Then place a green sphere in the center of the circular layout, completing the wheel." + self.task_completed_desc = "done building wheel." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue']] + blocks = [] + for i in range(8): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i % 2]) + blocks.append(block_id) + + # Add sphere. + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + sphere_color = utils.COLORS['green'] + sphere_pose = ((0.5, 0.0, 0.0), (0,0,0,1)) # fixed pose + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=sphere_color) + + # Goal: blocks are arranged in a circle and sphere is in the center. + circle_radius = 0.1 + circle_center = (0, 0, block_size[2] / 2) + angles = np.linspace(0, 2 * np.pi, 8, endpoint=False) + block_poses = [(circle_center[0] + circle_radius * np.cos(angle), + circle_center[1] + circle_radius * np.sin(angle), + circle_center[2]) for angle in angles] + block_poses = [(utils.apply(sphere_pose, pos), sphere_pose[1]) for pos in block_poses] + self.add_goal(objs=blocks, matches=np.ones((8, 8)), targ_poses=block_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=8 / 9, language_goal=self.lang_template) + + # Goal: sphere is in the center of the blocks. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[sphere_pose], replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1 / 9, language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/color_blocks_in_cylinder_maze.py b/cliport/generated_tasks/color_blocks_in_cylinder_maze.py new file mode 100644 index 0000000000000000000000000000000000000000..11386702fb28ed769345f6aac6b5d18f4a5a4f58 --- /dev/null +++ b/cliport/generated_tasks/color_blocks_in_cylinder_maze.py @@ -0,0 +1,51 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorBlocksInCylinderMaze(Task): + """Pick up five differently colored blocks (red, blue, yellow, green, and orange) that are scattered randomly on the table top. Arrange three cylindrical containers in a row to create a maze-like structure. Place the red, yellow, and blue block into the first, second, and third cylinder from left respectively. Then, stack the green and orange block on top of any container, followed by placing the same color palette on the respective block.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "arrange the blocks in the cylinders and stack the green and orange blocks" + self.task_completed_desc = "done arranging blocks in cylinders." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add cylinders. + cylinder_size = (0.05, 0.05, 0.1) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinder_poses = [] + for _ in range(3): + cylinder_pose = self.get_random_pose(env, cylinder_size) + env.add_object(cylinder_urdf, cylinder_pose, 'fixed') + cylinder_poses.append(cylinder_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['yellow'], utils.COLORS['green'], utils.COLORS['orange']] + blocks = [] + for i in range(5): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Goal: red, yellow, and blue blocks are in the first, second, and third cylinder respectively. + self.add_goal(objs=blocks[:3], matches=np.ones((3, 3)), targ_poses=cylinder_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, language_goal=self.lang_template) + + # Goal: green and orange blocks are stacked on top of any cylinder. + self.add_goal(objs=blocks[3:], matches=np.ones((2, 3)), targ_poses=cylinder_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/color_coded_blocks_on_corner.py b/cliport/generated_tasks/color_coded_blocks_on_corner.py new file mode 100644 index 0000000000000000000000000000000000000000..7eac819cade5fb643e6019b67b99a4ef5750432f --- /dev/null +++ b/cliport/generated_tasks/color_coded_blocks_on_corner.py @@ -0,0 +1,57 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCodedBlocksOnCorner(Task): + """Pick up blocks of different colors and place them in a corner structure in a specific color sequence.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "place the blocks in the corner in the sequence red, blue, green, yellow" + self.task_completed_desc = "done placing blocks in the corner." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add corner structure. + corner_size = (0.15, 0.15, 0.05) + corner_pose = self.get_random_pose(env, corner_size) + corner_urdf = 'corner/corner-template.urdf' + env.add_object(corner_urdf, corner_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, 0, 0.08)] + targs = [(utils.apply(corner_pose, i), corner_pose[1]) for i in place_pos] + + # Goal: blocks are placed in the corner in the sequence red, blue, green, yellow. + for i in range(4): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, + language_goal=self.lang_template.format(blocks="the red, blue, green, yellow blocks")) \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_arch_construction.py b/cliport/generated_tasks/color_coordinated_arch_construction.py new file mode 100644 index 0000000000000000000000000000000000000000..ecef843555ce8d03fef979308166b2b5cbe8e4bd --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_arch_construction.py @@ -0,0 +1,60 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedArchConstruction(Task): + """Construct an arch using six blocks: three red and three blue.""" + + def __init__(self): + super().__init__() + self.max_steps = 6 + self.lang_template = "construct an arch using six blocks: three red and three blue" + self.task_completed_desc = "done constructing arch." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.15, 0.15, 0.005) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['blue'], + utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['blue']] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0.05, 0.02), + (0, 0, 0.06), (0, -0.05, 0.08), + (0, 0.05, 0.08), (0, 0, 0.12)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in an arch (bottom row: red, red, blue). + self.add_goal(objs=blocks[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) + + # Goal: blocks are stacked in an arch (top row: red, red, blue). + self.add_goal(objs=blocks[3:], matches=np.ones((3, 3)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_ball_insertion.py b/cliport/generated_tasks/color_coordinated_ball_insertion.py new file mode 100644 index 0000000000000000000000000000000000000000..7d8fceb630c8bd198f533138c11573ff7fdbeb46 --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_ball_insertion.py @@ -0,0 +1,54 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedBallInsertion(Task): + """Insert balls into the cylinders of the same color in the order of red, blue, green, and yellow.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "insert the {color} ball into the {color} cylinder" + self.task_completed_desc = "done inserting balls into cylinders." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their order + colors = ['red', 'blue', 'green', 'yellow'] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.05, 0.05, 0.1) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinder_poses = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + env.add_object(cylinder_urdf, cylinder_pose, category='fixed', color=utils.COLORS[colors[i]]) + cylinder_poses.append(cylinder_pose) + + # Add balls. + # x, y, z dimensions for the asset size + balls = [] + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + for i in range(4): + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[colors[i]]) + balls.append(ball_id) + + # Goal: each ball is in the corresponding color cylinder. + for i in range(4): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[cylinder_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_ball_stacking.py b/cliport/generated_tasks/color_coordinated_ball_stacking.py new file mode 100644 index 0000000000000000000000000000000000000000..4790fe09a8429f1625d0b8ef19f062808f9f26f9 --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_ball_stacking.py @@ -0,0 +1,66 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedBallStacking(Task): + """Stack balls on top of the corresponding colored containers in a specific color sequence.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "stack the balls on top of the corresponding colored containers in the sequence blue, yellow, green, red" + self.task_completed_desc = "done stacking balls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define the color sequence + color_sequence = ['blue', 'yellow', 'green', 'red'] + + # Add containers. + container_size = (0.12, 0.12, 0.12) + container_urdf = 'container/container-template.urdf' + container_poses = [] + containers = [] + for color in color_sequence: + container_pose = self.get_random_pose(env, container_size) + container_id = env.add_object(container_urdf, container_pose, color=utils.COLORS[color]) + container_poses.append(container_pose) + containers.append(container_id) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for color in color_sequence: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[color]) + balls.append(ball_id) + + # Goal: each ball is stacked on top of the corresponding colored container in the color sequence. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[container_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(obj=color_sequence[i])) + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_ball = np.random.rand() > 0.5 + urdf = ball_urdf if is_ball else container_urdf + size = ball_size if is_ball else container_size + pose = self.get_random_pose(env, obj_size=size) + color = np.random.choice(list(utils.COLORS.keys())) + + obj_id = env.add_object(urdf, pose, color=utils.COLORS[color]) + n_distractors += 1 \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_block_bridge.py b/cliport/generated_tasks/color_coordinated_block_bridge.py new file mode 100644 index 0000000000000000000000000000000000000000..67344d749436fc0bb504dc12015ae6ece14a68f9 --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_block_bridge.py @@ -0,0 +1,63 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedBlockBridge(Task): + """Construct a bridge by interleaving three differently colored blocks (red, blue, and green) on a pallet in a specific sequence - red block at the edges, blue block in the middle, and a green block on top of the red and blue blocks. Repeat this sequence until a bridge is formed across the length of the pallet.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "construct a bridge by interleaving three differently colored blocks (red, blue, and green) on a pallet in a specific sequence" + self.task_completed_desc = "done constructing the bridge." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.30, 0.15, 0.02) + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object('pallet/pallet.urdf', pallet_pose, 'fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green']] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + + objs = [] + for i in range(9): # 3 sets of 3 colored blocks + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i % 3]) + objs.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0, 0.02), (0, 0.05, 0.02), # bottom layer + (0, -0.05, 0.06), (0, 0, 0.06), (0, 0.05, 0.06), # middle layer + (0, -0.05, 0.10), (0, 0, 0.10), (0, 0.05, 0.10)] # top layer + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a bridge (bottom layer: red, blue, red). + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (middle layer: green, green, green). + self.add_goal(objs=objs[3:6], matches=np.ones((3, 3)), targ_poses=targs[3:6], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (top layer: red, blue, red). + self.add_goal(objs=objs[6:], matches=np.ones((3, 3)), targ_poses=targs[6:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) diff --git a/cliport/generated_tasks/color_coordinated_block_shifting.py b/cliport/generated_tasks/color_coordinated_block_shifting.py new file mode 100644 index 0000000000000000000000000000000000000000..8b853f4fb4993f3fb12f5da5d0f24cc3c2964f3f --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_block_shifting.py @@ -0,0 +1,58 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedBlockShifting(Task): + """Pick up each block and precisely place it in the zone of the same color.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "move the {color} blocks to the {color} zone" + self.task_completed_desc = "done moving blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_colors = ['yellow', 'blue', 'green'] + zone_poses = [] + for color in zone_colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + blocks = [] + for color in zone_colors: + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add small blocks as obstacles. + small_block_size = (0.02, 0.02, 0.02) + small_block_urdf = 'stacking/block.urdf' + for _ in range(5): + small_block_pose = self.get_random_pose(env, small_block_size) + env.add_object(small_block_urdf, small_block_pose) + + # Goal: each block is in the zone of the same color. + for i in range(9): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i//3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/9, + language_goal=self.lang_template.format(color=zone_colors[i//3])) \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_block_tower.py b/cliport/generated_tasks/color_coordinated_block_tower.py new file mode 100644 index 0000000000000000000000000000000000000000..1aed00f84d14a6d4a68266dab0bb9b444d5e119e --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_block_tower.py @@ -0,0 +1,64 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedBlockTower(Task): + """Stack four blocks on a pallet in the following order from bottom to top: + two blue blocks side by side, one red block centered on the blue blocks, + and one green block on top of the red block.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "stack four blocks on a pallet in the following order from bottom to top: two blue blocks side by side, one red block centered on the blue blocks, and one green block on top of the red block." + self.task_completed_desc = "done stacking blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.015) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['blue'], utils.COLORS['blue'], utils.COLORS['red'], utils.COLORS['green']] + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.02, 0.02), (0, 0.02, 0.02), (0, 0, 0.06), (0, 0, 0.10)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: two blue blocks are placed side by side on the pallet. + # Break the language prompt step-by-step + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, + language_goal="place two blue blocks side by side on the pallet") + + # Goal: one red block is placed centered on the blue blocks. + self.add_goal(objs=blocks[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal="place one red block centered on the blue blocks") + + # Goal: one green block is placed on top of the red block. + self.add_goal(objs=blocks[3:], matches=np.ones((1, 1)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal="place one green block on top of the red block") \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_box_ball_matching.py b/cliport/generated_tasks/color_coordinated_box_ball_matching.py new file mode 100644 index 0000000000000000000000000000000000000000..a15c0586e63bd1c6cec7a016897e6278daeb3b8f --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_box_ball_matching.py @@ -0,0 +1,58 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedBoxBallMatching(Task): + """Pick up each ball and place it inside the box of the same color, navigate around the barrier without knocking over any small blocks.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "put the {color} ball in the {color} box" + self.task_completed_desc = "done placing balls in boxes." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for the boxes and balls + colors = ['red', 'blue', 'green', 'yellow'] + + # Add boxes. + box_size = (0.05, 0.05, 0.05) + box_urdf = 'box/box-template.urdf' + box_poses = [] + for color in colors: + box_pose = self.get_random_pose(env, box_size) + env.add_object(box_urdf, box_pose, color=color, category='fixed') + box_poses.append(box_pose) + + # Add balls. + balls = [] + ball_size = (0.02, 0.02, 0.02) + ball_urdf = 'ball/ball-template.urdf' + for color in colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add small blocks as barriers. + barrier_size = (0.01, 0.01, 0.01) + barrier_urdf = 'block/small.urdf' + for _ in range(10): + barrier_pose = self.get_random_pose(env, barrier_size) + env.add_object(barrier_urdf, barrier_pose, category='fixed') + + # Goal: each ball is in the box of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[box_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_cylinder_ball_match.py b/cliport/generated_tasks/color_coordinated_cylinder_ball_match.py new file mode 100644 index 0000000000000000000000000000000000000000..c68700bfe287370c2b31f3ccbbbafc1370ab92f9 --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_cylinder_ball_match.py @@ -0,0 +1,62 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedCylinderBallMatch(Task): + """Pick up each ball and place it on top of the cylinder of the same color.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the {color} ball on the {color} cylinder" + self.task_completed_desc = "done placing balls on cylinders." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.1) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinder_colors = ['red', 'blue', 'green', 'yellow'] + cylinder_poses = [] + cylinders = [] + for color in cylinder_colors: + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=color) + cylinder_poses.append(cylinder_pose) + cylinders.append(cylinder_id) + + # Add balls. + # x, y, z dimensions for the asset size + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for color in cylinder_colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add blocks as obstacles. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/small.urdf' + for _ in range(5): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose) + + # Goal: each ball is on top of the cylinder of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[cylinder_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=cylinder_colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_cylinder_pyramid.py b/cliport/generated_tasks/color_coordinated_cylinder_pyramid.py new file mode 100644 index 0000000000000000000000000000000000000000..fd5ca1a871f7999ed5874b36f04e493f97f8c0f1 --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_cylinder_pyramid.py @@ -0,0 +1,68 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedCylinderPyramid(Task): + """Construct a pyramid on a pallet using four cylinders of different colors (red, blue, green, and yellow).""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {cylinder}" + self.task_completed_desc = "done stacking cylinder pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.15, 0.15, 0.005) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + + # Cylinder colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'] + ] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=colors[i]) + cylinders.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0.05, 0.03), + (0, 0, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: cylinders are stacked in a pyramid (bottom row: red, blue). + self.add_goal(objs=cylinders[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, + language_goal=self.lang_template.format(cylinder="the red and blue cylinders", row="bottom")) + + # Goal: cylinders are stacked in a pyramid (middle row: green). + self.add_goal(objs=cylinders[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*1, + language_goal=self.lang_template.format(cylinder="the green cylinder", row="middle")) + + # Goal: cylinders are stacked in a pyramid (top row: yellow). + self.add_goal(objs=cylinders[3:], matches=np.ones((1, 1)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*1, + language_goal=self.lang_template.format(cylinder="the yellow cylinder", row="top")) \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_cylinder_stand_assembly.py b/cliport/generated_tasks/color_coordinated_cylinder_stand_assembly.py new file mode 100644 index 0000000000000000000000000000000000000000..b85541537c3812cff89e8274b91b2a30ef3cdb48 --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_cylinder_stand_assembly.py @@ -0,0 +1,52 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedCylinderStandAssembly(Task): + """Pick up each cylinder and place it on top of the stand of the same color, in a specific color sequence.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "place the {color} cylinder on the {color} stand" + self.task_completed_desc = "done placing cylinders on stands." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their sequence + colors = ['green', 'yellow', 'blue', 'red'] + color_sequence = [utils.COLORS[color] for color in colors] + + # Add stands. + stand_size = (0.04, 0.04, 0.04) + stand_urdf = 'stacking/stand.urdf' + stand_poses = [] + for i in range(4): + stand_pose = self.get_random_pose(env, stand_size) + env.add_object(stand_urdf, stand_pose, color=color_sequence[i], category='fixed') + stand_poses.append(stand_pose) + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=color_sequence[i]) + cylinders.append(cylinder_id) + + # Goal: each cylinder is on the stand of the same color, in the specified color sequence. + for i in range(4): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[stand_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_cylinder_tower.py b/cliport/generated_tasks/color_coordinated_cylinder_tower.py new file mode 100644 index 0000000000000000000000000000000000000000..1b79a3b5434b985f50252856644def1ad5edbb25 --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_cylinder_tower.py @@ -0,0 +1,53 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedCylinderTower(Task): + """Stack cylinders of four different colors (red, blue, green, yellow) on top of each other on a square stand in a specific sequence. The bottom of the stack should start with a blue cylinder, follow by a green cylinder, then a red one, and finally a yellow cylinder at the top. Each cylinder has to be aligned correctly to avoid falling.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "Stack cylinders of four different colors (red, blue, green, yellow) on top of each other on a square stand in a specific sequence. The bottom of the stack should start with a blue cylinder, follow by a green cylinder, then a red one, and finally a yellow cylinder at the top." + self.task_completed_desc = "done stacking cylinders." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Cylinder colors. + colors = [utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['red'], utils.COLORS['yellow']] + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + + objs = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=colors[i]) + objs.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0, 0, 0.03), (0, 0, 0.08), (0, 0, 0.13), (0, 0, 0.18)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: cylinders are stacked in a tower (bottom to top: blue, green, red, yellow). + for i in range(4): + self.add_goal(objs=[objs[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_insertion.py b/cliport/generated_tasks/color_coordinated_insertion.py new file mode 100644 index 0000000000000000000000000000000000000000..81375f5d89d6dc0d3c766c599535f8799333825e --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_insertion.py @@ -0,0 +1,62 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedInsertion(Task): + """Insert each block into the fixture of the same color""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "insert each block into the fixture of the same color" + self.task_completed_desc = "done with color-coordinated-insertion." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.35, 0.35, 0.01) + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_urdf = 'pallet/pallet.urdf' + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add fixtures and blocks. + colors = ['red', 'blue', 'green', 'yellow'] + fixtures = [] + blocks = [] + fixture_size = (0.05, 0.05, 0.05) + block_size = (0.04, 0.04, 0.04) + fixture_urdf = 'insertion/fixture.urdf' + block_urdf = 'block/block.urdf' + for color in colors: + # Add fixture. + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color]) + fixtures.append(fixture_id) + + # Add block. + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in the fixture of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(blocks), + language_goal=self.lang_template) + + # Goal: each fixture is on the pallet. + for i in range(len(fixtures)): + self.add_goal(objs=[fixtures[i]], matches=np.ones((1, 1)), targ_poses=[pallet_pose], replace=False, + rotations=True, metric='zone', params=[(pallet_pose, pallet_size)], step_max_reward=1 / len(fixtures), + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_sphere_and_cylinder_assembly.py b/cliport/generated_tasks/color_coordinated_sphere_and_cylinder_assembly.py new file mode 100644 index 0000000000000000000000000000000000000000..5bfbc2ac42a37fd5cc6b350ee893158e4e7e37bc --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_sphere_and_cylinder_assembly.py @@ -0,0 +1,46 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedSphereAndCylinderAssembly(Task): + """Pick up each sphere and place it on top of the cylinder of the same color, in a specific color sequence.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the {color} sphere on the {color} cylinder" + self.task_completed_desc = "done placing spheres on cylinders." + self.colors = ['red', 'blue', 'green', 'yellow'] + self.color_sequence = ['red', 'blue', 'green', 'yellow'] + + def reset(self, env): + super().reset(env) + + # Add spheres and cylinders. + sphere_size = (0.05, 0.05, 0.05) + cylinder_size = (0.05, 0.05, 0.1) + sphere_template = 'sphere/sphere-template.urdf' + cylinder_template = 'cylinder/cylinder-template.urdf' + + # Add spheres and cylinders of each color. + for color in self.colors: + sphere_pose = self.get_random_pose(env, sphere_size) + cylinder_pose = self.get_random_pose(env, cylinder_size) + sphere_id = env.add_object(sphere_template, sphere_pose, color=color) + cylinder_id = env.add_object(cylinder_template, cylinder_pose, color=color) + + # Goal: each sphere is on top of the cylinder of the same color. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[cylinder_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=color)) + + # The task is completed in a specific color sequence. + self.color_sequence = ['red', 'blue', 'green', 'yellow'] \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_sphere_insertion.py b/cliport/generated_tasks/color_coordinated_sphere_insertion.py new file mode 100644 index 0000000000000000000000000000000000000000..6904c620b037fb2ee7c4496294c48cee2d4a2a53 --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_sphere_insertion.py @@ -0,0 +1,56 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedSphereInsertion(Task): + """Insert each sphere into the bowl of the same color.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "insert each sphere into the bowl of the same color" + self.task_completed_desc = "done inserting spheres into bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and corresponding names + colors = ['red', 'blue', 'green', 'yellow'] + color_values = [utils.COLORS[color] for color in colors] + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0.02) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for i in range(4): + bowl_pose = self.get_random_pose(env, bowl_size) + env.add_object(bowl_urdf, bowl_pose, 'fixed', color=color_values[i]) + bowl_poses.append(bowl_pose) + + # Add spheres. + # x, y, z dimensions for the asset size + sphere_size = (0.04, 0.04, 0.04) + sphere_template = 'sphere/sphere-template.urdf' + spheres = [] + for i in range(4): + sphere_pose = self.get_random_pose(env, sphere_size) + replace = {'DIM': sphere_size, 'HALF': (sphere_size[0] / 2, sphere_size[1] / 2, sphere_size[2] / 2)} + sphere_urdf = self.fill_template(sphere_template, replace) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=color_values[i]) + spheres.append(sphere_id) + + # Goal: each sphere is in a bowl of the same color. + for i in range(4): + self.add_goal(objs=[spheres[i]], matches=np.ones((1, 1)), targ_poses=[bowl_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=f"insert the {colors[i]} sphere into the {colors[i]} bowl") \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_sphere_on_pallet_pyramid.py b/cliport/generated_tasks/color_coordinated_sphere_on_pallet_pyramid.py new file mode 100644 index 0000000000000000000000000000000000000000..403289939dfc00e54df7cda77544e3cf28b52c26 --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_sphere_on_pallet_pyramid.py @@ -0,0 +1,78 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedSphereOnPalletPyramid(Task): + """Build a pyramid of colored blocks on pallets and place a matching colored sphere on top.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "build a pyramid of {color} blocks on the pallet and place the {color} sphere on top" + self.task_completed_desc = "done building color-coordinated pyramids." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Pallets and Blocks + pallet_size = (0.15, 0.15, 0.01) + block_size = (0.04, 0.04, 0.04) + pallet_urdf = 'pallet/pallet.urdf' + block_urdf = 'block/block.urdf' + + # Colors for blocks and spheres + colors = ['red', 'blue', 'green'] + color_objects = {} + + # Add pallets and blocks + for color in colors: + # Add pallet + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + + # Add blocks + block_ids = [] + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + block_ids.append(block_id) + + color_objects[color] = {'pallet': pallet_pose, 'blocks': block_ids} + + # Spheres + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + + # Add spheres + for color in colors: + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS[color]) + color_objects[color]['sphere'] = sphere_id + + # Goals + for color in colors: + # Goal: blocks are stacked in a pyramid on the pallet + block_poses = [(0, -0.02, 0.02), (0, 0.02, 0.02), (0, 0, 0.06)] + targs = [(utils.apply(color_objects[color]['pallet'], i), color_objects[color]['pallet'][1]) for i in block_poses] + + self.add_goal(objs=color_objects[color]['blocks'], matches=np.ones((3, 3)), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(color=color)) + + # Goal: sphere is placed on top of the pyramid + sphere_pose = (0, 0, 0.1) + targ = (utils.apply(color_objects[color]['pallet'], sphere_pose), color_objects[color]['pallet'][1]) + + self.add_goal(objs=[color_objects[color]['sphere']], matches=np.ones((1, 1)), targ_poses=[targ], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2], + language_goal=self.lang_template.format(color=color)) \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_zone_arrangement.py b/cliport/generated_tasks/color_coordinated_zone_arrangement.py new file mode 100644 index 0000000000000000000000000000000000000000..9b4f9cf4b9e61bb387bece0cb0fa6db56dd25b27 --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_zone_arrangement.py @@ -0,0 +1,60 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedZoneArrangement(Task): + """Pick up blocks of different colors and place them on the pallets of the same color.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "place the {color} blocks on the {color} pallet" + self.task_completed_desc = "done arranging blocks on pallets." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallets. + # x, y, z dimensions for the asset size + pallet_size = (0.12, 0.12, 0.02) + pallet_urdf = 'pallet/pallet.urdf' + pallet_colors = ['red', 'blue', 'green'] + pallet_poses = [] + for color in pallet_colors: + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed', color=utils.COLORS[color]) + pallet_poses.append(pallet_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + for color in pallet_colors: + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add small blocks as obstacles. + small_block_size = (0.02, 0.02, 0.02) + small_block_urdf = 'block/small.urdf' + for _ in range(5): + small_block_pose = self.get_random_pose(env, small_block_size) + env.add_object(small_block_urdf, small_block_pose) + + # Goal: each block is on the pallet of the same color. + for i in range(9): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[pallet_poses[i // 3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 9, + language_goal=self.lang_template.format(color=pallet_colors[i // 3])) \ No newline at end of file diff --git a/cliport/generated_tasks/color_coordinated_zone_stacking.py b/cliport/generated_tasks/color_coordinated_zone_stacking.py new file mode 100644 index 0000000000000000000000000000000000000000..85832dcbb7d478f8168d38533e2bed0713674102 --- /dev/null +++ b/cliport/generated_tasks/color_coordinated_zone_stacking.py @@ -0,0 +1,60 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedZoneStacking(Task): + """Pick up blocks of different colors and stack them in zones to form a pyramid.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "stack the blocks in the zones to form a pyramid" + self.task_completed_desc = "done stacking blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for _ in range(3): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(9): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i//3]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(zone_poses[i//3], place_pos[i%3]), zone_poses[i//3][1]) for i in range(9)] + + # Goal: blocks are stacked in a pyramid in each zone. + for i in range(3): + self.add_goal(objs=blocks[i*3:(i+1)*3], matches=np.ones((3, 3)), targ_poses=targs[i*3:(i+1)*3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(blocks="the red, blue and green blocks", + row="bottom")) \ No newline at end of file diff --git a/cliport/generated_tasks/color_cued_ball_corner_sorting.py b/cliport/generated_tasks/color_cued_ball_corner_sorting.py new file mode 100644 index 0000000000000000000000000000000000000000..b24285254c772733bbdfb70ca226c0c618a208c0 --- /dev/null +++ b/cliport/generated_tasks/color_cued_ball_corner_sorting.py @@ -0,0 +1,62 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCuedBallCornerSorting(Task): + """Pick up each colored ball and place it in the corner of the same color while avoiding a zone marked by small blocks.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the {color} ball in the {color} corner" + self.task_completed_desc = "done sorting balls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add corners. + corner_size = (0.05, 0.05, 0.05) + corner_urdf = 'corner/corner-template.urdf' + corner_colors = ['red', 'blue', 'green', 'yellow'] + corner_poses = [] + for color in corner_colors: + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, color=color, category='fixed') + corner_poses.append(corner_pose) + + # Add balls. + balls = [] + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + for color in corner_colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add zone. + zone_size = (0.2, 0.2, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + zone_urdf = 'zone/zone.urdf' + env.add_object(zone_urdf, zone_pose, 'fixed') + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block_for_anchors.urdf' + for _ in range(4): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose) + + # Goal: each ball is in the corner of the same color. + for i in range(4): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[corner_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=corner_colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/color_ordered_blocks_on_pallet.py b/cliport/generated_tasks/color_ordered_blocks_on_pallet.py new file mode 100644 index 0000000000000000000000000000000000000000..b89e13ee73e9fa43d9f9b8efaf7c127d8c3cc5eb --- /dev/null +++ b/cliport/generated_tasks/color_ordered_blocks_on_pallet.py @@ -0,0 +1,58 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorOrderedBlocksOnPallet(Task): + """Pick up each colored block and place it onto the pallet in specific color sequence: red, blue, green, yellow, orange, and finally purple.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the colored blocks onto the pallet in the following order: red, blue, green, yellow, orange, and purple" + self.task_completed_desc = "done placing blocks on the pallet." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.02) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['purple'] + ] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are placed on the pallet in the order of red, blue, green, yellow, orange, purple. + for i in range(6): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2], language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/color_ordered_container_arrangement.py b/cliport/generated_tasks/color_ordered_container_arrangement.py new file mode 100644 index 0000000000000000000000000000000000000000..5d3da86ee288f7f719c3f9c081aa8649267508e3 --- /dev/null +++ b/cliport/generated_tasks/color_ordered_container_arrangement.py @@ -0,0 +1,58 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorOrderedContainerArrangement(Task): + """Arrange six containers with blocks of matching colors in a specific color order.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "arrange the containers in the color order: red, blue, green, yellow, orange, and purple" + self.task_completed_desc = "done arranging containers." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define color order + color_order = ['red', 'blue', 'green', 'yellow', 'orange', 'purple'] + + # Add containers and blocks + container_template = 'container/container-template.urdf' + container_size = (0.12, 0.12, 0.02) + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_template, replace) + + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + containers = [] + blocks = [] + for color in color_order: + # Add container + container_pose = self.get_random_pose(env, container_size) + container_id = env.add_object(container_urdf, container_pose, color=utils.COLORS[color]) + containers.append(container_id) + + # Add block + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add subgoal to place block in container + self.add_goal(objs=[block_id], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/6, + language_goal=self.lang_template) + + # Add final goal to arrange containers in color order + container_poses = [self.get_random_pose(env, container_size) for _ in color_order] + self.add_goal(objs=containers, matches=np.eye(len(color_order)), targ_poses=container_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/color_ordered_insertion.py b/cliport/generated_tasks/color_ordered_insertion.py new file mode 100644 index 0000000000000000000000000000000000000000..f8d56f69e842a2a77b55f17df1beb3ee5b01f661 --- /dev/null +++ b/cliport/generated_tasks/color_ordered_insertion.py @@ -0,0 +1,52 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorOrderedInsertion(Task): + """Insert differently-colored ell objects into the matching color fixture in a specific order.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "insert the {color} ell into the matching fixture" + self.task_completed_desc = "done inserting ells." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their order + colors = ['red', 'blue', 'green', 'yellow'] + color_order = {color: i for i, color in enumerate(colors)} + + # Add fixtures. + fixture_size = (0.12, 0.12, 0.02) + fixture_urdf = 'insertion/fixture.urdf' + fixtures = [] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color], category='fixed') + fixtures.append(fixture_id) + + # Add ell objects. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + ells = [] + for color in colors: + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[color]) + ells.append(ell_id) + + # Goal: each ell is inserted into the matching color fixture in the correct order. + for i, ell in enumerate(ells): + self.add_goal(objs=[ell], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(ells), + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/color_ordered_insertion_new.py b/cliport/generated_tasks/color_ordered_insertion_new.py new file mode 100644 index 0000000000000000000000000000000000000000..72cc3f4f34d8822ba14e7a7e9c73b1e995304a8f --- /dev/null +++ b/cliport/generated_tasks/color_ordered_insertion_new.py @@ -0,0 +1,52 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorOrderedInsertionNew(Task): + """Insert differently-colored ell objects into the matching color fixture in a specific order.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "put the {color} L shape block in the L shape hole" + self.task_completed_desc = "done with insertion." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their order + colors = ['red', 'blue', 'green', 'yellow'] + color_order = {color: i for i, color in enumerate(colors)} + + # Add fixtures. + fixture_size = (0.12, 0.12, 0.02) + fixture_urdf = 'insertion/fixture.urdf' + fixtures = [] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color], category='fixed') + fixtures.append(fixture_id) + + # Add ell objects. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + ells = [] + for color in colors: + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[color]) + ells.append(ell_id) + + # Goal: each ell is inserted into the matching color fixture in the correct order. + for i, ell in enumerate(ells): + self.add_goal(objs=[ell], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(ells), + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/color_sequenced_pyramid_packing.py b/cliport/generated_tasks/color_sequenced_pyramid_packing.py new file mode 100644 index 0000000000000000000000000000000000000000..c2a2dea351a5b7a978c8a7763e5e0e687a0bdb1e --- /dev/null +++ b/cliport/generated_tasks/color_sequenced_pyramid_packing.py @@ -0,0 +1,63 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorSequencedPyramidPacking(Task): + """Sort cubes by color into four pallets and stack them in each pallet as a pyramid""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "sort the {color} cubes into the pallet and stack them as a pyramid" + self.task_completed_desc = "done sorting and stacking cubes." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallets. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.02) + pallet_urdf = 'pallet/pallet.urdf' + pallet_poses = [] + for _ in range(4): + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + pallet_poses.append(pallet_pose) + + # Cube colors. + colors = [ + utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['yellow'] + ] + + # Add cubes. + # x, y, z dimensions for the asset size + cube_size = (0.04, 0.04, 0.04) + cube_urdf = 'block/block.urdf' + + objs = [] + for i in range(12): + cube_pose = self.get_random_pose(env, cube_size) + cube_id = env.add_object(cube_urdf, cube_pose, color=colors[i%4]) + objs.append(cube_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos for pallet_pose in pallet_poses] + + # Goal: cubes are sorted by color and stacked in a pyramid in each pallet. + for i in range(4): + self.add_goal(objs=objs[i*3:(i+1)*3], matches=np.ones((3, 3)), targ_poses=targs[i*3:(i+1)*3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(color=list(utils.COLORS.keys())[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/color_sequenced_sphere_placement.py b/cliport/generated_tasks/color_sequenced_sphere_placement.py new file mode 100644 index 0000000000000000000000000000000000000000..07296929bada75ece2e7c2f9c4cde99469dcd8c1 --- /dev/null +++ b/cliport/generated_tasks/color_sequenced_sphere_placement.py @@ -0,0 +1,51 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorSequencedSpherePlacement(Task): + """Pick up spheres of different colors and place them in the center of the square of the same color in a specific sequence.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the {color} sphere in the {color} square" + self.task_completed_desc = "done placing spheres." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their sequence + colors = ['red', 'blue', 'green', 'yellow'] + + # Add squares of different colors + square_size = (0.1, 0.1, 0.005) + square_urdf = 'square/square-template.urdf' + square_poses = [] + for color in colors: + square_pose = self.get_random_pose(env, square_size) + env.add_object(square_urdf, square_pose, 'fixed', color=color) + square_poses.append(square_pose) + + # Add spheres of different colors + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + spheres = [] + for color in colors: + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=color) + spheres.append(sphere_id) + + # Goal: each sphere is in the square of the same color, in the correct sequence + for i in range(len(colors)): + self.add_goal(objs=[spheres[i]], matches=np.ones((1, 1)), targ_poses=[square_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(colors), + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/color_sorted_block_race.py b/cliport/generated_tasks/color_sorted_block_race.py new file mode 100644 index 0000000000000000000000000000000000000000..be99732b6d256d70d3c309b0f89df49b6c5e9cce --- /dev/null +++ b/cliport/generated_tasks/color_sorted_block_race.py @@ -0,0 +1,51 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorSortedBlockRace(Task): + """Pick up blocks of two colors and place them in corresponding colored zones in a sequence.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the blocks in the corresponding colored zones in sequence" + self.task_completed_desc = "done placing blocks in zones." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_colors = ['blue', 'red'] + zone_poses = [] + for color in zone_colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = ['blue', 'red'] + blocks = [] + for color in block_colors: + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in the corresponding colored zone. + for i, block in enumerate(blocks): + self.add_goal(objs=[block], matches=np.ones((1, 1)), targ_poses=[zone_poses[i//3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/color_sorted_container_stack.py b/cliport/generated_tasks/color_sorted_container_stack.py new file mode 100644 index 0000000000000000000000000000000000000000..9fbe7c12f389a5a12edef8d930d6e4eaefb682a5 --- /dev/null +++ b/cliport/generated_tasks/color_sorted_container_stack.py @@ -0,0 +1,58 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorSortedContainerStack(Task): + """Stack four differently colored blocks (red, blue, green, yellow) inside a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "stack the blocks in the container in the order: red, blue, green, then yellow" + self.task_completed_desc = "done stacking blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + # x, y, z dimensions for the asset size + container_size = (0.15, 0.15, 0.15) + container_pose = self.get_random_pose(env, container_size) + container_urdf = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_urdf, replace) + env.add_object(container_urdf, container_pose, 'fixed') + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + for i in range(2): + bowl_pose = self.get_random_pose(env, bowl_size) + env.add_object(bowl_urdf, bowl_pose, 'fixed') + + # Goal: each block is stacked in the container in the order: red, blue, green, yellow. + for i in range(4): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/color_specific_container_fill.py b/cliport/generated_tasks/color_specific_container_fill.py new file mode 100644 index 0000000000000000000000000000000000000000..0ecd6f44cfd87e8a1488bf2ba1e08456b38804f3 --- /dev/null +++ b/cliport/generated_tasks/color_specific_container_fill.py @@ -0,0 +1,63 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorSpecificContainerFill(Task): + """Arrange four colored blocks (red, blue, green, and yellow) around a pallet. + Then, pick up these blocks and place them inside a container marked in the same color. + The task requires precise placement, color matching, and an understanding of spatial structures.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "put the {color} block in the {color} container" + self.task_completed_desc = "done arranging blocks in containers." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Define block and container colors. + colors = ['red', 'blue', 'green', 'yellow'] + + # Add blocks and containers. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + container_size = (0.12, 0.12, 0.05) + container_template = 'container/container-template.urdf' + blocks = [] + containers = [] + for color in colors: + # Add block. + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add container. + container_pose = self.get_random_pose(env, container_size) + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_template, replace) + container_id = env.add_object(container_urdf, container_pose, 'fixed', color=utils.COLORS[color]) + containers.append(container_id) + + # Goal: each block is in a container of the same color. + for i in range(len(colors)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(containers[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(colors), + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/color_structured_block_tower.py b/cliport/generated_tasks/color_structured_block_tower.py new file mode 100644 index 0000000000000000000000000000000000000000..b50c77f617836ab751f8e9d969853fef929dca12 --- /dev/null +++ b/cliport/generated_tasks/color_structured_block_tower.py @@ -0,0 +1,52 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorStructuredBlockTower(Task): + """Construct a tower using six blocks: two red, two blue, and two green. + The tower should be built in the order of a red block at the base, + followed by a blue, then green, then red, blue and green at the top.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "construct a tower using six blocks: two red, two blue, and two green. " \ + "The tower should be built in the order of a red block at the base, " \ + "followed by a blue, then green, then red, blue and green at the top." + self.task_completed_desc = "done building color-structured block tower." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define block colors and sizes + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green']] * 2 + block_size = (0.04, 0.04, 0.04) + + # Add blocks + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Define target poses for the blocks in the tower + base_pose = self.get_random_pose(env, block_size) + targ_poses = [base_pose] + for i in range(1, 6): + targ_poses.append((np.array(base_pose[0]) + np.array([0, 0, i * block_size[2]]), base_pose[1])) + + # Add goals + for i in range(6): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targ_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/6, symmetries=[np.pi/2], + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/colored_balls_sorting_in_corner.py b/cliport/generated_tasks/colored_balls_sorting_in_corner.py new file mode 100644 index 0000000000000000000000000000000000000000..5d43e21aacb169a4af6f22386d20552de6d05e7d --- /dev/null +++ b/cliport/generated_tasks/colored_balls_sorting_in_corner.py @@ -0,0 +1,51 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColoredBallsSortingInCorner(Task): + """Pick up each ball and place it in the corner of the same color, in the specific sequence of red, blue, green and yellow, starting from the leftmost corner to the rightmost.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the {color} ball in the {color} corner" + self.task_completed_desc = "done sorting balls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define the colors and their sequence + colors = ['red', 'blue', 'green', 'yellow'] + + # Add corners. + corner_size = (0.12, 0.12, 0) + corner_urdf = 'corner/corner-template.urdf' + corner_poses = [] + for i in range(4): + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, 'fixed', color=utils.COLORS[colors[i]]) + corner_poses.append(corner_pose) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for i in range(4): + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[colors[i]]) + balls.append(ball_id) + + # Goal: each ball is in the corner of the same color. + for i in range(4): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[corner_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/colored_cylinder_in_square.py b/cliport/generated_tasks/colored_cylinder_in_square.py new file mode 100644 index 0000000000000000000000000000000000000000..be3f01bea7c5d8e3f302d9d92ec0c6193612d78e --- /dev/null +++ b/cliport/generated_tasks/colored_cylinder_in_square.py @@ -0,0 +1,44 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColoredCylinderInSquare(Task): + """Pick up five differently colored cylinder blocks and arrange them inside the square template on the tabletop. Each block should be placed along the corresponding color edge: red, blue, green, yellow, and orange.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "arrange the {color} cylinder along the {color} edge" + self.task_completed_desc = "done arranging cylinders." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add square template. + square_size = (0.3, 0.3, 0.005) # x, y, z dimensions for the asset size + square_pose = self.get_random_pose(env, square_size) + square_urdf = 'square/square-template.urdf' + env.add_object(square_urdf, square_pose, 'fixed') + + # Cylinder colors. + colors = ['red', 'blue', 'green', 'yellow', 'orange'] + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.08) # x, y, z dimensions for the asset size + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for color in colors: + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=utils.COLORS[color]) + cylinders.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0.1, 0, 0.04), (-0.1, 0, 0.04), (0, 0.1, 0.04), (0, -0.1, 0.04), (0, 0, 0.04)] + targs = [(utils.apply(square_pose, i), square_pose[1]) for i in place_pos] + + # Goal: each cylinder is placed along the corresponding color edge. + for i, cylinder in enumerate(cylinders): + self.add_goal(objs=[cylinder], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 5, + language_goal=self.lang_template.format(color=colors[i])) diff --git a/cliport/generated_tasks/colorful_block_tower_on_cylinder_base.py b/cliport/generated_tasks/colorful_block_tower_on_cylinder_base.py new file mode 100644 index 0000000000000000000000000000000000000000..f99051e71c939dc29ba33a29c89b196a9574c972 --- /dev/null +++ b/cliport/generated_tasks/colorful_block_tower_on_cylinder_base.py @@ -0,0 +1,55 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorfulBlockTowerOnCylinderBase(Task): + """Construct a tower using four blocks of different colors (red, blue, green, and yellow) on a placed cylindrical base at the corner of the tabletop. The sequence from bottom to top should be red, blue, green, and yellow.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "construct a tower using four blocks of different colors (red, blue, green, and yellow) on a placed cylindrical base at the corner of the tabletop. The sequence from bottom to top should be red, blue, green, and yellow." + self.task_completed_desc = "done building the tower." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add cylindrical base. + # x, y, z dimensions for the asset size + base_size = (0.05, 0.05, 0.05) + base_urdf = 'cylinder/cylinder-template.urdf' + base_pose = self.get_random_pose(env, base_size) + base_id = env.add_object(base_urdf, base_pose, 'fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + + objs = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, 0, 0.05), (0, 0, 0.09), (0, 0, 0.13), (0, 0, 0.17)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked on the cylindrical base in the order red, blue, green, yellow from bottom to top. + for i in range(4): + self.add_goal(objs=[objs[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/connect_boxes_with_rope.py b/cliport/generated_tasks/connect_boxes_with_rope.py new file mode 100644 index 0000000000000000000000000000000000000000..519b6315d845e41b71a206a7ca17d1a81fe5db87 --- /dev/null +++ b/cliport/generated_tasks/connect_boxes_with_rope.py @@ -0,0 +1,49 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import IPython + +class ConnectBoxesWithRope(Task): + """Connect two colored blocks with ropes.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "connect the {color1} and {color2} blocks with the rope." + self.task_completed_desc = "done connecting." + self.additional_reset() + self.pos_eps = 0.04 # higher tolerance + + def reset(self, env): + super().reset(env) + colors = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'] + blocks = [] + target_colors = np.random.choice(colors, 2, replace=False) + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + corner_poses = [] + + for color in colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + if color in target_colors: + corner_poses.append(block_pose) + + dist = np.linalg.norm(np.array(corner_poses[0][0])-np.array(corner_poses[1][0])) + n_parts = int(20 * dist / 0.4) + + # IMPORTANT: use `make_ropes` to add cable (series of articulated small blocks). + objects, targets, matches = self.make_ropes(env, corners=(corner_poses[0][0], corner_poses[1][0]), n_parts=n_parts) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1., + language_goal=self.lang_template.format(color1=target_colors[0], color2=target_colors[1])) + + # wait for the scene to settle down + for i in range(600): + p.stepSimulation() \ No newline at end of file diff --git a/cliport/generated_tasks/construct_colorful_arch.py b/cliport/generated_tasks/construct_colorful_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..38f8e7d8d17833ec8e3c65e8cdfa352acd0eccac --- /dev/null +++ b/cliport/generated_tasks/construct_colorful_arch.py @@ -0,0 +1,63 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ConstructColorfulArch(Task): + """Construct an arch using six blocks: three red, and three blue.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "Construct an arch using six blocks: three red, and three blue." + self.task_completed_desc = "done constructing colorful arch." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + colors = [utils.COLORS['red'], utils.COLORS['blue']] + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + color = colors[i // 3] # First three blocks are red, last three are blue + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0.05, 0.02), # Base layer + (0, 0, 0.06), # Second layer + (0, -0.05, 0.10), (0, 0.05, 0.10), # Third layer + (0, 0, 0.14)] # Top layer + targs = [(utils.apply(block_pose, i), block_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in an arch (bottom layer: red, red). + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, + language_goal="Place two red blocks on the tabletop parallel to each other") + + # Goal: blocks are stacked in an arch (second layer: blue). + self.add_goal(objs=blocks[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal="Place a blue block on top of the red blocks to form a basic arch") + + # Goal: blocks are stacked in an arch (third layer: red, red). + self.add_goal(objs=blocks[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, + language_goal="Place a red block on each side of the base arch") + + # Goal: blocks are stacked in an arch (top layer: blue). + self.add_goal(objs=blocks[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal="Bridge them with the last blue block") \ No newline at end of file diff --git a/cliport/generated_tasks/construct_corner_blocks.py b/cliport/generated_tasks/construct_corner_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..8910e5f28b27720962a5404d1bfccd0ccb2b5e43 --- /dev/null +++ b/cliport/generated_tasks/construct_corner_blocks.py @@ -0,0 +1,59 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ConstructCornerBlocks(Task): + """Create a corner structure using four blocks. Two red blocks form the base, one on each side of the corner, followed by a green block that is positioned on the red blocks at the corner junction, and finally a blue block on top of the green one. The overall structure forms a 3-D corner.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "Create a corner structure using four blocks. Two red blocks form the base, one on each side of the corner, followed by a green block that is positioned on the red blocks at the corner junction, and finally a blue block on top of the green one. The overall structure forms a 3-D corner." + self.task_completed_desc = "done constructing corner blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add corner. + corner_size = (0.15, 0.15, 0.05) + corner_urdf = 'corner/corner-template.urdf' + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, 'fixed') + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue']] + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0.05, 0.02), (0, 0, 0.06), (0, 0, 0.10)] + targs = [(utils.apply(corner_pose, i), corner_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a corner (bottom row: two red blocks). + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a corner (middle row: one green block). + self.add_goal(objs=blocks[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*1, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a corner (top row: one blue block). + self.add_goal(objs=blocks[3:], matches=np.ones((1, 1)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*1, + language_goal=self.lang_template) diff --git a/cliport/generated_tasks/construct_corner_building.py b/cliport/generated_tasks/construct_corner_building.py new file mode 100644 index 0000000000000000000000000000000000000000..a35bf198ff79c4a2ee817e9bbbec61e996732f9d --- /dev/null +++ b/cliport/generated_tasks/construct_corner_building.py @@ -0,0 +1,57 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import random +import pybullet as p + +class ConstructCornerBuilding(Task): + """Construct a building-like structure by placing four blocks of different colors + at each corner of a square and one block at the center. + Starting from the center, each block should be placed in a clockwise direction + in the following order: red, green, blue, orange, and yellow.""" + + def __init__(self): + super().__init__() + self.max_steps = 6 + self.lang_template = "construct a building-like structure by placing five blocks of different colors at each corner of a square and one block at the center. Starting from the center, each block should be placed in a clockwise direction in the following order: red, green, blue, orange, and yellow." + self.task_completed_desc = "done constructing corner building." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define block colors + colors = [utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['orange'], utils.COLORS['yellow']] + + # Define block size + block_size = (0.04, 0.04, 0.04) + + # Define block urdf + block_urdf = 'block/block.urdf' + + # Add blocks + body_block_urdf = "box/box-template.urdf" + body_block_urdf = self.fill_template(body_block_urdf, {'DIM': (0.10, 0.10, 0.04)}) + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(body_block_urdf, block_pose, color=colors[0]) + blocks = [block_id] + + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i+1]) + blocks.append(block_id) + + # Define target positions for blocks + center_pos = (0.5, 0, 0.02) + corner_positions = [(0.55, 0.05, 0.02), (0.45, 0.05, 0.02), (0.45, -0.05, 0.02), (0.55, -0.05, 0.02)] + target_positions = [center_pos] + corner_positions + + # Define target poses for blocks + target_poses = [(pos, p.getQuaternionFromEuler((0, 0, 0))) for pos in target_positions] + + # Add goals + for i in range(1,5): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[target_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/6, language_goal=self.lang_template) + self.add_goal(objs=[blocks[0]], matches=np.ones((1, 1)), targ_poses=[target_poses[0]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/6, language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/corner_block_challenge.py b/cliport/generated_tasks/corner_block_challenge.py new file mode 100644 index 0000000000000000000000000000000000000000..a2a9c1d450ad1864ecc88eeafa64f38a59185ac3 --- /dev/null +++ b/cliport/generated_tasks/corner_block_challenge.py @@ -0,0 +1,58 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CornerBlockChallenge(Task): + """Construct two columns using eight cubes - four red, two green, and two blue. + The columns should be constructed at two distinct marked corners of the tabletop + using the 'corner/corner-template.urdf' asset. The first column should be constructed + with the red cubes and the second column should use the green and blue cubes, + with blue at the base.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "construct two columns using eight cubes - four red, two green, and two blue" + self.task_completed_desc = "done constructing columns." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add corners. + corner_size = (0.15, 0.15, 0.01) + corner_urdf = 'corner/corner-template.urdf' + corner_poses = [] + for _ in range(2): + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, 'fixed') + corner_poses.append(corner_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red']] * 4 + [utils.COLORS['green']] * 2 + [utils.COLORS['blue']] * 2 + blocks = [] + for i in range(8): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Goal: each block is stacked in the correct corner in the correct order. + for i in range(4): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[corner_poses[0]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/8, + language_goal=self.lang_template) + + for i in range(4, 8): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[corner_poses[1]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/8, + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/corner_sort_cylinders.py b/cliport/generated_tasks/corner_sort_cylinders.py new file mode 100644 index 0000000000000000000000000000000000000000..c0d843727d55b757e5b8fe38e176c9c517456820 --- /dev/null +++ b/cliport/generated_tasks/corner_sort_cylinders.py @@ -0,0 +1,55 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CornerSortCylinders(Task): + """Pick up cylinders of four different colors (red, blue, green, yellow) and place them into four corners accordingly marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the {color} cylinder in the {color} corner" + self.task_completed_desc = "done sorting cylinders." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors + colors = ['red', 'blue', 'green', 'yellow'] + + # Add corners + corner_size = (0.04, 0.04, 0.04) # x, y, z dimensions for the asset size + corner_template = 'corner/corner-template.urdf' + corner_poses = [] + for color in colors: + replace = {'DIM': corner_size, 'HALF': (corner_size[0] / 2, corner_size[1] / 2, corner_size[2] / 2), 'COLOR': utils.COLORS[color]} + corner_urdf = self.fill_template(corner_template, replace) + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, 'fixed') + corner_poses.append(corner_pose) + + # Add cylinders + cylinder_size = (0.02, 0.02, 0.06) # x, y, z dimensions for the asset size + cylinder_template = 'cylinder/cylinder-template.urdf' + cylinders = [] + for color in colors: + replace = {'DIM': cylinder_size, 'HALF': (cylinder_size[0] / 2, cylinder_size[1] / 2, cylinder_size[2] / 2), 'COLOR': utils.COLORS[color]} + cylinder_urdf = self.fill_template(cylinder_template, replace) + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose) + cylinders.append(cylinder_id) + + # Add goals + for i in range(len(cylinders)): + self.add_goal(objs=[cylinders[i]], matches=np.int32([[1]]), targ_poses=[corner_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(cylinders), + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/create_pyramid_blocks_and_container.py b/cliport/generated_tasks/create_pyramid_blocks_and_container.py new file mode 100644 index 0000000000000000000000000000000000000000..1e388816bd66d21bff46513cc2506bd4acd81a54 --- /dev/null +++ b/cliport/generated_tasks/create_pyramid_blocks_and_container.py @@ -0,0 +1,68 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CreatePyramidBlocksAndContainer(Task): + """Create a pyramid structure using six blocks of three different colors (two red, two green, and two blue) inside a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "Create a pyramid structure using six blocks of three different colors (two red, two green, and two blue) inside a container." + self.task_completed_desc = "done creating pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + # x, y, z dimensions for the asset size + container_size = (0.3, 0.3, 0.1) + container_pose = self.get_random_pose(env, container_size) + container_urdf = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_urdf, replace) + env.add_object(container_urdf, container_pose, 'fixed') + self.add_corner_anchor_for_pose(env, container_pose) + + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['blue']] + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), (0, 0.05, 0.03), (0, -0.025, 0.08), (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(container_pose, i), container_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, green, blue). + self.add_goal(objs=blocks[2:5], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(blocks="the green and blue blocks", + row="bottom")) + + # Goal: blocks are stacked in a pyramid (middle row: red, red). + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, + language_goal=self.lang_template.format(blocks="the red blocks", + row="middle")) + + # Goal: blocks are stacked in a pyramid (top row: blue). + self.add_goal(objs=blocks[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=self.lang_template.format(blocks="the blue block", + row="top")) \ No newline at end of file diff --git a/cliport/generated_tasks/create_pyramid_with_color_coded_ells.py b/cliport/generated_tasks/create_pyramid_with_color_coded_ells.py new file mode 100644 index 0000000000000000000000000000000000000000..392494eb59d1a1c08ebd5698822e45f63c8dd021 --- /dev/null +++ b/cliport/generated_tasks/create_pyramid_with_color_coded_ells.py @@ -0,0 +1,58 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class CreatePyramidWithColorCodedElls(Task): + """Pick up ell-shaped objects of different colors and stack them onto a pallet in the shape of a pyramid.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "stack the {color} ell on the pyramid" + self.task_completed_desc = "done stacking ell pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + + # Ell colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], + utils.COLORS['yellow'], utils.COLORS['green'] + ] + color_names = ['red', 'blue', 'yellow', 'green'] + + # Add Ells. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + objs = [] + for i in range(4): + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=colors[i]) + objs.append(ell_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, 0, 0.08)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: Ells are stacked in a pyramid (bottom row: red, middle row: blue, top row: yellow, green). + for i in range(4): + self.add_goal(objs=[objs[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template.format(color=color_names[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/cylinder_balancing_and_placement.py b/cliport/generated_tasks/cylinder_balancing_and_placement.py new file mode 100644 index 0000000000000000000000000000000000000000..abaf1c85c498199faa7090445f0f173bdcc13fd8 --- /dev/null +++ b/cliport/generated_tasks/cylinder_balancing_and_placement.py @@ -0,0 +1,49 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CylinderBalancingAndPlacement(Task): + """Pick up each cylinder and balance it on its end at the center of the corresponding colored zone.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "balance the {color} cylinder in the {color} zone" + self.task_completed_desc = "done balancing and placing cylinders." + self.colors = ['red', 'green', 'blue'] + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for color in self.colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.12) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for color in self.colors: + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=utils.COLORS[color]) + cylinders.append(cylinder_id) + + # Goal: each cylinder is balanced in the corresponding colored zone. + for i in range(len(cylinders)): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/3, + language_goal=self.lang_template.format(color=self.colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/cylinder_ring_stack.py b/cliport/generated_tasks/cylinder_ring_stack.py new file mode 100644 index 0000000000000000000000000000000000000000..557da0b8bd880dc808447d29e9f314e20b719b23 --- /dev/null +++ b/cliport/generated_tasks/cylinder_ring_stack.py @@ -0,0 +1,62 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CylinderRingStack(Task): + """Pick up each block and stack it on top of the corresponding colored cylinder. + Each cylinder and block pair should be stacked inside a differently colored container.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "stack the {color} block on the {color} cylinder in the {container_color} container" + self.task_completed_desc = "done stacking." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for cylinders, blocks and containers + colors = ['red', 'blue', 'green', 'yellow'] + container_colors = ['blue', 'green', 'yellow', 'red'] + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=utils.COLORS[colors[i]]) + cylinders.append(cylinder_id) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[colors[i]]) + blocks.append(block_id) + + # Add containers. + container_size = (0.12, 0.12, 0.12) + container_urdf = 'container/container-template.urdf' + containers = [] + for i in range(4): + container_pose = self.get_random_pose(env, container_size) + container_id = env.add_object(container_urdf, container_pose, color=utils.COLORS[container_colors[i]]) + containers.append(container_id) + + # Goal: each block is stacked on the corresponding colored cylinder inside a differently colored container. + for i in range(4): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[cylinder_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i], container_color=container_colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/cylinder_stand_alignment.py b/cliport/generated_tasks/cylinder_stand_alignment.py new file mode 100644 index 0000000000000000000000000000000000000000..7c5bda6db24e6541249a47894f7c3ae6d17a0df1 --- /dev/null +++ b/cliport/generated_tasks/cylinder_stand_alignment.py @@ -0,0 +1,58 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CylinderStandAlignment(Task): + """Arrange four colored cylinders (red, blue, green, yellow) in order of their colors on four stands of matching color.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "Arrange the {color} cylinder on the {color} stand" + self.task_completed_desc = "done arranging cylinders." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and corresponding names + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + color_names = ['red', 'blue', 'green', 'yellow'] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + replace = {'DIM': cylinder_size, 'HALF': (cylinder_size[0] / 2, cylinder_size[1] / 2, cylinder_size[2] / 2), + 'COLOR': colors[i]} + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(cylinder_urdf, replace) + cylinder_id = env.add_object(urdf, cylinder_pose) + cylinders.append(cylinder_id) + + # Add stands. + # x, y, z dimensions for the asset size + stand_size = (0.05, 0.05, 0.005) + stand_urdf = 'stacking/stand.urdf' + stands = [] + for i in range(4): + stand_pose = self.get_random_pose(env, stand_size) + env.add_object(stand_urdf, stand_pose, color=colors[i], category='fixed') + stands.append(stand_pose) + + # Goal: each cylinder is on a stand of the same color. + for i in range(4): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[stands[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, + language_goal=self.lang_template.format(color=color_names[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/guided_block_path.py b/cliport/generated_tasks/guided_block_path.py new file mode 100644 index 0000000000000000000000000000000000000000..69936e23bad734ba6ce765f82b726d654914d19e --- /dev/null +++ b/cliport/generated_tasks/guided_block_path.py @@ -0,0 +1,54 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class GuidedBlockPath(Task): + """Pick up each block and move it along the line of the same color from start to end.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "move the {color} block along the {color} line from start to end" + self.task_completed_desc = "done moving blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and corresponding names + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + color_names = ['red', 'blue', 'green', 'yellow'] + + # Add lines and blocks. + # x, y, z dimensions for the asset size + line_size = (0.3, 0.01, 0.01) + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + line_urdf = 'line/line-template.urdf' + + blocks = [] + lines = [] + for i in range(4): + # Add line + line_pose = self.get_random_pose(env, line_size) + env.add_object(line_urdf, line_pose, color=colors[i], category='fixed') + lines.append(line_pose) + + # Add block + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Add goal + self.add_goal(objs=[block_id], matches=np.ones((1, 1)), targ_poses=[line_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=color_names[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/insert_blocks_lineup.py b/cliport/generated_tasks/insert_blocks_lineup.py new file mode 100644 index 0000000000000000000000000000000000000000..5dc083718ced899b8599185e4caf6150416ec984 --- /dev/null +++ b/cliport/generated_tasks/insert_blocks_lineup.py @@ -0,0 +1,58 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertBlocksLineup(Task): + """Pick up four different color blocks and insert them into the corresponding color fixtures.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "insert the {color} block into the {color} fixture" + self.task_completed_desc = "done inserting blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for blocks and fixtures + colors = ['red', 'blue', 'green', 'yellow'] + + # Add fixtures. + fixture_size = (0.04, 0.04, 0.04) + fixture_urdf = 'insertion/fixture.urdf' + fixture_poses = [] + for i in range(4): + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[colors[i]], category='fixed') + fixture_poses.append((fixture_pose, fixture_id)) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[colors[i]]) + blocks.append(block_id) + + # Add small blocks as barriers. + small_block_size = (0.02, 0.02, 0.02) + small_block_urdf = 'block/small.urdf' + for _ in range(10): + small_block_pose = self.get_random_pose(env, small_block_size) + env.add_object(small_block_urdf, small_block_pose) + + # Goal: each block is in the corresponding color fixture. + for i in range(4): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[fixture_poses[i][0]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/insert_ell_along_square_path.py b/cliport/generated_tasks/insert_ell_along_square_path.py new file mode 100644 index 0000000000000000000000000000000000000000..b1dbce4c230b14bbfdd888392a1727673addd7c7 --- /dev/null +++ b/cliport/generated_tasks/insert_ell_along_square_path.py @@ -0,0 +1,59 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class InsertEllAlongSquarePath(Task): + """Pick up each ell block and insert it into the fixture of the same color. However, the robot must move each ell block along the marked square path to reach the fixture.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "move the {color} ell block into the {color} fixture" + self.task_completed_desc = "done inserting ell blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Ell block colors. + colors = ['red', 'blue', 'green', 'yellow'] + + # Add ell blocks and fixtures. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + fixture_urdf = 'insertion/fixture.urdf' + ell_blocks = [] + fixtures = [] + for color in colors: + # Add ell block + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[color]) + ell_blocks.append(ell_id) + + # Add fixture + fixture_pose = self.get_random_pose(env, ell_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color]) + fixtures.append(fixture_id) + + # Goal: each ell block is inserted into the fixture of the same color. + for i in range(len(colors)): + self.add_goal(objs=[ell_blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(colors), + language_goal=self.lang_template.format(color=colors[i])) + + # Add square path marked by small blocks. + path_block_size = (0.02, 0.02, 0.02) + path_block_urdf = 'block/small.urdf' + path_block_color = utils.COLORS['gray'] + for _ in range(16): + path_block_pose = self.get_random_pose(env, path_block_size) + env.add_object(path_block_urdf, path_block_pose, color=path_block_color) \ No newline at end of file diff --git a/cliport/generated_tasks/insert_sphere_into_container.py b/cliport/generated_tasks/insert_sphere_into_container.py new file mode 100644 index 0000000000000000000000000000000000000000..5f53b5f60eb563f15fcc4b6c0539700df6ec9d71 --- /dev/null +++ b/cliport/generated_tasks/insert_sphere_into_container.py @@ -0,0 +1,46 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertSphereIntoContainer(Task): + """Pick up a blue sphere and place it into an open container.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "pick up a blue sphere and place it into an open container" + self.task_completed_desc = "done inserting sphere into container." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + # x, y, z dimensions for the asset size + container_size = (0.1, 0.1, 0.1) + container_pose = self.get_random_pose(env, container_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + container_id = env.add_object(container_urdf, container_pose, 'fixed') + + # Add sphere. + # x, y, z dimensions for the asset size + sphere_size = (0.04, 0.04, 0.04) + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_urdf = 'sphere/sphere.urdf' + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS['blue']) + + # Goal: the blue sphere is in the container. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/insertion_in_color_sequenced_zones.py b/cliport/generated_tasks/insertion_in_color_sequenced_zones.py new file mode 100644 index 0000000000000000000000000000000000000000..c865dd66b1ee50c5e3e05feb1c8460d80b0757ad --- /dev/null +++ b/cliport/generated_tasks/insertion_in_color_sequenced_zones.py @@ -0,0 +1,51 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertionInColorSequencedZones(Task): + """Pick up each ell and place it in the zone of the same color, in the specific sequence of red, blue, green, and yellow from left to right.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the {color} ell in the {color} zone" + self.task_completed_desc = "done placing ells in color sequenced zones." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Ell colors. + colors = ['red', 'blue', 'green', 'yellow'] + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for i in range(4): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[colors[i]]) + zone_poses.append(zone_pose) + + # Add ells. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + ells = [] + for i in range(4): + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[colors[i]]) + ells.append(ell_id) + + # Goal: each ell is in the zone of the same color. + for i in range(4): + self.add_goal(objs=[ells[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/kit_in_bowl_in_zone.py b/cliport/generated_tasks/kit_in_bowl_in_zone.py new file mode 100644 index 0000000000000000000000000000000000000000..3e5110ea2f8c8336c947653226c5808b4d5bb00a --- /dev/null +++ b/cliport/generated_tasks/kit_in_bowl_in_zone.py @@ -0,0 +1,63 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import os + +class KitInBowlInZone(Task): + """Pick up each kit and place it on the corresponding colored bowl, which are located in specific positions on a zone.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "place the {} bowl on the zone" + self.lang_template_2 = "place the {} on the {} bowl" + + self.task_completed_desc = "done placing kits on bowls and bowl on zone." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zone. + zone_size = (0.2, 0.2, 0.01) + zone_pose = self.get_random_pose(env, zone_size) + zone_urdf = 'zone/zone.urdf' + env.add_object(zone_urdf, zone_pose, 'fixed') + + # Define colors. + kit_colors = ['red'] + bowl_colors = ['blue'] + + # Add bowls. + bowl_size = (0.04, 0.04, 0.06) + bowls = [] + bowl_urdf = 'bowl/bowl.urdf' + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_id = env.add_object(bowl_urdf, bowl_pose) + bowls.append(bowl_id) + + # Add kits. + kit_size = utils.map_kit_scale((0.03, 0.03, 0.02)) + obj_shapes = self.get_kitting_shapes(1) + shape = os.path.join(self.assets_root, 'kitting', + f'{obj_shapes[0]:02d}.obj') + template = 'kitting/object-template.urdf' + replace = {'FNAME': (shape,), 'SCALE': kit_size, 'COLOR': kit_colors[0]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + kit_urdf = self.fill_template(template, replace) + kits = [] + kit_pose = self.get_random_pose(env, kit_size) + kit_id = env.add_object(kit_urdf, kit_pose, color=bowl_colors[0]) + kits.append(kit_id) + + # Goal: place the bowl on top of the zone + self.add_goal(objs=[bowls[0]], matches=np.ones((1, 1)), targ_poses=[zone_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=self.lang_template.format(bowl_colors[0])) + + + # Goal: place the kit on top of the bowl + pick_name = kit_colors[0] + " " + utils.assembling_kit_shapes[obj_shapes[0]] + language_goal = self.lang_template_2.format(pick_name, bowl_colors[0]) + self.add_goal(objs=[kits[0]], matches=np.ones((1, 1)), targ_poses=[zone_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=language_goal) diff --git a/cliport/generated_tasks/manipulating_two_ropes.py b/cliport/generated_tasks/manipulating_two_ropes.py new file mode 100644 index 0000000000000000000000000000000000000000..1b55a14b5a5a6298f493f83441538405d6efe38a --- /dev/null +++ b/cliport/generated_tasks/manipulating_two_ropes.py @@ -0,0 +1,55 @@ +import numpy as np +import os +import pybullet as p +import random + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula + +class ManipulatingTwoRopes(Task): + """rearrange the red and blue deformable ropes such that it connects the two endpoints of a 3-sided square of corresponding color.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "rearrange the {color_name} rope such that it connects the two endpoints of a 3-sided square of corresponding color." + self.task_completed_desc = "done manipulating two ropes." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + n_parts = 20 + radius = 0.005 + length = 2 * radius * n_parts * np.sqrt(2) + + # Add 3-sided square for the red rope. + color_list = ['red', 'blue'] + for color_name in color_list: + square_size = (length, length, 0) + square_pose = self.get_random_pose(env, square_size) + square_template = 'square/square-template.urdf' + + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + replace = {'DIM': (length,), 'HALF': (np.float32(length) / 2 - 0.005,)} + urdf = self.fill_template(square_template, replace) + env.add_object(urdf, square_pose, 'fixed', color=utils.COLORS[color_name]) + + # compute corners + corner0 = (length / 2, length / 2, 0.001) + corner1 = (-length / 2, length / 2, 0.001) + corner_0 = utils.apply(square_pose, corner0) + corner_1 = utils.apply(square_pose, corner1) + + # IMPORTANT: use `make_ropes` to add cable (series of articulated small blocks). + objects, targets, matches = self.make_ropes(env, corners=(corner_0, corner_1), color_name=color_name) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1. / len(color_list), + language_goal=self.lang_template.format(color_name=color_name)) + + print(f"len of languages: {len(self.lang_goals)} obj:{len(objects)}") + for i in range(480): + p.stepSimulation() diff --git a/cliport/generated_tasks/mix_piles.py b/cliport/generated_tasks/mix_piles.py new file mode 100644 index 0000000000000000000000000000000000000000..0e1c26f121dfbdc83ca1d74dce50562431ef1c83 --- /dev/null +++ b/cliport/generated_tasks/mix_piles.py @@ -0,0 +1,42 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula + +class MixPiles(Task): + """Create two separate piles of ten blocks with different colors. Then, push them into a zone.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.num_blocks = 10 + self.lang_template = "create two separate piles of ten blocks with different colors. Then, push them into a zone." + self.task_completed_desc = "done mixing piles." + self.ee = Spatula + self.primitive = primitives.push + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Get two random colors of piles + sample_colors, _ = utils.get_colors(self.mode, n_colors=2) + + # Add piles 1. + piles1 = self.make_piles(env, block_color=sample_colors[0]) + + # Add piles 2. + piles2 = self.make_piles(env, block_color=sample_colors[1]) + + # Goal: each block is in the goal zone, alternating between red and blue. + blocks = piles1 + piles2 + matches = np.ones((len(blocks), 1)) + self.add_goal(objs=blocks, matches=matches, targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, + language_goal=self.lang_template) diff --git a/cliport/generated_tasks/mixed_color_block_barrier_insertion.py b/cliport/generated_tasks/mixed_color_block_barrier_insertion.py new file mode 100644 index 0000000000000000000000000000000000000000..9b270892a64a53185e9c3c7caa6b22607bc77d53 --- /dev/null +++ b/cliport/generated_tasks/mixed_color_block_barrier_insertion.py @@ -0,0 +1,59 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class MixedColorBlockBarrierInsertion(Task): + """Pick up each colored block, navigate the barriers, and insert each block into the fixture of the same color.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "insert the {color} block into the {color} fixture" + self.task_completed_desc = "done inserting blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for blocks and fixtures + colors = ['red', 'blue', 'green', 'yellow'] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for color in colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add fixtures. + fixture_size = (0.06, 0.06, 0.06) + fixture_urdf = 'insertion/fixture.urdf' + fixtures = [] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color]) + fixtures.append(fixture_id) + + # Add barriers. + barrier_size = (0.12, 0.04, 0.04) + barrier_colors = ['orange', 'purple', 'brown'] + for _ in range(2): + for color in barrier_colors: + barrier_pose = self.get_random_pose(env, barrier_size) + env.add_object(block_urdf, barrier_pose, color=utils.COLORS[color]) + + # Goal: each block is inserted into the fixture of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/move_bowl_from_pallet_to_corner.py b/cliport/generated_tasks/move_bowl_from_pallet_to_corner.py new file mode 100644 index 0000000000000000000000000000000000000000..86f6efc9173310751cd203d1fbaadf7d70dc56b0 --- /dev/null +++ b/cliport/generated_tasks/move_bowl_from_pallet_to_corner.py @@ -0,0 +1,59 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import random +import pybullet as p +import os +import copy + +class MoveBowlFromPalletToCorner(Task): + """Place the specific bowl from a pallet to a corner.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the {pick} from pallet to {place} corner." + self.task_completed_desc = "done placing bowl around corner." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + n_pallets = 3 + n_objects = 3 + colors, color_names = utils.get_colors(mode=self.mode, n_colors=n_objects) + # Add pallets and objects + # x, y, z dimensions for the asset size + corner_size = (0.12, 0.12, 0) + corner_urdf = 'corner/corner-template.urdf' + corner_poses = [] + + pallet_size = (0.06, 0.06, 0) + pallet_urdf = 'pallet/pallet.urdf' + pallet_poses = [] + objects_ids = [] + bowl_shapes = self.get_kitting_shapes(n_objects) + + for i in range(n_pallets): + # add pallet + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_id = env.add_object(pallet_urdf, pallet_pose, category='fixed', color=colors[i]) + pallet_poses.append(pallet_pose) + + # add kit + bowl_urdf = 'bowl/bowl.urdf' + bowl_pose = pallet_pose + bowl_id = env.add_object(bowl_urdf, bowl_pose, color=colors[i]) + objects_ids.append(bowl_id) + + # add corner + corner_pose = self.get_random_pose(env, pallet_size) + corner_id = env.add_object(corner_urdf, corner_pose, category='fixed', color=colors[i]) + corner_poses.append(corner_pose) + + # Goal: put a specific kit from a pallet to the top of a corner + target_idx = np.random.randint(n_pallets) + pick_name = color_names[target_idx] + " " + 'bowl' + language_goal = (self.lang_template.format(pick=pick_name, place=color_names[target_idx])) + self.add_goal(objs=[objects_ids[target_idx]], matches=np.ones((1, 1)), targ_poses=[corner_poses[target_idx]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / n_objects, language_goal=language_goal) diff --git a/cliport/generated_tasks/move_kit_from_zone_to_cylinder.py b/cliport/generated_tasks/move_kit_from_zone_to_cylinder.py new file mode 100644 index 0000000000000000000000000000000000000000..c079d8d321bba49e757d1ee950e5a77aa91ea25e --- /dev/null +++ b/cliport/generated_tasks/move_kit_from_zone_to_cylinder.py @@ -0,0 +1,70 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import random +import pybullet as p +import os +import copy + +class MoveKitFromZoneToCylinder(Task): + """Place the specific kit from a zone to a cylinder.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the {pick} from zone to {place} cylinder." + self.task_completed_desc = "done placing kit in zones." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + n_zones = 3 + n_objects = 3 + colors, color_names = utils.get_colors(mode=self.mode, n_colors=n_objects) + + # Add zones and objects + # x, y, z dimensions for the asset size + cylinder_size = (0.12, 0.12, 0) + cylinder_template = 'cylinder/cylinder-template.urdf' + cylinder_poses = [] + + zone_size = (0.06, 0.06, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + objects_ids = [] + obj_shapes = self.get_kitting_shapes(n_objects) + + for i in range(n_zones): + # add zone + zone_pose = self.get_random_pose(env, zone_size) + zone_id = env.add_object(zone_urdf, zone_pose, category='fixed', color=colors[i]) + zone_poses.append(zone_pose) + + # add kit + scale = utils.map_kit_scale((0.03, 0.03, 0.02)) + shape = os.path.join(self.assets_root, 'kitting', + f'{obj_shapes[i]:02d}.obj') + template = 'kitting/object-template.urdf' + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + obj_pose = zone_pose + obj_id = env.add_object(urdf, obj_pose) + objects_ids.append(obj_id) + + # add cylinder + cylinder_pose = self.get_random_pose(env, zone_size) + template = 'kitting/object-template.urdf' + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': colors[i]} + cylinder_urdf = self.fill_template(cylinder_template, replace) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, category='fixed', color=colors[i]) + cylinder_poses.append(cylinder_pose) + + # Goal: put a specific kit from a zone to the top of a cylinder + target_idx = np.random.randint(n_zones) + pick_name = color_names[target_idx] + " " + utils.assembling_kit_shapes[obj_shapes[target_idx]] + language_goal = (self.lang_template.format(pick=pick_name, place=color_names[target_idx])) + self.add_goal(objs=[objects_ids[target_idx]], matches=np.ones((1, 1)), targ_poses=[cylinder_poses[target_idx]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / n_objects, language_goal=language_goal) diff --git a/cliport/generated_tasks/move_piles_along_line.py b/cliport/generated_tasks/move_piles_along_line.py new file mode 100644 index 0000000000000000000000000000000000000000..b3963dfaa5d7551149c72ce8fe759393424fbd66 --- /dev/null +++ b/cliport/generated_tasks/move_piles_along_line.py @@ -0,0 +1,70 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class MovePilesAlongLine(Task): + """Move three piles of small blocks, each pile a different color (red, blue, green), + along three matching colored lines to three separate zones of the same color using a spatula.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "move the piles of blocks along the lines to the matching colored zones" + self.task_completed_desc = "done moving piles." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add three colored lines. + line_template = 'line/line-template.urdf' + line_colors = ['red', 'blue', 'green'] + line_poses = [] + for color in line_colors: + line_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + line_pose = self.get_random_pose(env, line_size) + replace = {'DIM': line_size, 'HALF': (line_size[0] / 2, line_size[1] / 2, line_size[2] / 2), 'COLOR': color} + line_urdf = self.fill_template(line_template, replace) + env.add_object(line_urdf, line_pose, 'fixed') + line_poses.append(line_pose) + + # Add three colored zones. + zone_template = 'zone/zone.urdf' + zone_poses = [] + for color in line_colors: + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2), 'COLOR': color} + zone_urdf = self.fill_template(zone_template, replace) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Add three piles of small blocks. + block_template = 'block/small.urdf' + block_colors = ['red', 'blue', 'green'] + block_ids = [] + for color in block_colors: + block_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + block_pose = self.get_random_pose(env, block_size) + replace = {'DIM': block_size, 'HALF': (block_size[0] / 2, block_size[1] / 2, block_size[2] / 2), 'COLOR': color} + block_urdf = self.fill_template(block_template, replace) + block_id = env.add_object(block_urdf, block_pose) + block_ids.append(block_id) + + # Add goals. + for i in range(3): + self.add_goal(objs=[block_ids[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i]], replace=False, + rotations=False, metric='zone', params=[(zone_poses[i], zone_size)], step_max_reward=1/3, + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/multi_level_block_construction.py b/cliport/generated_tasks/multi_level_block_construction.py new file mode 100644 index 0000000000000000000000000000000000000000..55e77b307c40ee83765f9e5dc1a22f4bc2f522b0 --- /dev/null +++ b/cliport/generated_tasks/multi_level_block_construction.py @@ -0,0 +1,57 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class MultiLevelBlockConstruction(Task): + """Construct a two-level structure on a pallet using four blocks: two red and two blue. + The lower level should be a rectangle created by placing the red blocks side by side. + The upper level is made up by placing the blue blocks placed on top of the red blocks + creating a line aligned perpendicular to the red blocks.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "construct a two-level structure on a pallet using four blocks: two red and two blue" + self.task_completed_desc = "done constructing multi-level block structure." + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.015) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['blue']] + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.02, 0.02), (0, 0.02, 0.02), + (0, -0.02, 0.06), (0, 0.02, 0.06)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: red blocks are placed side by side on the pallet. + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, language_goal=self.lang_template) + + # Goal: blue blocks are stacked on top of the red blocks. + self.add_goal(objs=blocks[2:], matches=np.ones((2, 2)), targ_poses=targs[2:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/multi_level_insertion_and_zone_matching.py b/cliport/generated_tasks/multi_level_insertion_and_zone_matching.py new file mode 100644 index 0000000000000000000000000000000000000000..0c17cff964a97499949da41310fda25e38a41e35 --- /dev/null +++ b/cliport/generated_tasks/multi_level_insertion_and_zone_matching.py @@ -0,0 +1,51 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class MultiLevelInsertionAndZoneMatching(Task): + """Pick up ell objects from their current position and insert them into the corresponding colored zone on the same level, in a specific order - large, medium, and small.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "insert the {size} {color} ell into the {color} zone on the same level" + self.task_completed_desc = "done inserting." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_sizes = [(0.12, 0.12, 0), (0.12, 0.12, 0.05), (0.12, 0.12, 0.1)] + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + zone_colors = ['red', 'blue', 'green'] + for i in range(3): + zone_pose = self.get_random_pose(env, zone_sizes[i]) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[zone_colors[i]]) + zone_poses.append(zone_pose) + + # Add ell objects. + ell_sizes = [(0.08, 0.08, 0.02), (0.06, 0.06, 0.015), (0.04, 0.04, 0.01)] + ell_urdf = 'insertion/ell.urdf' + ells = [] + for i in range(3): + for j in range(3): + ell_pose = self.get_random_pose(env, ell_sizes[j]) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[zone_colors[i]]) + ells.append(ell_id) + + # Goal: each ell object is in the corresponding colored zone on the same level. + for i in range(9): + self.add_goal(objs=[ells[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i//3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/9, + language_goal=self.lang_template.format(size=['large', 'medium', 'small'][i%3], color=zone_colors[i//3])) \ No newline at end of file diff --git a/cliport/generated_tasks/multi_level_pyramid_construction.py b/cliport/generated_tasks/multi_level_pyramid_construction.py new file mode 100644 index 0000000000000000000000000000000000000000..49940804ac8a3aef2207ad37f541e8a10a011d65 --- /dev/null +++ b/cliport/generated_tasks/multi_level_pyramid_construction.py @@ -0,0 +1,56 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class MultiLevelPyramidConstruction(Task): + """Construct a two-level pyramid on a pallet using six blocks: three green and three blue.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "Construct a two-level pyramid on a pallet using six blocks: three green and three blue. The first level should be a triangle created by placing the green blocks side by side. The second level should be built by placing the blue blocks on top of the green blocks, forming another triangle rotated 60 degrees with respect to the first one." + self.task_completed_desc = "done constructing pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.35, 0.35, 0.01) # x, y, z dimensions for the pallet size + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_urdf = 'pallet/pallet.urdf' + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add blocks. + block_size = (0.04, 0.04, 0.04) # x, y, z dimensions for the block size + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['green']] * 3 + [utils.COLORS['blue']] * 3 # three green and three blue blocks + + blocks = [] + for color in block_colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0, 0.02), (0, 0.05, 0.02), # first level + (0, -0.025, 0.06), (0, 0.025, 0.06), (0, 0, 0.10)] # second level + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (first level: green blocks). + self.add_goal(objs=blocks[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(blocks="the green blocks", row="bottom")) + + # Goal: blocks are stacked in a pyramid (second level: blue blocks). + self.add_goal(objs=blocks[3:], matches=np.ones((3, 3)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(blocks="the blue blocks", row="top")) \ No newline at end of file diff --git a/cliport/generated_tasks/multicolor_block_bridge.py b/cliport/generated_tasks/multicolor_block_bridge.py new file mode 100644 index 0000000000000000000000000000000000000000..8c40472513c445e02bdac4ab4f45160f25961481 --- /dev/null +++ b/cliport/generated_tasks/multicolor_block_bridge.py @@ -0,0 +1,73 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class MulticolorBlockBridge(Task): + """Build a bridge by stacking three red, three blue, and three green blocks on a pallet. + Arrange in a sequence from left to right: red, blue, and green. + Then, place three cylinders of corresponding colors on top of the stacked blocks, forming a bridge. + The cylinders should roll from the top block to the pallet, creating a challenge of precision and control.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "Build a bridge by stacking three red, three blue, and three green blocks on a pallet. Arrange in a sequence from left to right: red, blue, and green. Then, place three cylinders of corresponding colors on top of the stacked blocks, forming a bridge." + self.task_completed_desc = "done building the bridge." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green']] + blocks = [] + for i in range(9): # 3 blocks of each color + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i // 3]) + blocks.append(block_id) + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.04) + cylinder_template = 'cylinder/cylinder-template.urdf' + cylinders = [] + for i in range(3): # 1 cylinder of each color + cylinder_pose = self.get_random_pose(env, cylinder_size) + replace = {'DIM': cylinder_size, 'HALF': (cylinder_size[0] / 2, cylinder_size[1] / 2, cylinder_size[2] / 2)} + cylinder_urdf = self.fill_template(cylinder_template, replace) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=block_colors[i]) + cylinders.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), (0, 0.05, 0.03)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked on the pallet in the order red, blue, green. + for i in range(9): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targs[i // 3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 9, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: cylinders are placed on top of the stacked blocks. + for i in range(3): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/place_ball_in_elevated_bowl.py b/cliport/generated_tasks/place_ball_in_elevated_bowl.py new file mode 100644 index 0000000000000000000000000000000000000000..019c9ff091c9954a4ccf33a3220f182e7e02fe7b --- /dev/null +++ b/cliport/generated_tasks/place_ball_in_elevated_bowl.py @@ -0,0 +1,53 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class PlaceBallInElevatedBowl(Task): + """Pick up a red ball and carefully place it into a bowl, which is positioned on a raised platform that is surrounded by small blocks.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "place the red ball in the elevated bowl" + self.task_completed_desc = "done placing ball in bowl." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add elevated platform. + platform_size = (0.3, 0.3, 0.05) + + # Add bowl on the platform. + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_pose[0][2] += platform_size[2] # place the bowl on top of the platform + bowl_id = env.add_object(bowl_urdf, bowl_pose, 'fixed') + + # Add red ball. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS['red']) + + # Add small blocks around the platform. + block_size = (0.02, 0.02, 0.02) + block_urdf = 'block/small.urdf' + for _ in range(5): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose) + + # Goal: the red ball is in the bowl. + self.add_goal(objs=[ball_id], matches=np.ones((1, 1)), targ_poses=[bowl_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/place_blue_on_line_ends.py b/cliport/generated_tasks/place_blue_on_line_ends.py new file mode 100644 index 0000000000000000000000000000000000000000..27315d5da9c10ad40d55d4ae740c332b0a57d8cf --- /dev/null +++ b/cliport/generated_tasks/place_blue_on_line_ends.py @@ -0,0 +1,47 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class PlaceBlueOnLineEnds(Task): + """Pick up each blue box and accurately place it at the end of a green line.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "place the blue box at the end of the green line" + self.task_completed_desc = "done placing blue boxes on line ends." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add lines. + line_size = (0.3, 0.01, 0.01) + line_template = 'line/line-template.urdf' + replace = {'DIM': line_size} + line_urdf = self.fill_template(line_template, replace) + + line_colors = ['green'] + line_poses = [] + + line_pose = self.get_random_pose(env, line_size) + color = utils.COLORS[line_colors[0]] + env.add_object(line_urdf, line_pose, 'fixed', color=color) + line_poses.append(utils.apply(line_pose, (-0.15,0,0))) + line_poses.append(utils.apply(line_pose, (0.15,0,0))) + + # Add blue boxes. + box_size = (0.04, 0.04, 0.04) + box_urdf = 'box/box-template.urdf' + box_color = utils.COLORS['blue'] + boxes = [] + for _ in range(2): + box_pose = self.get_random_pose(env, box_size) + box_id = env.add_object(box_urdf, box_pose, color=box_color) + boxes.append(box_id) + + # Goal: each blue box is at the end of a different colored line. + for i in range(2): + language_goal = self.lang_template.format(line_colors[0]) + self.add_goal(objs=[boxes[i]], matches=np.ones((1, 1)), targ_poses=[line_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, language_goal=language_goal) diff --git a/cliport/generated_tasks/push_piles_into_letter.py b/cliport/generated_tasks/push_piles_into_letter.py new file mode 100644 index 0000000000000000000000000000000000000000..7ab3ac594da79c6f32ffb6acfb5184045fc55689 --- /dev/null +++ b/cliport/generated_tasks/push_piles_into_letter.py @@ -0,0 +1,47 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import IPython + +class PushPilesIntoLetter(Task): + """Push piles of small objects into a target goal zone shaped in some letters.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks fill in the green shape" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + num_blocks = 50 + + # Add the target letter + rand_letter = self.get_kitting_shapes(n_objects=1)[0] + shape = os.path.join(self.assets_root, 'kitting', + f'{rand_letter:02d}.obj') + zone_pose = self.get_random_pose(env, (0.2,0.2,0.01)) + scale = [0.006, 0.006, 0.00001] # .0005 + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': [0.2, 0.5, 0.2]} # green color + template = 'kitting/object-template-nocollision.urdf' + urdf = self.fill_template(template, replace) + letter_zone = env.add_object(urdf, zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Sample point from the object shape as the target poses for the piles + target_poses = self.get_target_sample_surface_points(shape, scale, zone_pose, num_points=num_blocks) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((num_blocks, num_blocks)), targ_poses=target_poses, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=2, + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/put_blocks_between_zones.py b/cliport/generated_tasks/put_blocks_between_zones.py new file mode 100644 index 0000000000000000000000000000000000000000..a644f66a71bd1db131174e3fd4741b2efc6e67b1 --- /dev/null +++ b/cliport/generated_tasks/put_blocks_between_zones.py @@ -0,0 +1,51 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import copy + +class PutBlocksBetweenZones(Task): + """Arrange four differently colored blocks (red, blue, green, and yellow) between two designated zones on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "Arrange the blocks between the zones in the order: red, blue, green, yellow" + self.task_completed_desc = "done arranging blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone1_pose = self.get_random_pose(env, zone_size) + zone2_pose = copy.deepcopy(zone1_pose) + zone2_pose = (utils.apply(zone1_pose, (0,0.1,0)), zone2_pose[1]) + env.add_object(zone_urdf, zone1_pose, 'fixed') + env.add_object(zone_urdf, zone2_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Goal: blocks are arranged between the zones in the order: red, blue, green, yellow. + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, 0.1, 0.03)] + targs = [(utils.apply(zone1_pose, i), zone1_pose[1]) for i in place_pos] + + # Add goal + self.add_goal(objs=blocks, matches=np.ones((4, 4)), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, symmetries=[np.pi/2]*4, language_goal=self.lang_template) diff --git a/cliport/generated_tasks/put_blues_around_red.py b/cliport/generated_tasks/put_blues_around_red.py new file mode 100644 index 0000000000000000000000000000000000000000..9c8baa97589135bf3a025971389364404b411ce0 --- /dev/null +++ b/cliport/generated_tasks/put_blues_around_red.py @@ -0,0 +1,47 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class PlaceBluesAroundRed(Task): + """Pick up the blue blocks one by one and place them around the red block, forming a circle.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "place the blue blocks around the red block" + self.task_completed_desc = "done placing blue blocks around red block." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add red block. + red_block_size = (0.04, 0.04, 0.04) + red_block_urdf = 'block/block_for_anchors.urdf' + red_block_pose = self.get_random_pose(env, red_block_size) + red_block_id = env.add_object(red_block_urdf, red_block_pose, 'fixed') + + # Add blue blocks. + blue_blocks = [] + blue_block_size = (0.02, 0.02, 0.02) + blue_block_urdf = 'block/block_for_anchors.urdf' + N = 4 + + for _ in range(N): + blue_block_pose = self.get_random_pose(env, blue_block_size) + blue_block_id = env.add_object(blue_block_urdf, blue_block_pose, color=utils.COLORS['blue']) + blue_blocks.append(blue_block_id) + + # Calculate target poses for blue blocks to form a circle around the red block. + radius = 0.06 # radius of the circle + angles = np.linspace(0, 2*np.pi, N, endpoint=False) # angles for each blue block + targ_poses = [] + for angle in angles: + x = red_block_pose[0][0] + radius * np.cos(angle) + y = red_block_pose[0][1] + radius * np.sin(angle) + z = red_block_pose[0][2] + targ_poses.append(((x, y, z), red_block_pose[1])) + + # Add goal. + self.add_goal(objs=blue_blocks, matches=np.eye(N), targ_poses=targ_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1., language_goal=self.lang_template) diff --git a/cliport/generated_tasks/put_kit_in_bowl.py b/cliport/generated_tasks/put_kit_in_bowl.py new file mode 100644 index 0000000000000000000000000000000000000000..3cdd9969de32521e33a68418e05d6d30053771a2 --- /dev/null +++ b/cliport/generated_tasks/put_kit_in_bowl.py @@ -0,0 +1,80 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import random +import pybullet as p +import os + + +class PutKitInBowl(Task): + """Place the specific kit in a bowl of specified color.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the {pick} in a {place} bowl" + self.task_completed_desc = "done placing kit in bowls." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_objects = np.random.randint(1, n_bowls + 1) + colors, selected_color_names = utils.get_colors(mode=self.mode, n_colors=2) + block_urdf = 'stacking/block.urdf' + block_size = (0.04, 0.04, 0.04) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_id = env.add_object(bowl_urdf, bowl_pose, category='fixed', color=colors[1]) + bowl_poses.append(bowl_pose) + + # Add kits. + objects_ids = [] + obj_shapes = self.get_kitting_shapes(n_objects) + + for i in range(n_objects): + scale = utils.map_kit_scale((0.03, 0.03, 0.02)) + shape = os.path.join(self.assets_root, 'kitting', + f'{obj_shapes[i]:02d}.obj') + template = 'kitting/object-template.urdf' + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': colors[0]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + obj_pose = self.get_random_pose(env, block_size) + obj_id = env.add_object(urdf, obj_pose) + objects_ids.append(obj_id) + + # Goal: put each block in a different bowl. + pick_name = selected_color_names[0] + " " + utils.assembling_kit_shapes[obj_shapes[i]] + language_goal = (self.lang_template.format(pick=pick_name, place=selected_color_names[1])) + self.add_goal(objs=[obj_id], matches=np.ones((1, 1)), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / n_objects, language_goal=language_goal) + + # Only one mistake allowed. + self.max_steps = len(objects_ids) + 1 + + # Colors of distractor objects. + distractor_bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c not in selected_color_names] + distractor_block_colors = [utils.COLORS[c] for c in utils.COLORS if c not in selected_color_names] + + # Add distractors. + n_distractors = 0 + max_distractors = 6 + while n_distractors < max_distractors: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = distractor_block_colors if is_block else distractor_bowl_colors + pose = self.get_random_pose(env, size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 \ No newline at end of file diff --git a/cliport/generated_tasks/pyramid_blocks_assemble.py b/cliport/generated_tasks/pyramid_blocks_assemble.py new file mode 100644 index 0000000000000000000000000000000000000000..44088a4bbe9b68325eaa061c73b1d931e5e32fbb --- /dev/null +++ b/cliport/generated_tasks/pyramid_blocks_assemble.py @@ -0,0 +1,62 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class PyramidBlocksAssemble(Task): + """Construct a pyramid using nine blocks in a specific color order on a pallet.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "construct a pyramid using nine blocks in a specific color order on a pallet" + self.task_completed_desc = "done constructing pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.35, 0.35, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'] + ] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for _ in range(9): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [ + (-0.1, -0.1, 0.02), (0, -0.1, 0.02), (0.1, -0.1, 0.02), (-0.1, 0, 0.02), (0.1, 0, 0.02), + (-0.05, 0.05, 0.06), (0.05, 0.05, 0.06), (0, 0.1, 0.06), + (0, 0.05, 0.1) + ] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid in a specific color order. + for i in range(9): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 9, + language_goal=self.lang_template.format(blocks="the blocks", + row="row")) \ No newline at end of file diff --git a/cliport/generated_tasks/rainbow_stack.py b/cliport/generated_tasks/rainbow_stack.py new file mode 100644 index 0000000000000000000000000000000000000000..7d1fb25ba504bd0c2b8f2c28b7d172eb16e1d18b --- /dev/null +++ b/cliport/generated_tasks/rainbow_stack.py @@ -0,0 +1,39 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class RainbowStack(Task): + """Pick up blocks of seven different colors and stack them on the stand in the order of the rainbow (red, orange, yellow, green, blue, indigo, violet) from bottom to top.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "stack the blocks on the stand in the order of the rainbow from bottom to top" + self.task_completed_desc = "done stacking." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add stand. + # x, y, z dimensions for the asset size + stand_size = (0.12, 0.12, 0.02) + stand_pose = self.get_random_pose(env, stand_size) + stand_urdf = 'stacking/stand.urdf' + env.add_object(stand_urdf, stand_pose, 'fixed') + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + colors = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'] + blocks = [] + for color in colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Goal: stack the blocks on the stand in the order of the rainbow from bottom to top. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[stand_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(blocks), language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/sequential_block_insertion.py b/cliport/generated_tasks/sequential_block_insertion.py new file mode 100644 index 0000000000000000000000000000000000000000..ad511701bf21b805757b0cab008b8a3a02b82c49 --- /dev/null +++ b/cliport/generated_tasks/sequential_block_insertion.py @@ -0,0 +1,53 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class SequentialBlockInsertion(Task): + """Pick up blocks of different colors and insert them into the fixture of the same color in a specific sequence.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "insert the {color} block into the {color} fixture" + self.task_completed_desc = "done inserting blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define the sequence of colors + colors = ['red', 'blue', 'green', 'yellow'] + + # Add fixtures. + # x, y, z dimensions for the asset size + fixture_size = (0.12, 0.12, 0) + fixture_urdf = 'insertion/fixture.urdf' + fixtures = [] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color], category='fixed') + fixtures.append(fixture_id) + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for color in colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in the fixture of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/sequential_insertion_and_stacking.py b/cliport/generated_tasks/sequential_insertion_and_stacking.py new file mode 100644 index 0000000000000000000000000000000000000000..665c67fa6c6d101577095b12a4d533d7a3ff8d8f --- /dev/null +++ b/cliport/generated_tasks/sequential_insertion_and_stacking.py @@ -0,0 +1,63 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class SequentialInsertionAndStacking(Task): + """Pick up and insert each ell block into the corresponding colored fixture in the sequence of red, blue, and green. After successful insertion, pick up the three blocks again from the fixtures and stack them in a corner of the tabletop in the same color sequence - red at the bottom, blue in the middle, and green on top.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "insert the {color} ell block into the {color} fixture and then stack them in the corner" + self.task_completed_desc = "done inserting and stacking." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add fixtures. + fixture_size = (0.12, 0.12, 0) + fixture_urdf = 'insertion/fixture.urdf' + fixture_poses = [] + colors = ['red', 'blue', 'green'] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + env.add_object(fixture_urdf, fixture_pose, category='fixed', color=utils.COLORS[color]) + fixture_poses.append(fixture_pose) + + # Add ell blocks. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + ells = [] + for color in colors: + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[color]) + ells.append(ell_id) + + # Goal: each ell block is in the corresponding colored fixture. + for i in range(3): + self.add_goal(objs=[ells[i]], matches=np.ones((1, 1)), targ_poses=[fixture_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/3) + self.lang_goals.append(self.lang_template.format(color=colors[i])) + + # Add corner. + corner_size = (0.12, 0.12, 0) + corner_pose = self.get_random_pose(env, corner_size) + corner_urdf = 'corner/corner-template.urdf' + env.add_object(corner_urdf, corner_pose, category='fixed') + + # Goal: ell blocks are stacked in the corner in the color sequence - red at the bottom, blue in the middle, and green on top. + stack_poses = [(0, 0, 0.04), (0, 0, 0.08), (0, 0, 0.12)] + targs = [(utils.apply(corner_pose, i), corner_pose[1]) for i in stack_poses] + self.add_goal(objs=ells, matches=np.ones((3, 3)), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/3, + language_goal="stack the ell blocks in the corner in the color sequence - red at the bottom, blue in the middle, and green on top") \ No newline at end of file diff --git a/cliport/generated_tasks/sort_and_assemble_block_castle.py b/cliport/generated_tasks/sort_and_assemble_block_castle.py new file mode 100644 index 0000000000000000000000000000000000000000..35f29c037e1b24849757e10b92e1ca3f5962da48 --- /dev/null +++ b/cliport/generated_tasks/sort_and_assemble_block_castle.py @@ -0,0 +1,59 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SortAndAssembleBlockCastle(Task): + """Sort blocks by color and assemble them into a castle-like structure.""" + + def __init__(self): + super().__init__() + self.max_steps = 50 + self.lang_template = "sort the blocks by color and assemble them into a castle" + self.task_completed_desc = "done sorting and assembling." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for _ in range(3): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue']] + blocks = [] + for color in block_colors: + for _ in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Goal: each block is in a different zone based on color. + for i in range(12): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 3)), targ_poses=zone_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/12) + + # Goal: blocks are stacked in a pyramid in each zone. + for i in range(3): + zone_blocks = blocks[i*4:(i+1)*4] + place_pos = [(0, -0.02, 0.02), (0, 0.02, 0.02), + (0, 0, 0.06), (0, 0, 0.10)] + targs = [(utils.apply(zone_poses[i], pos), zone_poses[i][1]) for pos in place_pos] + for j in range(4): + self.add_goal(objs=[zone_blocks[j]], matches=np.ones((1, 1)), targ_poses=[targs[j]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/12, language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/sort_and_stack_clr_blocks.py b/cliport/generated_tasks/sort_and_stack_clr_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..d8bf990b0f3e279843be5e6252501d7c03ddf84f --- /dev/null +++ b/cliport/generated_tasks/sort_and_stack_clr_blocks.py @@ -0,0 +1,62 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SortAndStackClrBlocks(Task): + """Pick up four blocks of different colors (red, blue, green, yellow) and place them into separate corners of a pallet. After sorting, stack them in a specific sequence on top of the pallet. The bottom of the stack should start with a green block followed by a blue, then red, and finally a yellow block at the top.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "sort and stack the blocks in the order of green, blue, red, and yellow" + self.task_completed_desc = "done sorting and stacking blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0.05, 0.05, 0.02), (-0.05, 0.05, 0.02), (-0.05, -0.05, 0.02), (0.05, -0.05, 0.02)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are sorted into separate corners of the pallet. + self.add_goal(objs=blocks, matches=np.eye(4), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=0.5, symmetries=[np.pi/2]*4, + language_goal=self.lang_template) + + # Associate stacking locations for goals. + stack_pos = [(0, 0, 0.02), (0, 0, 0.06), (0, 0, 0.10), (0, 0, 0.14)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in stack_pos] + + # Goal: blocks are stacked on top of the pallet in the order of green, blue, red, and yellow. + self.add_goal(objs=blocks, matches=np.eye(4), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=0.5, symmetries=[np.pi/2]*4, + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/sort_insert_color_coordinated_blocks.py b/cliport/generated_tasks/sort_insert_color_coordinated_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..1ef6783dee825ecf1d4a4dfd2b97889adf88d394 --- /dev/null +++ b/cliport/generated_tasks/sort_insert_color_coordinated_blocks.py @@ -0,0 +1,53 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SortInsertColorCoordinatedBlocks(Task): + """Sort blocks by their colors and place them into the containers of the matching color.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "sort the blocks by their colors and place them into the containers of the matching color" + self.task_completed_desc = "done sorting and inserting blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add containers. + container_size = (0.12, 0.12, 0.12) + container_size = (0.1, 0.1, 0.1) + container_pose = self.get_random_pose(env, container_size) + container_urdf = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_urdf, replace) + container_colors = ['red', 'blue', 'green'] + container_poses = [] + for color in container_colors: + container_pose = self.get_random_pose(env, container_size) + env.add_object(container_urdf, container_pose, color=utils.COLORS[color]) + container_poses.append(container_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = ['red', 'red', 'blue', 'blue', 'green', 'green'] + blocks = [] + for color in block_colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in a container of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[container_poses[i//2]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/sorting_blocks_into_pallets.py b/cliport/generated_tasks/sorting_blocks_into_pallets.py new file mode 100644 index 0000000000000000000000000000000000000000..a1cb5eadf470a02476cb0b43548a6c3b25325a9b --- /dev/null +++ b/cliport/generated_tasks/sorting_blocks_into_pallets.py @@ -0,0 +1,51 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SortingBlocksIntoPallets(Task): + """Pick up blocks of four different colors (red, blue, green, yellow) and place them into four separate pallets of matching color. The pallets are placed in a row and the blocks are scattered randomly on the table.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "put the {color} block into the {color} pallet" + self.task_completed_desc = "done sorting blocks into pallets." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallets. + # x, y, z dimensions for the asset size + pallet_size = (0.12, 0.12, 0.02) + pallet_urdf = 'pallet/pallet.urdf' + pallet_poses = [] + pallet_colors = ['red', 'blue', 'green', 'yellow'] + for color in pallet_colors: + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed', color=utils.COLORS[color]) + pallet_poses.append(pallet_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + for color in pallet_colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in a different pallet of matching color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[pallet_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), + language_goal=self.lang_template.format(color=pallet_colors[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/sphere_align_stand.py b/cliport/generated_tasks/sphere_align_stand.py new file mode 100644 index 0000000000000000000000000000000000000000..5097ce06bd8ef63523b622f230d9b3ff75d53294 --- /dev/null +++ b/cliport/generated_tasks/sphere_align_stand.py @@ -0,0 +1,54 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SphereAlignStand(Task): + """Pick up each sphere and place it on the stand of the matching color.""" + + def __init__(self): + super().__init__() + self.max_steps = 5 + self.lang_template = "place the {color} sphere on the {color} stand" + self.task_completed_desc = "done aligning spheres with stands." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for the spheres and stands + colors = ['red', 'green', 'blue', 'yellow', 'purple'] + color_names = ['red', 'green', 'blue', 'yellow', 'purple'] + + # Add stands. + # x, y, z dimensions for the asset size + stand_size = (0.05, 0.05, 0.05) + stand_urdf = 'stacking/stand.urdf' + stand_poses = [] + for i in range(5): + stand_pose = self.get_random_pose(env, stand_size) + env.add_object(stand_urdf, stand_pose, 'fixed', color=utils.COLORS[colors[i]]) + stand_poses.append(stand_pose) + + # Add spheres. + # x, y, z dimensions for the asset size + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + spheres = [] + for i in range(5): + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS[colors[i]]) + spheres.append(sphere_id) + + # Goal: each sphere is on the stand of the matching color. + for i in range(5): + self.add_goal(objs=[spheres[i]], matches=np.ones((1, 1)), targ_poses=[stand_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/5, + language_goal=self.lang_template.format(color=color_names[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/sphere_container_color_match.py b/cliport/generated_tasks/sphere_container_color_match.py new file mode 100644 index 0000000000000000000000000000000000000000..7f2c51d44fefaa3b4cde37a51d2f3479e2668e9c --- /dev/null +++ b/cliport/generated_tasks/sphere_container_color_match.py @@ -0,0 +1,53 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class SphereContainerColorMatch(Task): + """Pick up each sphere and place it into a container of the same color.""" + + def __init__(self): + super().__init__() + self.max_steps = 4 + self.lang_template = "put the {color} sphere in the {color} container" + self.task_completed_desc = "done matching spheres and containers." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and corresponding names + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + color_names = ['red', 'blue', 'green', 'yellow'] + + # Add containers. + container_size = (0.12, 0.12, 0.12) + container_urdf = 'container/container-template.urdf' + containers = [] + for i in range(4): + container_pose = self.get_random_pose(env, container_size) + container_id = env.add_object(container_urdf, container_pose, color=colors[i]) + containers.append(container_id) + + # Add spheres. + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + spheres = [] + for i in range(4): + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=colors[i]) + spheres.append(sphere_id) + + # Goal: each sphere is in a container of the same color. + for i in range(4): + self.add_goal(objs=[spheres[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(containers[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=color_names[i])) \ No newline at end of file diff --git a/cliport/generated_tasks/stack_blocks_in_container.py b/cliport/generated_tasks/stack_blocks_in_container.py new file mode 100644 index 0000000000000000000000000000000000000000..d7121c06c76a825c008373a64e180366249358bd --- /dev/null +++ b/cliport/generated_tasks/stack_blocks_in_container.py @@ -0,0 +1,51 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class StackBlocksInContainer(Task): + """Pick up five blocks of different colors (red, blue, green, yellow, and orange) + and stack them in a container in a specific sequence. + The bottom of the stack should start with a red block followed by a blue, + green, yellow and finally an orange block at the top.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "stack the blocks in the container in the following order: {order}" + self.task_completed_desc = "done stacking blocks in container." + self.order = ['red', 'blue', 'green', 'yellow', 'orange'] + self.colors = [utils.COLORS[color] for color in self.order] + + def reset(self, env): + super().reset(env) + + # Add container. + container_size = (0.15, 0.15, 0.15) # x, y, z dimensions for the container size + container_pose = self.get_random_pose(env, container_size) + container_urdf = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_urdf, replace) + env.add_object(container_urdf, container_pose, 'fixed') + + # Add blocks. + block_size = (0.04, 0.04, 0.04) # x, y, z dimensions for the block size + block_urdf = 'block/block.urdf' + blocks = [] + for color in self.colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Goal: each block is stacked in the container in the specified order. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(blocks), + language_goal=self.lang_template.format(order=', '.join(self.order))) \ No newline at end of file diff --git a/cliport/generated_tasks/stack_color_coordinated_blocks.py b/cliport/generated_tasks/stack_color_coordinated_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..27cc15a0e8e1145cea0fa3ca03d2b6965816d8c1 --- /dev/null +++ b/cliport/generated_tasks/stack_color_coordinated_blocks.py @@ -0,0 +1,68 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class StackColorCoordinatedBlocks(Task): + """Pick up six blocks of different colors (red, blue, green, yellow, orange, and purple) + and stack them on a pallet in two separate stacks. The first stack should be red at the bottom, + blue in the middle, and green at top. The second stack should be yellow at the bottom, + orange in the middle, and purple at the top.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "stack the blocks on the pallet in two separate stacks. " \ + "The first stack should be red at the bottom, blue in the middle, " \ + "and green at top. The second stack should be yellow at the bottom, " \ + "orange in the middle, and purple at the top." + self.task_completed_desc = "done stacking color-coordinated blocks." + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['purple'] + ] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'box/box-template.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0, 0.02), (0, 0.05, 0.02), + (0, -0.05, 0.06), (0, 0, 0.06), (0, 0.05, 0.06)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked on the pallet in two separate stacks. + # First stack: red at the bottom, blue in the middle, and green at top. + self.add_goal(objs=blocks[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) + + # Second stack: yellow at the bottom, orange in the middle, and purple at the top. + self.add_goal(objs=blocks[3:], matches=np.ones((3, 3)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/stack_three_layer_red_wall.py b/cliport/generated_tasks/stack_three_layer_red_wall.py new file mode 100644 index 0000000000000000000000000000000000000000..023aa4cf13f1b9715dc7db303b81b70f691f1abe --- /dev/null +++ b/cliport/generated_tasks/stack_three_layer_red_wall.py @@ -0,0 +1,42 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class StackThreeLayerRedWall(Task): + """Build a wall by stacking blocks. The wall should consist of three layers with each layer having three red blocks aligned in a straight line.""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = "stack the red blocks to form a three-layer wall" + self.task_completed_desc = "done stacking blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + block_size = (0.05, 0.05, 0.03) # x, y, z dimensions for the block size + block_urdf = 'block/block_for_anchors.urdf' # URDF for the block + block_color = utils.COLORS['red'] # Color for the block + + # We need 9 blocks for a three-layer wall with each layer having three blocks. + blocks = [] + for _ in range(9): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_color) + blocks.append(block_id) + + # Define target poses for the blocks to form a three-layer wall. + # The target poses are defined relative to a base pose. + base_pose = ((0.5, 0.0, 0.0), (0, 0, 0, 1)) + target_poses = [] + for i in range(3): # three layers + for j in range(3): # three blocks per layer + target_pos = (j * block_size[0], 0, i * block_size[2]) + target_pose = (utils.apply(base_pose, target_pos), (0, 0, 0, 1)) + target_poses.append(target_pose) + + # Goal: all blocks are stacked to form a three-layer wall. + self.add_goal(objs=blocks[3*i:3*(i+1)], matches=np.ones((3, 3)), targ_poses=target_poses[3*i:3*(i+1)], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3., language_goal=self.lang_template) diff --git a/cliport/generated_tasks/sweep_and_sort_blocks.py b/cliport/generated_tasks/sweep_and_sort_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..8fe339511053f1074ade39ee5538f3815097f7c3 --- /dev/null +++ b/cliport/generated_tasks/sweep_and_sort_blocks.py @@ -0,0 +1,56 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class SweepAndSortBlocks(Task): + """Sweep a pile of small blocks of different colors (red, blue, green, and yellow) into their corresponding colored zones marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "sweep the pile of {color} blocks into the {color} square" + self.task_completed_desc = "done sweeping and sorting." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add colored zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + colors = ['red', 'blue', 'green', 'yellow'] + zone_poses = [] + for color in colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add piles of colored blocks. + block_urdf = 'block/small.urdf' + block_size = (0.04, 0.04, 0.04) + piles = [] + for color in colors: + pile = [] + for _ in range(10): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + pile.append(block_id) + piles.append(pile) + + # Add goals for each color. + for i, color in enumerate(colors): + self.add_goal(objs=piles[i], matches=np.ones((10, 1)), targ_poses=[zone_poses[i]], replace=True, + rotations=False, metric='zone', params=[(zone_poses[i], zone_size)], step_max_reward=1, + language_goal=self.lang_template.format(color=color)) \ No newline at end of file diff --git a/cliport/generated_tasks/symmetric_block_bridge_construction.py b/cliport/generated_tasks/symmetric_block_bridge_construction.py new file mode 100644 index 0000000000000000000000000000000000000000..29c8bef1b472b9f3b04149815e7caf3d1c3439a6 --- /dev/null +++ b/cliport/generated_tasks/symmetric_block_bridge_construction.py @@ -0,0 +1,87 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class SymmetricBlockBridgeConstruction(Task): + """Create a symmetrical bridge-shaped structure on a stand using eight blocks of two different colors (four red and four blue).""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "create a symmetrical bridge-shaped structure on a stand using eight blocks of two different colors (four red and four blue)" + self.task_completed_desc = "done building the bridge." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['blue']] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(8): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i%2]) + objs.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13), + (0, -0.025, 0.18), (0, 0.025, 0.18)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a bridge (bottom row: red, red). + self.add_goal(objs=objs[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*2, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (second row: blue). + self.add_goal(objs=objs[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (third row: red). + self.add_goal(objs=objs[3:4], matches=np.ones((1, 1)), targ_poses=targs[3:4], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (fourth row: blue). + self.add_goal(objs=objs[4:5], matches=np.ones((1, 1)), targ_poses=targs[4:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (fifth row: red). + self.add_goal(objs=objs[5:6], matches=np.ones((1, 1)), targ_poses=targs[5:6], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (sixth row: blue). + self.add_goal(objs=objs[6:7], matches=np.ones((1, 1)), targ_poses=targs[6:7], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (top row: red, red). + self.add_goal(objs=objs[7:], matches=np.ones((1, 1)), targ_poses=targs[7:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/generated_tasks/vertical_insertion_blocks.py b/cliport/generated_tasks/vertical_insertion_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..54f769aaee1f5e396ed72277ca8b24082fd7cf40 --- /dev/null +++ b/cliport/generated_tasks/vertical_insertion_blocks.py @@ -0,0 +1,54 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class VerticalInsertionBlocks(Task): + """Pick up four color specific blocks and insert each block into four differently colored stands set upright on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "insert the {color} block into the {color} stand" + self.task_completed_desc = "done inserting blocks into stands." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for blocks and stands + colors = ['red', 'blue', 'green', 'yellow'] + + # Add stands. + # x, y, z dimensions for the asset size + stand_size = (0.04, 0.04, 0.1) + stand_urdf = 'stacking/stand.urdf' + stands = [] + for color in colors: + stand_pose = self.get_random_pose(env, stand_size) + stand_id = env.add_object(stand_urdf, stand_pose, color=utils.COLORS[color], category='fixed') + stands.append(stand_id) + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + blocks = [] + for color in colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is inserted into the stand of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(stands[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), + language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/cliport/models/__init__.py b/cliport/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..46e2f45a5e375d93585e99b04ce0f83bf107936f --- /dev/null +++ b/cliport/models/__init__.py @@ -0,0 +1,56 @@ +from cliport.models.resnet import ResNet43_8s +from cliport.models.clip_wo_skip import CLIPWithoutSkipConnections + +from cliport.models.rn50_bert_unet import RN50BertUNet +from cliport.models.rn50_bert_lingunet import RN50BertLingUNet +from cliport.models.rn50_bert_lingunet_lat import RN50BertLingUNetLat +from cliport.models.untrained_rn50_bert_lingunet import UntrainedRN50BertLingUNet + +from cliport.models.clip_unet import CLIPUNet +from cliport.models.clip_lingunet import CLIPLingUNet + +from cliport.models.resnet_lang import ResNet43_8s_lang + +from cliport.models.resnet_lat import ResNet45_10s +from cliport.models.clip_unet_lat import CLIPUNetLat +from cliport.models.clip_lingunet_lat import CLIPLingUNetLat +from cliport.models.clip_film_lingunet_lat import CLIPFilmLingUNet +from cliport.models.clip_ling import CLIPLing +from cliport.models.resnet_lat_reduce import ResNet45_Reduced_10s +from cliport.models.pretrain_resnet import PretrainedResNet18 +from cliport.models.mdetr_lingunet_lat_fuse import MDETRLingUNetLat_fuse +from cliport.models.resnet_lat_origin import ResNet45_10s_origin + + +names = { + # resnet + 'plain_resnet': ResNet43_8s, + 'plain_resnet_lang': ResNet43_8s_lang, + 'pretrained_resnet18': PretrainedResNet18, + + + # without skip-connections + 'clip_woskip': CLIPWithoutSkipConnections, + + # unet + 'clip_unet': CLIPUNet, + 'rn50_bert_unet': RN50BertUNet, + + # lingunet + 'clip_lingunet': CLIPLingUNet, + 'rn50_bert_lingunet': RN50BertLingUNet, + 'untrained_rn50_bert_lingunet': UntrainedRN50BertLingUNet, + 'clip_ling': CLIPLing, + + # mdetr + 'mdetr_lingunet_lat_fuse': MDETRLingUNetLat_fuse, + + # lateral connections + 'plain_resnet_lat': ResNet45_10s, + 'plain_resnet_lat_origin': ResNet45_10s_origin, + 'clip_unet_lat': CLIPUNetLat, + 'clip_lingunet_lat': CLIPLingUNetLat, + 'clip_film_lingunet_lat': CLIPFilmLingUNet, + 'rn50_bert_lingunet_lat': RN50BertLingUNetLat, + 'plain_resnet_reduce_lat': ResNet45_Reduced_10s, +} diff --git a/cliport/models/backbone_full.py b/cliport/models/backbone_full.py new file mode 100644 index 0000000000000000000000000000000000000000..9b99b145d2c84444771045ad74992d0bf360f39b --- /dev/null +++ b/cliport/models/backbone_full.py @@ -0,0 +1,162 @@ +# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Backbone modules. +""" +from collections import OrderedDict + +import torch +import torch.nn.functional as F +import torchvision +from timm.models import create_model +from torch import nn +from torchvision.models._utils import IntermediateLayerGetter + +from cliport.models.misc import NestedTensor + + + +class FrozenBatchNorm2d(torch.nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + Copy-paste from torchvision.misc.ops with added eps before rqsrt, + without which any other models than torchvision.models.resnet[18,34,50,101] + produce nans. + """ + + def __init__(self, n): + super(FrozenBatchNorm2d, self).__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + num_batches_tracked_key = prefix + "num_batches_tracked" + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super(FrozenBatchNorm2d, self)._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def forward(self, x): + # move reshapes to the beginning + # to make it fuser-friendly + w = self.weight.reshape(1, -1, 1, 1) + b = self.bias.reshape(1, -1, 1, 1) + rv = self.running_var.reshape(1, -1, 1, 1) + rm = self.running_mean.reshape(1, -1, 1, 1) + eps = 1e-5 + scale = w * (rv + eps).rsqrt() + bias = b - rm * scale + return x * scale + bias + + +class BackboneBase(nn.Module): + def __init__(self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool): + super().__init__() + for name, parameter in backbone.named_parameters(): + if not train_backbone or "layer2" not in name and "layer3" not in name and "layer4" not in name: + parameter.requires_grad_(False) + if return_interm_layers: + return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} + else: + return_layers = {"layer4": 0} + self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) + self.num_channels = num_channels + + def forward(self, tensor_list): + xs = self.body(tensor_list.tensors) + out = OrderedDict() + for name, x in xs.items(): + mask = F.interpolate(tensor_list.mask[None].float(), size=x.shape[-2:]).bool()[0] + out[name] = NestedTensor(x, mask) + return out + + +class Backbone(BackboneBase): + """ResNet backbone with frozen BatchNorm.""" + + def __init__(self, name: str, train_backbone: bool, return_interm_layers: bool, dilation: bool): + backbone = getattr(torchvision.models, name)( + replace_stride_with_dilation=[False, False, dilation], pretrained=False, norm_layer=FrozenBatchNorm2d + ) + num_channels = 512 if name in ("resnet18", "resnet34") else 2048 + super().__init__(backbone, train_backbone, num_channels, return_interm_layers) + + +class GroupNorm32(torch.nn.GroupNorm): + def __init__(self, num_channels, num_groups=32, **kargs): + super().__init__(num_groups, num_channels, **kargs) + + +class GroupNormBackbone(BackboneBase): + """ResNet backbone with GroupNorm with 32 channels.""" + + def __init__(self, name: str, train_backbone: bool, return_interm_layers: bool, dilation: bool): + name_map = { + "resnet50-gn": ("resnet50", "/checkpoint/szagoruyko/imagenet/22014122/checkpoint.pth"), + "resnet101-gn": ("resnet101", "/checkpoint/szagoruyko/imagenet/22080524/checkpoint.pth"), + } + backbone = getattr(torchvision.models, name_map[name][0])( + replace_stride_with_dilation=[False, False, dilation], pretrained=False, norm_layer=GroupNorm32 + ) + checkpoint = torch.load(name_map[name][1], map_location="cpu") + state_dict = {k[7:]: p for k, p in checkpoint["model"].items()} + backbone.load_state_dict(state_dict) + num_channels = 512 if name_map[name][0] in ("resnet18", "resnet34") else 2048 + super().__init__(backbone, train_backbone, num_channels, return_interm_layers) + + +def replace_bn(m, name=""): + for attr_str in dir(m): + target_attr = getattr(m, attr_str) + if isinstance(target_attr, torch.nn.BatchNorm2d): + frozen = FrozenBatchNorm2d(target_attr.num_features) + bn = getattr(m, attr_str) + frozen.weight.data.copy_(bn.weight) + frozen.bias.data.copy_(bn.bias) + frozen.running_mean.data.copy_(bn.running_mean) + frozen.running_var.data.copy_(bn.running_var) + setattr(m, attr_str, frozen) + for n, ch in m.named_children(): + replace_bn(ch, n) + + +class GN_8(nn.Module): + def __init__(self, num_channels): + super().__init__() + self.gn = torch.nn.GroupNorm(8, num_channels) + + def forward(self, x): + return self.gn(x) + + +class TimmBackbone(nn.Module): + def __init__(self, name, return_interm_layers, main_layer=-1, group_norm=False): + super().__init__() + backbone = create_model(name, pretrained=True, in_chans=3, features_only=True, out_indices=(1, 2, 3, 4)) + + with torch.no_grad(): + replace_bn(backbone) + num_channels = backbone.feature_info.channels()[-1] + self.body = backbone + self.num_channels = num_channels + self.interm = return_interm_layers + self.main_layer = main_layer + + def forward(self, tensor_list): + xs = self.body(tensor_list.tensors) + if not self.interm: + xs = [xs[self.main_layer]] + out = OrderedDict() + for i, x in enumerate(xs): + mask = F.interpolate(tensor_list.mask[None].float(), size=x.shape[-2:]).bool()[0] + out[f"layer{i}"] = NestedTensor(x, mask) + return out + + diff --git a/cliport/models/clip_film_lingunet_lat.py b/cliport/models/clip_film_lingunet_lat.py new file mode 100644 index 0000000000000000000000000000000000000000..32a8ee64326d9bc33e0e2890de514dc3af266196 --- /dev/null +++ b/cliport/models/clip_film_lingunet_lat.py @@ -0,0 +1,116 @@ +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils +from cliport.models.resnet import IdentityBlock, ConvBlock +from cliport.models.core.unet import Up +from cliport.models.clip_lingunet_lat import CLIPLingUNetLat + +from cliport.models.core import fusion +from cliport.models.core.fusion import FusionConvLat + + +class CLIPFilmLingUNet(CLIPLingUNetLat): + """ CLIP RN50 with U-Net skip connections """ + + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super().__init__(input_shape, output_dim, cfg, device, preprocess) + + def _build_decoder(self): + # language + self.lang_fusion_type = 'film' + + self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2) + self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4) + self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8) + + self.proj_input_dim = 1024 + + self.lang_gamma1 = nn.Linear(self.proj_input_dim, 1024) + self.lang_gamma2 = nn.Linear(self.proj_input_dim, 512) + self.lang_gamma3 = nn.Linear(self.proj_input_dim, 256) + + self.lang_beta1 = nn.Linear(self.proj_input_dim, 1024) + self.lang_beta2 = nn.Linear(self.proj_input_dim, 512) + self.lang_beta3 = nn.Linear(self.proj_input_dim, 256) + + # vision + self.conv1 = nn.Sequential( + nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False), + nn.ReLU(True) + ) + self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear) + self.lat_fusion1 = FusionConvLat(input_dim=1024+512, output_dim=512) + + self.up2 = Up(1024, 512 // self.up_factor, self.bilinear) + self.lat_fusion2 = FusionConvLat(input_dim=512+256, output_dim=256) + + self.up3 = Up(512, 256 // self.up_factor, self.bilinear) + self.lat_fusion3 = FusionConvLat(input_dim=256+128, output_dim=128) + + self.layer1 = nn.Sequential( + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion4 = FusionConvLat(input_dim=128+64, output_dim=64) + + self.layer2 = nn.Sequential( + ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion5 = FusionConvLat(input_dim=64+32, output_dim=32) + + self.layer3 = nn.Sequential( + ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion6 = FusionConvLat(input_dim=32+16, output_dim=16) + + self.conv2 = nn.Sequential( + nn.Conv2d(16, self.output_dim, kernel_size=1) + ) + + def forward(self, x, lat, l): + x = self.preprocess(x, dist='clip') + + in_type = x.dtype + in_shape = x.shape + x = x[:,:3] # select RGB + x, im = self.encode_image(x) + x = x.to(in_type) + + l_enc, l_emb, l_mask = self.encode_text(l) + l_input = l_enc + l_input = l_input.to(dtype=x.dtype) + + assert x.shape[1] == self.input_dim + x = self.conv1(x) + + x = self.lang_fuser1(x, l_input, gamma=self.lang_gamma1, beta=self.lang_beta1) + x = self.up1(x, im[-2]) + x = self.lat_fusion1(x, lat[-6]) + + x = self.lang_fuser2(x, l_input, gamma=self.lang_gamma2, beta=self.lang_beta2) + x = self.up2(x, im[-3]) + x = self.lat_fusion2(x, lat[-5]) + + x = self.lang_fuser3(x, l_input, gamma=self.lang_gamma3, beta=self.lang_beta3) + x = self.up3(x, im[-4]) + x = self.lat_fusion3(x, lat[-4]) + + x = self.layer1(x) + x = self.lat_fusion4(x, lat[-3]) + + x = self.layer2(x) + x = self.lat_fusion5(x, lat[-2]) + + x = self.layer3(x) + x = self.lat_fusion6(x, lat[-1]) + + x = self.conv2(x) + + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x \ No newline at end of file diff --git a/cliport/models/clip_ling.py b/cliport/models/clip_ling.py new file mode 100644 index 0000000000000000000000000000000000000000..f0e1a434f6df82defd2128760115a062939710ab --- /dev/null +++ b/cliport/models/clip_ling.py @@ -0,0 +1,97 @@ +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils +from cliport.models.resnet import IdentityBlock, ConvBlock +from cliport.models.core.unet import Up +from cliport.models.core import fusion +from cliport.models.clip_lingunet_lat import CLIPLingUNetLat + + +class CLIPLing(CLIPLingUNetLat): + """ CLIP RN50 with U-Net skip connections """ + + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super().__init__(input_shape, output_dim, cfg, device, preprocess) + + # def _build_decoder(self): + # # language + # self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2) + # self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4) + # self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8) + + # self.proj_input_dim = 512 if 'word' in self.lang_fusion_type else 1024 + # self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024) + # self.lang_proj2 = nn.Linear(self.proj_input_dim, 512) + # self.lang_proj3 = nn.Linear(self.proj_input_dim, 256) + + # # vision + # self.conv1 = nn.Sequential( + # nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False), + # nn.ReLU(True) + # ) + + # self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear) + + # self.up2 = Up(1024, 512 // self.up_factor, self.bilinear) + + # self.up3 = Up(512, 256 // self.up_factor, self.bilinear) + + # self.layer1 = nn.Sequential( + # ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # nn.UpsamplingBilinear2d(scale_factor=2), + # ) + + # self.layer2 = nn.Sequential( + # ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # nn.UpsamplingBilinear2d(scale_factor=2), + # ) + + # self.layer3 = nn.Sequential( + # ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # nn.UpsamplingBilinear2d(scale_factor=2), + # ) + + del self.lang_fuser2, self.lang_fuser1, self.lang_proj1, self.lang_proj2, self.layer2, self.layer1, self.layer3 + + self.conv2 = nn.Sequential( + nn.Conv2d(128, self.output_dim, kernel_size=1) + ) + + + + + def forward(self, x, lat, l): + x = self.preprocess(x, dist='clip') + + in_type = x.dtype + in_shape = x.shape + x = x[:,:3] # select RGB + x, im = self.encode_image(x) + x = x.to(in_type) + + l_enc, l_emb, l_mask = self.encode_text(l) + l_input = l_emb if 'word' in self.lang_fusion_type else l_enc + l_input = l_input.to(dtype=x.dtype) + + assert x.shape[1] == self.input_dim + x = self.conv1(x) + + # x = self.lang_fuser1(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj1) + # x = self.up1(x, im[-2]) + # x = self.lat_fusion1(x, lat[-6]) + + # x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2) + # x = self.up2(x, im[-3]) + # x = self.lat_fusion2(x, lat[-5]) + + x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3) + x = self.up3(x, im[-4]) + x = self.lat_fusion3(x, lat[1]) + x = self.conv2(x) + + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x \ No newline at end of file diff --git a/cliport/models/clip_lingunet.py b/cliport/models/clip_lingunet.py new file mode 100644 index 0000000000000000000000000000000000000000..843d5d6e901257df882a33ec56a7a06ea20810c3 --- /dev/null +++ b/cliport/models/clip_lingunet.py @@ -0,0 +1,93 @@ +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils +from cliport.models.resnet import IdentityBlock, ConvBlock +from cliport.models.core.unet import Up +from cliport.models.core import fusion +from cliport.models.clip_lingunet_lat import CLIPLingUNetLat + + +class CLIPLingUNet(CLIPLingUNetLat): + """ CLIP RN50 with U-Net skip connections """ + + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super().__init__(input_shape, output_dim, cfg, device, preprocess) + + def _build_decoder(self): + # language + self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2) + self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4) + self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8) + + self.proj_input_dim = 512 if 'word' in self.lang_fusion_type else 1024 + self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024) + self.lang_proj2 = nn.Linear(self.proj_input_dim, 512) + self.lang_proj3 = nn.Linear(self.proj_input_dim, 256) + + # vision + self.conv1 = nn.Sequential( + nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False), + nn.ReLU(True) + ) + + self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear) + + self.up2 = Up(1024, 512 // self.up_factor, self.bilinear) + + self.up3 = Up(512, 256 // self.up_factor, self.bilinear) + + self.layer1 = nn.Sequential( + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer2 = nn.Sequential( + ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer3 = nn.Sequential( + ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.conv2 = nn.Sequential( + nn.Conv2d(16, self.output_dim, kernel_size=1) + ) + + def forward(self, x, l): + x = self.preprocess(x, dist='clip') + + in_type = x.dtype + in_shape = x.shape + x = x[:,:3] # select RGB + x, im = self.encode_image(x) + x = x.to(in_type) + + # encode text + l_enc, l_emb, l_mask = self.encode_text(l) + l_input = l_emb if 'word' in self.lang_fusion_type else l_enc + l_input = l_input.to(dtype=x.dtype) + + # encode image + assert x.shape[1] == self.input_dim + x = self.conv1(x) + + x = self.lang_fuser1(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj1) + x = self.up1(x, im[-2]) + + x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2) + x = self.up2(x, im[-3]) + + x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3) + x = self.up3(x, im[-4]) + + for layer in [self.layer1, self.layer2, self.layer3, self.conv2]: + x = layer(x) + + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x \ No newline at end of file diff --git a/cliport/models/clip_lingunet_lat.py b/cliport/models/clip_lingunet_lat.py new file mode 100644 index 0000000000000000000000000000000000000000..74e9006ecd5eac1df433085427443ae15489734b --- /dev/null +++ b/cliport/models/clip_lingunet_lat.py @@ -0,0 +1,149 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils +from cliport.models.resnet import IdentityBlock, ConvBlock +from cliport.models.core.unet import Up +from cliport.models.core.clip import build_model, load_clip, tokenize + +from cliport.models.core import fusion +from cliport.models.core.fusion import FusionConvLat + + +class CLIPLingUNetLat(nn.Module): + """ CLIP RN50 with U-Net skip connections and lateral connections """ + + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super(CLIPLingUNetLat, self).__init__() + self.input_shape = input_shape + self.output_dim = output_dim + self.input_dim = 2048 # penultimate layer channel-size of CLIP-RN50 + self.cfg = cfg + self.device = device + self.batchnorm = self.cfg['train']['batchnorm'] + self.lang_fusion_type = self.cfg['train']['lang_fusion_type'] + self.bilinear = True + self.up_factor = 2 if self.bilinear else 1 + self.preprocess = preprocess + + self._load_clip() + self._build_decoder() + + def _load_clip(self): + model, _ = load_clip("RN50", device=self.device) + self.clip_rn50 = build_model(model.state_dict()).to(self.device) + del model + + def _build_decoder(self): + # language + self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2) + self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4) + self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8) + + self.proj_input_dim = 512 if 'word' in self.lang_fusion_type else 1024 + self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024) + self.lang_proj2 = nn.Linear(self.proj_input_dim, 512) + self.lang_proj3 = nn.Linear(self.proj_input_dim, 256) + + # vision + # self.conv1 = nn.Sequential( + # nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False), + # nn.ReLU(True) + # ) + + # self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear) + # self.lat_fusion1 = FusionConvLat(input_dim=1024+512, output_dim=512) + + # self.up2 = Up(1024, 512 // self.up_factor, self.bilinear) + # self.lat_fusion2 = FusionConvLat(input_dim=512+256, output_dim=256) + + self.conv1 = nn.Sequential( + nn.Conv2d(self.input_dim, 256, kernel_size=3, stride=1, padding=1, bias=False), + nn.ReLU(True) + ) + + self.up3 = Up(512, 256 // self.up_factor, self.bilinear) + self.lat_fusion3 = FusionConvLat(input_dim=256+128, output_dim=128) + + self.layer1 = nn.Sequential( + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion4 = FusionConvLat(input_dim=128+64, output_dim=64) + + self.layer2 = nn.Sequential( + ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion5 = FusionConvLat(input_dim=64+32, output_dim=32) + + self.layer3 = nn.Sequential( + ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion6 = FusionConvLat(input_dim=32+16, output_dim=16) + + self.conv2 = nn.Sequential( + nn.Conv2d(16, self.output_dim, kernel_size=1) + ) + + def encode_image(self, img): + with torch.no_grad(): + img_encoding, img_im = self.clip_rn50.visual.prepool_im(img) + return img_encoding, img_im + + def encode_text(self, x): + with torch.no_grad(): + tokens = tokenize(x).to(self.device) + text_feat, text_emb = self.clip_rn50.encode_text_with_embeddings(tokens) + + text_mask = torch.where(tokens==0, tokens, 1) # [1, max_token_len] + return text_feat, text_emb, text_mask + + def forward(self, x, lat, l): + x = self.preprocess(x, dist='clip') + + in_type = x.dtype + in_shape = x.shape + x = x[:,:3] # select RGB + x, im = self.encode_image(x) + x = x.to(in_type) + + l_enc, l_emb, l_mask = self.encode_text(l) + l_input = l_emb if 'word' in self.lang_fusion_type else l_enc + l_input = l_input.to(dtype=x.dtype) + + assert x.shape[1] == self.input_dim + x = self.conv1(x) + + # x = self.lang_fuser1(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj1) + # x = self.up1(x, im[-2]) + # x = self.lat_fusion1(x, lat[-6]) + + # x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2) + # x = self.up2(x, im[-3]) + # x = self.lat_fusion2(x, lat[-5]) + if (x.shape[0] > 8) and ((x.shape[0] % 36) == 0): + l_input = l_input.repeat_interleave(36, dim=0) + + x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3) + x = self.up3(x, im[-4]) + x = self.lat_fusion3(x, lat[-4]) + + x = self.layer1(x) + x = self.lat_fusion4(x, lat[-3]) + + x = self.layer2(x) + x = self.lat_fusion5(x, lat[-2]) + + x = self.layer3(x) + x = self.lat_fusion6(x, lat[-1]) + + x = self.conv2(x) + + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x \ No newline at end of file diff --git a/cliport/models/clip_unet.py b/cliport/models/clip_unet.py new file mode 100644 index 0000000000000000000000000000000000000000..adc0f4f81118dc1063cf19f05dca87650c9cf8cc --- /dev/null +++ b/cliport/models/clip_unet.py @@ -0,0 +1,69 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils +from cliport.models.resnet import IdentityBlock, ConvBlock +from cliport.models.core.unet import Up +from cliport.models.clip_lingunet_lat import CLIPLingUNetLat + + +class CLIPUNet(CLIPLingUNetLat): + """ CLIP RN50 with U-Net skip connections without language """ + + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super().__init__(input_shape, output_dim, cfg, device, preprocess) + + def _build_decoder(self): + self.conv1 = nn.Sequential( + nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False), + nn.ReLU(True) + ) + + self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear) + + self.up2 = Up(1024, 512 // self.up_factor, self.bilinear) + + self.up3 = Up(512, 256 // self.up_factor, self.bilinear) + + self.layer1 = nn.Sequential( + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer2 = nn.Sequential( + ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer3 = nn.Sequential( + ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.conv2 = nn.Sequential( + nn.Conv2d(16, self.output_dim, kernel_size=1) + ) + + def forward(self, x): + x = self.preprocess(x, dist='clip') + + in_type = x.dtype + in_shape = x.shape + x = x[:,:3] # select RGB + x, im = self.encode_image(x) + x = x.to(in_type) + + x = self.conv1(x) + x = self.up1(x, im[-2]) + x = self.up2(x, im[-3]) + x = self.up3(x, im[-4]) + + for layer in [self.layer1, self.layer2, self.layer3, self.conv2]: + x = layer(x) + + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x \ No newline at end of file diff --git a/cliport/models/clip_unet_lat.py b/cliport/models/clip_unet_lat.py new file mode 100644 index 0000000000000000000000000000000000000000..811414569b33609353ed4eae5708aab1c6251025 --- /dev/null +++ b/cliport/models/clip_unet_lat.py @@ -0,0 +1,90 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils +from cliport.models.resnet import IdentityBlock, ConvBlock +from cliport.models.core.unet import Up +from cliport.models.core.fusion import FusionConvLat +from cliport.models.clip_lingunet_lat import CLIPLingUNetLat + + +class CLIPUNetLat(CLIPLingUNetLat): + """ CLIP RN50 with U-Net skip connections and lateral connections without language """ + + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super().__init__(input_shape, output_dim, cfg, device, preprocess) + + def _build_decoder(self): + self.conv1 = nn.Sequential( + nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False), + nn.ReLU(True) + ) + + self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear) + self.lat_fusion1 = FusionConvLat(input_dim=1024+512, output_dim=512) + + self.up2 = Up(1024, 512 // self.up_factor, self.bilinear) + self.lat_fusion2 = FusionConvLat(input_dim=512+256, output_dim=256) + + self.up3 = Up(512, 256 // self.up_factor, self.bilinear) + self.lat_fusion3 = FusionConvLat(input_dim=256+128, output_dim=128) + + self.layer1 = nn.Sequential( + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion4 = FusionConvLat(input_dim=128+64, output_dim=64) + + self.layer2 = nn.Sequential( + ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion5 = FusionConvLat(input_dim=64+32, output_dim=32) + + self.layer3 = nn.Sequential( + ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion6 = FusionConvLat(input_dim=32+16, output_dim=16) + + self.conv2 = nn.Sequential( + nn.Conv2d(16, self.output_dim, kernel_size=1) + ) + + def forward(self, x, lat): + x = self.preprocess(x, dist='clip') + + in_type = x.dtype + in_shape = x.shape + x = x[:,:3] # select RGB + x, im = self.encode_image(x) + x = x.to(in_type) + + x = self.conv1(x) + + x = self.up1(x, im[-2]) + x = self.lat_fusion1(x, lat[-6]) + + x = self.up2(x, im[-3]) + x = self.lat_fusion2(x, lat[-5]) + + x = self.up3(x, im[-4]) + x = self.lat_fusion3(x, lat[-4]) + + x = self.layer1(x) + x = self.lat_fusion4(x, lat[-3]) + + x = self.layer2(x) + x = self.lat_fusion5(x, lat[-2]) + + x = self.layer3(x) + x = self.lat_fusion6(x, lat[-1]) + + x = self.conv2(x) + + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x \ No newline at end of file diff --git a/cliport/models/clip_wo_skip.py b/cliport/models/clip_wo_skip.py new file mode 100644 index 0000000000000000000000000000000000000000..915c4bc64b674fc91872a89c97692263a690ac31 --- /dev/null +++ b/cliport/models/clip_wo_skip.py @@ -0,0 +1,67 @@ +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils +from cliport.models.resnet import IdentityBlock, ConvBlock +from cliport.models.clip_lingunet_lat import CLIPLingUNetLat + + +class CLIPWithoutSkipConnections(CLIPLingUNetLat): + """ CLIP RN50 with decoders (no skip connections) """ + + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super().__init__(input_shape, output_dim, cfg, device, preprocess) + + def _build_decoder(self): + self.layers = nn.Sequential( + # conv1 + nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False), + nn.ReLU(True), + nn.UpsamplingBilinear2d(scale_factor=2), + + # decoder blocks + ConvBlock(1024, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + + ConvBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + + ConvBlock(512, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + + ConvBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + + ConvBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + + ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + + ConvBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + + # conv2 + nn.UpsamplingBilinear2d(scale_factor=2), + nn.Conv2d(32, self.output_dim, kernel_size=1) + ) + + def forward(self, x): + x = self.preprocess(x, dist='clip') + + in_type = x.dtype + in_shape = x.shape + x = x[:,:3] # select RGB + x, _ = self.encode_image(x) + x = x.to(in_type) + + assert x.shape[1] == self.input_dim + x = self.layers(x) + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x \ No newline at end of file diff --git a/cliport/models/core/__init__.py b/cliport/models/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/cliport/models/core/attention.py b/cliport/models/core/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..9a615a4f32114df8f68a33efb43701fca5e0f4f5 --- /dev/null +++ b/cliport/models/core/attention.py @@ -0,0 +1,77 @@ +"""Attention module.""" + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import cliport.models as models +from cliport.utils import utils + + +class Attention(nn.Module): + """Attention (a.k.a Pick) module.""" + + def __init__(self, stream_fcn, in_shape, n_rotations, preprocess, cfg, device): + super().__init__() + self.stream_fcn = stream_fcn + self.n_rotations = n_rotations + self.preprocess = preprocess + self.cfg = cfg + self.device = device + self.batchnorm = self.cfg['train']['batchnorm'] + + self.padding = np.zeros((3, 2), dtype=int) + max_dim = np.max(in_shape[:2]) + pad = (max_dim - np.array(in_shape[:2])) / 2 + self.padding[:2] = pad.reshape(2, 1) # left right top bown front back + + in_shape = np.array(in_shape) + in_shape += np.sum(self.padding, axis=1) + in_shape = tuple(in_shape) + self.in_shape = in_shape + self.rotator = utils.ImageRotator(self.n_rotations) + self._build_nets() + + def _build_nets(self): + stream_one_fcn, _ = self.stream_fcn + self.attn_stream = models.names[stream_one_fcn](self.in_shape, 1, self.cfg, self.device) + print(f"Attn FCN: {stream_one_fcn}") + + def attend(self, x): + return self.attn_stream(x) + + def forward(self, inp_img, softmax=True): + """Forward pass.""" + # print("in_img.shape", inp_img.shape) + in_data = np.pad(inp_img, self.padding, mode='constant') + in_shape = input_data.shape + if len(inp_shape) == 3: + inp_shape = (1,) + inp_shape + in_data = in_data.reshape(in_shape) + in_tens = torch.from_numpy(in_data.copy()).to(dtype=torch.float, device=self.device) # [B W H 6] + + # Rotation pivot. + pv = np.array(in_data.shape[1:3]) // 2 + + # Rotate input. + in_tens = in_tens.permute(0, 3, 1, 2) # [B 6 W H] + in_tens = in_tens.repeat(self.n_rotations, 1, 1, 1) + in_tens = self.rotator(in_tens, pivot=pv) + + # Forward pass. + logits = self.attend(torch.cat(in_tens, dim=0), lang_goal) + + # Rotate back output. + logits = self.rotator(logits, reverse=True, pivot=pv) + logits = torch.cat(logits, dim=0) + c0 = self.padding[:2, 0] + c1 = c0 + inp_img.shape[:2] + logits = logits[:, :, c0[0]:c1[0], c0[1]:c1[1]] + + logits = logits.permute(1, 2, 3, 0) # [B W H 1] + output = logits.reshape(len(logits), np.prod(logits.shape)) + if softmax: + output = F.softmax(output, dim=-1) + output = output.reshape(logits.shape[1:]) + return output \ No newline at end of file diff --git a/cliport/models/core/attention_image_goal.py b/cliport/models/core/attention_image_goal.py new file mode 100644 index 0000000000000000000000000000000000000000..1d2a1049be2b03309a85db0337e33551dd561aa2 --- /dev/null +++ b/cliport/models/core/attention_image_goal.py @@ -0,0 +1,57 @@ +"""Attention module.""" + +import numpy as np +import torch +import torch.nn.functional as F + + +from cliport.models.core.attention import Attention + + +class AttentionImageGoal(Attention): + """Attention (a.k.a Pick) with image-goals module.""" + + def __init__(self, stream_fcn, in_shape, n_rotations, preprocess, cfg, device): + super().__init__(stream_fcn, in_shape, n_rotations, preprocess, cfg, device) + + def forward(self, inp_img, goal_img, softmax=True): + """Forward pass.""" + # Input image. + in_data = np.pad(inp_img, self.padding, mode='constant') + in_shape = (1,) + in_data.shape + in_data = in_data.reshape(in_shape) + in_tens = torch.from_numpy(in_data).to(dtype=torch.float, device=self.device) + + goal_tensor = np.pad(goal_img, self.padding, mode='constant') + goal_shape = (1,) + goal_tensor.shape + goal_tensor = goal_tensor.reshape(goal_shape) + goal_tensor = torch.from_numpy(goal_tensor.copy()).to(dtype=torch.float, device=self.device) + in_tens = in_tens * goal_tensor + + # Rotation pivot. + pv = np.array(in_data.shape[1:3]) // 2 + + # Rotate input. + in_tens = in_tens.permute(0, 3, 1, 2) + in_tens = in_tens.repeat(self.n_rotations, 1, 1, 1) + in_tens = self.rotator(in_tens, pivot=pv) + + # Forward pass. + logits = [] + for x in in_tens: + logits.append(self.attend(x)) + logits = torch.cat(logits, dim=0) + + # Rotate back output. + logits = self.rotator(logits, reverse=True, pivot=pv) + logits = torch.cat(logits, dim=0) + c0 = self.padding[:2, 0] + c1 = c0 + inp_img.shape[:2] + logits = logits[:, :, c0[0]:c1[0], c0[1]:c1[1]] + + logits = logits.permute(1, 2, 3, 0) # D H W C + output = logits.reshape(1, np.prod(logits.shape)) + if softmax: + output = F.softmax(output, dim=-1) + output = output.reshape(logits.shape[1:]) + return output \ No newline at end of file diff --git a/cliport/models/core/clip.py b/cliport/models/core/clip.py new file mode 100644 index 0000000000000000000000000000000000000000..d98ea192d65032535737cc6acced14c050894613 --- /dev/null +++ b/cliport/models/core/clip.py @@ -0,0 +1,615 @@ +########################################### +#### Authors: OpenAI +#### Credit: https://github.com/openai/CLIP +#### MIT License. + +from collections import OrderedDict +from typing import Tuple, Union + +import torch +import torch.nn.functional as F +from torch import nn + +import hashlib +import os +import urllib +import warnings +from typing import Union, List + +import torch +from PIL import Image +from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize +from tqdm import tqdm + +from cliport.utils.simple_tokenizer import SimpleTokenizer as _Tokenizer + + +__all__ = ["available_models", "load", "tokenize"] +_tokenizer = _Tokenizer() + +_MODELS = { + "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", + "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", +} + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + + self.relu = nn.ReLU(inplace=True) + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential(OrderedDict([ + ("-1", nn.AvgPool2d(stride)), + ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), + ("1", nn.BatchNorm2d(planes * self.expansion)) + ])) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.relu(self.bn1(self.conv1(x))) + out = self.relu(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x, key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0, + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + + return x[0] + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, input_resolution=224, width=64): + super().__init__() + self.output_dim = output_dim + self.input_resolution = input_resolution + + # the 3-layer stem + self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(width // 2) + self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(width // 2) + self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(width) + self.avgpool = nn.AvgPool2d(2) + self.relu = nn.ReLU(inplace=True) + + # residual layers + self._inplanes = width # this is a *mutable* variable used during construction + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim) + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.prepool(x) + x = self.attnpool(x) + return x + + def prepool(self, x): + def stem(x): + for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]: + x = self.relu(bn(conv(x))) + x = self.avgpool(x) + return x + + x = x.type(self.conv1.weight.dtype) + x = stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + return x + + def prepool_im(self, x): + """Run until prepool and save intermediate features""" + im = [] + def stem(x): + for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]: + x = self.relu(bn(conv(x))) + im.append(x) + x = self.avgpool(x) + im.append(x) + return x + + x = x.type(self.conv1.weight.dtype) + x = stem(x) + + for layer in [self.layer1, self.layer2, self.layer3, self.layer4]: + x = layer(x) + im.append(x) + + return x, im + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +class QuickGELU(nn.Module): + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model)) + ])) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class Transformer(nn.Module): + def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None): + super().__init__() + self.width = width + self.layers = layers + self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)]) + + def forward(self, x: torch.Tensor): + return self.resblocks(x) + + +class VisualTransformer(nn.Module): + def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int): + super().__init__() + self.input_resolution = input_resolution + self.output_dim = output_dim + self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) + + scale = width ** -0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width)) + self.ln_pre = LayerNorm(width) + + self.transformer = Transformer(width, layers, heads) + + self.ln_post = LayerNorm(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + def forward(self, x: torch.Tensor): + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + x = self.ln_post(x[:, 0, :]) + + if self.proj is not None: + x = x @ self.proj + + return x + + +class CLIP(nn.Module): + def __init__(self, + embed_dim: int, + # vision + image_resolution: int, + vision_layers: Union[Tuple[int, int, int, int], int], + vision_width: int, + vision_patch_size: int, + # text + context_length: int, + vocab_size: int, + transformer_width: int, + transformer_heads: int, + transformer_layers: int + ): + super().__init__() + + self.context_length = context_length + + if isinstance(vision_layers, (tuple, list)): + vision_heads = vision_width * 32 // 64 + self.visual = ModifiedResNet( + layers=vision_layers, + output_dim=embed_dim, + heads=vision_heads, + input_resolution=image_resolution, + width=vision_width + ) + else: + vision_heads = vision_width // 64 + self.visual = VisualTransformer( + input_resolution=image_resolution, + patch_size=vision_patch_size, + width=vision_width, + layers=vision_layers, + heads=vision_heads, + output_dim=embed_dim + ) + + self.transformer = Transformer( + width=transformer_width, + layers=transformer_layers, + heads=transformer_heads, + attn_mask=self.build_attention_mask() + ) + + self.vocab_size = vocab_size + self.token_embedding = nn.Embedding(vocab_size, transformer_width) + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width)) + self.ln_final = LayerNorm(transformer_width) + + self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim)) + self.logit_scale = nn.Parameter(torch.ones([])) + + self.initialize_parameters() + + def initialize_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + + if isinstance(self.visual, ModifiedResNet): + if self.visual.attnpool is not None: + std = self.visual.attnpool.c_proj.in_features ** -0.5 + nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std) + + for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + @property + def dtype(self): + return self.visual.conv1.weight.dtype + + def encode_image(self, image): + return self.visual(image.type(self.dtype)) + + def encode_text(self, text): + x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.type(self.dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x).type(self.dtype) + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + + return x + + def encode_text_with_embeddings(self, text): + x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.type(self.dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x).type(self.dtype) + + emb = x.clone() + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + + return x, emb + + def forward(self, image, text): + image_features = self.encode_image(image) + text_features = self.encode_text(text) + + # normalized features + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + + # cosine similarity as logits + logit_scale = self.logit_scale.exp() + logits_per_image = logit_scale * image_features @ text_features.t() + logits_per_text = logit_scale * text_features @ image_features.t() + + # shape = [global_batch_size, global_batch_size] + return logits_per_image, logits_per_text + + +def convert_weights(model: nn.Module): + """Convert applicable model parameters to fp16""" + + def _convert_weights_to_fp16(l): + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.half() + if l.bias is not None: + l.bias.data = l.bias.data.half() + + if isinstance(l, nn.MultiheadAttention): + for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: + tensor = getattr(l, attr) + if tensor is not None: + tensor.data = tensor.data.half() + + for name in ["text_projection", "proj"]: + if hasattr(l, name): + attr = getattr(l, name) + if attr is not None: + attr.data = attr.data.half() + + model.apply(_convert_weights_to_fp16) + + +def build_model(state_dict: dict): + vit = "visual.proj" in state_dict + + if vit: + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) + image_resolution = vision_patch_size * grid_size + else: + counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] + vision_layers = tuple(counts) + vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] + output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) + vision_patch_size = None + assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] + image_resolution = output_width * 32 + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) + + model = CLIP( + embed_dim, + image_resolution, vision_layers, vision_width, vision_patch_size, + context_length, vocab_size, transformer_width, transformer_heads, transformer_layers + ) + + # for key in ["input_resolution", "context_length", "vocab_size"]: + # del state_dict[key] + + convert_weights(model) + model.load_state_dict(state_dict, strict=False) + return model.eval() + + +def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")): + os.makedirs(root, exist_ok=True) + filename = os.path.basename(url) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256: + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.info().get("Content-Length")), ncols=80) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256: + raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") + + return download_target + + +def available_models(): + return list(_MODELS.keys()) + + +def load_clip(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit=False): + if name not in _MODELS: + raise RuntimeError(f"Model {name} not found; available models = {available_models()}") + + model_path = _download(_MODELS[name]) + model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() + n_px = model.input_resolution.item() + + transform = Compose([ + Resize(n_px, interpolation=Image.BICUBIC), + CenterCrop(n_px), + lambda image: image.convert("RGB"), + ToTensor(), + Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), + ]) + + if not jit: + model = build_model(model.state_dict()).to(device) + if str(device) == "cpu": + model.float() + return model, transform + + # patch the device names + device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) + device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] + + def patch_device(module): + graphs = [module.graph] if hasattr(module, "graph") else [] + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("prim::Constant"): + if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): + node.copyAttributes(device_node) + + model.apply(patch_device) + patch_device(model.encode_image) + patch_device(model.encode_text) + + # patch dtype to float32 on CPU + if str(device) == "cpu": + float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) + float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] + float_node = float_input.node() + + def patch_float(module): + graphs = [module.graph] if hasattr(module, "graph") else [] + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("aten::to"): + inputs = list(node.inputs()) + for i in [1, 2]: # dtype can be the second or third argument to aten::to() + if inputs[i].node()["value"] == 5: + inputs[i].node().copyAttributes(float_node) + + model.apply(patch_float) + patch_float(model.encode_image) + patch_float(model.encode_text) + + model.float() + + return model, transform + + +def tokenize(texts: Union[str, List[str]], context_length: int = 77): + if isinstance(texts, str): + texts = [texts] + + sot_token = _tokenizer.encoder["<|startoftext|>"] + eot_token = _tokenizer.encoder["<|endoftext|>"] + all_tokens = [[sot_token] + _tokenizer.encode([text]) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}") + result[i, :len(tokens)] = torch.tensor(tokens) + + return result diff --git a/cliport/models/core/fusion.py b/cliport/models/core/fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..00d0126a2de11c5b2ce419997aeb2836c2605971 --- /dev/null +++ b/cliport/models/core/fusion.py @@ -0,0 +1,355 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import numpy as np + + +class DotAttn(nn.Module): + """ Dot-Attention """ + + def forward(self, inp, h): + score = self.softmax(inp, h) + return score.expand_as(inp).mul(inp).sum(1), score + + def softmax(self, inp, h): + raw_score = inp.bmm(h.unsqueeze(2)) + score = F.softmax(raw_score, dim=1) + return score + + +class ScaledDotAttn(nn.Module): + """ Scaled Dot-Attention """ + + def forward(self, inp, h): + score = self.softmax(inp, h) + return score.expand_as(inp).mul(inp).sum(1), score + + def softmax(self, inp, h): + raw_score = inp.bmm(h.unsqueeze(2)) / np.sqrt(h.shape[-1]) + score = F.softmax(raw_score, dim=1) + return score + + +class Fusion(nn.Module): + """ Base Fusion Class""" + + def __init__(self, input_dim=3): + super().__init__() + self.input_dim = input_dim + + def tile_x2(self, x1, x2, x2_proj=None): + if x2_proj: + x2 = x2_proj(x2) + + x2 = x2.unsqueeze(-1).unsqueeze(-1) + x2 = x2.repeat(x1.shape[0], 1, x1.shape[-2], x1.shape[-1]) + return x2 + + def batch_tile_x2(self, x1, x2, x2_proj=None): + if x2_proj: + x2 = x2_proj(x2) + + x2 = x2.unsqueeze(-1).unsqueeze(-1) + x2 = x2.repeat(1, 1, x1.shape[-2], x1.shape[-1]) + return x2 + + def forward(self, x1, x2, x2_mask=None, x2_proj=None): + raise NotImplementedError() + + +class FusionAdd(Fusion): + """ x1 + x2 """ + + def __init__(self, input_dim=3): + super(FusionAdd, self).__init__(input_dim=input_dim) + + def forward(self, x1, x2, x2_mask=None, x2_proj=None): + if x1.shape != x2.shape and len(x1.shape) != len(x2.shape): + x2 = self.tile_x2(x1, x2, x2_proj) + return x1 + x2 + + +class FusionMult(Fusion): + """ x1 * x2 """ + + def __init__(self, input_dim=3): + super(FusionMult, self).__init__(input_dim=input_dim) + + def forward(self, x1, x2, x2_mask=None, x2_proj=None): + if x1.shape != x2.shape and len(x1.shape) != len(x2.shape): + x2 = self.batch_tile_x2(x1, x2, x2_proj) # self.batch_tile_x2(x1, x2, x2_proj) + return x1 * x2 + + +class FusionMax(Fusion): + """ max(x1, x2) """ + + def __init__(self, input_dim=3): + super(FusionMax, self).__init__(input_dim=input_dim) + + def forward(self, x1, x2, x2_mask=None, x2_proj=None): + if x1.shape != x2.shape and len(x1.shape) != len(x2.shape): + x2 = self.tile_x2(x1, x2, x2_proj) + return torch.max(x1, x2) + + +class FusionConcat(Fusion): + """ [x1; x2] """ + + def __init__(self, input_dim=3): + super(FusionConcat, self).__init__(input_dim=input_dim) + + def forward(self, x1, x2, x2_mask=None, x2_proj=None): + if x1.shape != x2.shape and len(x1.shape) != len(x2.shape): + x2 = self.tile_x2(x1, x2, x2_proj) + return torch.cat([x1, x2], dim=1) + + +class FusionConv(Fusion): + """ 1x1 convs after [x1; x2] """ + + def __init__(self, input_dim=3): + super(FusionConv, self).__init__(input_dim=input_dim) + self.conv = nn.Sequential( + nn.ReLU(True), + nn.Conv2d(input_dim * 2, input_dim, kernel_size=1, bias=False) + ) + + def forward(self, x1, x2, x2_mask=None, x2_proj=None): + if x1.shape != x2.shape and len(x1.shape) != len(x2.shape): + x2 = self.tile_x2(x1, x2, x2_proj) + x = torch.cat([x1, x2], dim=1) # [B, 2C, H, W] + x = self.conv(x) # [B, C, H, W] + return x + + +class FusionConvLat(Fusion): + """ 1x1 convs after [x1; x2] for lateral fusion """ + + def __init__(self, input_dim=3, output_dim=3): + super(FusionConvLat, self).__init__(input_dim=input_dim) + self.conv = nn.Sequential( + nn.ReLU(True), + nn.Conv2d(input_dim, output_dim, kernel_size=1, bias=False) + ) + + def forward(self, x1, x2, x2_mask=None, x2_proj=None): + if x1.shape != x2.shape and len(x1.shape) != len(x2.shape): + x2 = self.tile_x2(x1, x2, x2_proj) + x = torch.cat([x1, x2], dim=1) # [B, input_dim, H, W] + x = self.conv(x) # [B, output_dim, H, W] + return x + + +## ------------- NOTE ---------------- +## The following are various fusion types I experimented with. +## Most of them didn't work well ¯\_(ツ)_/¯ +## But it doesn't mean there isn't a better way of +## doing lateral and multi-modal (language+vision) fusion. + + +class FusionFiLM(Fusion): + """ FiLM (Perez et. al, https://arxiv.org/abs/1709.07871). + Note: This is not used inside a Residual block before ReLU. + I had a version this in UpBlock with FiLM, which didn't seem to work at all. + """ + + def __init__(self, input_dim=3, output_dim=3): + super(FusionFiLM, self).__init__(input_dim=input_dim) + + def forward(self, x1, x2, gamma, beta): + g = self.tile_x2(x1, x2, gamma) + b = self.tile_x2(x1, x2, beta) + return x1 * g + b + + +class FusionDeepConv(Fusion): + """ Multi-Layer 1x1 convs after [x1; x2] """ + + def __init__(self, input_dim=3): + super(FusionDeepConv, self).__init__(input_dim=input_dim) + self.conv = nn.Sequential( + nn.ReLU(True), + nn.Conv2d(input_dim * 2, input_dim, kernel_size=1, bias=False), + nn.ReLU(True), + nn.Conv2d(input_dim, input_dim, kernel_size=1, bias=False), + nn.ReLU(True), + nn.Conv2d(input_dim, input_dim, kernel_size=1, bias=False), + ) + + def forward(self, x1, x2, x2_mask=None, x2_proj=None): + if x1.shape != x2.shape and len(x1.shape) != len(x2.shape): + x2 = self.tile_x2(x1, x2, x2_proj) + x = torch.cat([x1, x2], dim=1) # [B, 2C, H, W] + x = self.conv(x) # [B, C, H, W] + return x + + +class FusionMultWord(nn.Module): + """ Product with weighted-sum of words """ + + def __init__(self, input_dim=3): + super().__init__() + self.input_dim = input_dim + + def forward(self, x1, x2, x2_mask=None, x2_proj=None): + B, D, H, W = x1.shape + x2_len = int(x2_mask.count_nonzero()) + + weighted_x1 = torch.zeros_like(x1) + for t in range(x2_len): + x2_t = x2_proj(x2[:,t]) if x2_proj else x2[:,t] + x2_t = x2_t.unsqueeze(-1).unsqueeze(-1).repeat(B, 1, H, W) + weighted_x1 += x1 * x2_t + weighted_x1 /= x2_len + return weighted_x1 + + +class FusionWordAttention(nn.Module): + """ Word Attention """ + + def __init__(self, input_dim=3): + super().__init__() + self.input_dim = input_dim + self.dot_attn = DotAttn() + + def forward(self, x1, x2, x2_mask=None, x2_proj=None): + B, D, H, W = x1.shape + x1_flat = x1.reshape(B, D, H*W) + x2_len = int(x2_mask.count_nonzero()) + + # TODO: batch this unrolling? + weight_sum_x1_flat = torch.zeros_like(x1_flat) + for t in range(x2_len): + x2_t = x2_proj(x2[:,t]) if x2_proj else x2[:,t] + x2_t = x2_t.repeat(B, 1) + + _, attn_x1 = self.dot_attn(x1_flat.transpose(1, 2), x2_t) + weight_sum_x1_flat += x1_flat * attn_x1.transpose(1, 2) + + weight_sum_x1_flat /= x2_len + x2 = weight_sum_x1_flat.reshape(B, D, H, W) + return x2 + + +class FusionSentenceAttention(nn.Module): + """ Sentence Attention """ + + def __init__(self, input_dim=3): + super().__init__() + self.input_dim = input_dim + self.dot_attn = ScaledDotAttn() + + def forward(self, x1, x2, x2_mask=None, x2_proj=None): + B, D, H, W = x1.shape + x1_flat = x1.reshape(B, D, H*W) + + x2_t = x2_proj(x2) if x2_proj else x2 + x2_t = x2_t.repeat(B, 1) + + _, attn_x1 = self.dot_attn(x1_flat.transpose(1, 2), x2_t) + weight_sum_x1_flat = x1_flat * attn_x1.transpose(1, 2) + + x2 = weight_sum_x1_flat.reshape(B, D, H, W) + return x2 + + +class CrossModalAttention2d(nn.Module): + """ Cross-Modal Attention. Adapted from: https://github.com/openai/CLIP/blob/main/clip/model.py#L56 """ + + def __init__(self, spacial_dim=7, embed_dim=1024, num_heads=32, + output_dim=1024, lang_dim=512, lang_max_tokens=77): + super().__init__() + self.embed_dim = embed_dim + self.lang_dim = lang_dim + self.lang_max_tokens = lang_max_tokens + self.num_heads = num_heads + self.lang_proj = nn.Linear(self.lang_dim, embed_dim) + self.vision_positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2, embed_dim) / embed_dim ** 0.5) + self.lang_positional_embedding = nn.Parameter(torch.randn(lang_max_tokens, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + + def forward(self, x, l, l_mask): + # reshape vision features + x_shape = x.shape + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC + x = x + self.vision_positional_embedding[:x.shape[0], None, :].to(x.dtype) # (HW)NC + + # project language + l = l.permute(1, 0, 2) + l_shape = l.shape + l = l.reshape(-1, self.lang_dim) + l = self.lang_proj(l) + l = l.reshape(l_shape[0], l_shape[1], self.embed_dim) + l = l + self.lang_positional_embedding[:, None, :].to(l.dtype) + + # hard language mask + l_len = int(l_mask.count_nonzero()) + l = l[:l_len] + l = l.repeat(1, x.shape[1], 1) + + x, _ = F.multi_head_attention_forward( + query=x, key=l, value=l, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0, + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + + x = x.permute(1, 2, 0) + x = x.reshape(x_shape) + return x + + +class FusionMultiHeadedWordAttention(nn.Module): + """ Multi-Headed Word Attention that uses Cross Modal Attention at different scales """ + + def __init__(self, input_dim=3): + super().__init__() + self.input_dim = input_dim + self.attn1 = CrossModalAttention2d(spacial_dim=7, embed_dim=1024, output_dim=1024) + self.attn2 = CrossModalAttention2d(spacial_dim=14, embed_dim=512, output_dim=512) + self.attn3 = CrossModalAttention2d(spacial_dim=28, embed_dim=256, output_dim=256) + + self.multi_headed_attns = { + 1024: self.attn1, + 512: self.attn2, + 256: self.attn3, + } + + def forward(self, x1, x2, x2_mask=None, x2_proj=None): + emb_dim = x1.shape[1] + x = self.multi_headed_attns[emb_dim](x1, x2, x2_mask) + return x + + +names = { + 'add': FusionAdd, + 'mult': FusionMult, + 'mult_word': FusionMultWord, + 'film': FusionFiLM, + 'max': FusionMax, + 'concat': FusionConcat, + 'conv': FusionConv, + 'deep_conv': FusionDeepConv, + 'word_attn': FusionWordAttention, + 'sent_attn': FusionSentenceAttention, + 'multi_headed_word_attn': FusionMultiHeadedWordAttention, +} + diff --git a/cliport/models/core/transport.py b/cliport/models/core/transport.py new file mode 100644 index 0000000000000000000000000000000000000000..bc81e33d800f9b9e1ce504c8c9352672037a32e4 --- /dev/null +++ b/cliport/models/core/transport.py @@ -0,0 +1,109 @@ +import numpy as np +import cliport.models as models +from cliport.utils import utils + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class Transport(nn.Module): + + def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device): + """Transport (a.k.a Place) module.""" + super().__init__() + + self.iters = 0 + self.stream_fcn = stream_fcn + self.n_rotations = n_rotations + self.crop_size = crop_size # crop size must be N*16 (e.g. 96) + self.preprocess = preprocess + self.cfg = cfg + self.device = device + self.batchnorm = self.cfg['train']['batchnorm'] + + self.pad_size = int(self.crop_size / 2) + self.padding = np.zeros((3, 2), dtype=int) + self.padding[:2, :] = self.pad_size + + in_shape = np.array(in_shape) + in_shape = tuple(in_shape) + self.in_shape = in_shape + + # Crop before network (default from Transporters CoRL 2020). + self.kernel_shape = (self.crop_size, self.crop_size, self.in_shape[2]) + + if not hasattr(self, 'output_dim'): + self.output_dim = 3 + if not hasattr(self, 'kernel_dim'): + self.kernel_dim = 3 + + self.rotator = utils.ImageRotator(self.n_rotations) + + self._build_nets() + + def _build_nets(self): + stream_one_fcn, _ = self.stream_fcn + model = models.names[stream_one_fcn] + self.key_resnet = model(self.in_shape, self.output_dim, self.cfg, self.device) + self.query_resnet = model(self.kernel_shape, self.kernel_dim, self.cfg, self.device) + print(f"Transport FCN: {stream_one_fcn}") + + def correlate(self, in0, in1, softmax): + """Correlate two input tensors.""" + output = F.conv2d(in0, in1, padding=(self.pad_size, self.pad_size)) + output = F.interpolate(output, size=(in0.shape[-2], in0.shape[-1]), mode='bilinear') + output = output[:,:,self.pad_size:-self.pad_size, self.pad_size:-self.pad_size] + output_shape = output.shape + + # a hack around the batch size 1. The shape needs to tile back. + channel_num = in1.shape[0] // in0.shape[0] + output = torch.stack([output[i,i*channel_num:(i+1)*channel_num] for i in range(len(output))], dim=0) + if softmax: + output = output.reshape((len(output), -1)) + output = F.softmax(output, dim=-1) + output = output.reshape(len(output),channel_num,output_shape[2],output_shape[3]) + + return output + + def transport(self, in_tensor, crop): + logits = self.key_resnet(in_tensor) + kernel = self.query_resnet(crop) + return logits, kernel + + def forward(self, inp_img, p, softmax=True): + """Forward pass.""" + img_unprocessed = np.pad(inp_img, self.padding, mode='constant') + input_data = img_unprocessed + in_shape = input_data.shape + if len(inp_shape) == 3: + inp_shape = (1,) + inp_shape + input_data = input_data.reshape(in_shape) # [B W H D] + in_tensor = torch.from_numpy(input_data).to(dtype=torch.float, device=self.device) + + # Rotation pivot. + pv = p + self.pad_size # np.array([p[0], p[1]]) + + # Crop before network (default from Transporters CoRL 2020). + hcrop = self.pad_size + in_tensor = in_tensor.permute(0, 3, 1, 2) # [B D W H] + + crop = in_tensor.repeat(self.n_rotations, 1, 1, 1) + crop = self.rotator(crop, pivot=pv) + crop = torch.cat(crop, dim=0) + crop = crop[:, :, pv[0]-hcrop:pv[0]+hcrop, pv[1]-hcrop:pv[1]+hcrop] + + logits, kernel = self.transport(in_tensor, crop) + + # TODO(Mohit): Crop after network. Broken for now. + # in_tensor = in_tensor.permute(0, 3, 1, 2) + # logits, crop = self.transport(in_tensor) + # crop = crop.repeat(self.n_rotations, 1, 1, 1) + # crop = self.rotator(crop, pivot=pv) + # crop = torch.cat(crop, dim=0) + + # kernel = crop[:, :, pv[0]-hcrop:pv[0]+hcrop, pv[1]-hcrop:pv[1]+hcrop] + # kernel = crop[:, :, p[0]:(p[0] + self.crop_size), p[1]:(p[1] + self.crop_size)] + + return self.correlate(logits, kernel, softmax) + diff --git a/cliport/models/core/transport_image_goal.py b/cliport/models/core/transport_image_goal.py new file mode 100644 index 0000000000000000000000000000000000000000..13882d4b493eeaefc7d404a5756ad87005278d13 --- /dev/null +++ b/cliport/models/core/transport_image_goal.py @@ -0,0 +1,129 @@ +import numpy as np +import cliport.models as models +from cliport.utils import utils + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class TransportImageGoal(nn.Module): + """Transport module.""" + + def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device): + """Transport module for placing. + Args: + in_shape: shape of input image. + n_rotations: number of rotations of convolving kernel. + crop_size: crop size around pick argmax used as convolving kernel. + preprocess: function to preprocess input images. + """ + super().__init__() + + self.iters = 0 + self.stream_fcn = stream_fcn + self.n_rotations = n_rotations + self.crop_size = crop_size # crop size must be N*16 (e.g. 96) + self.preprocess = preprocess + self.cfg = cfg + self.device = device + self.batchnorm = self.cfg['train']['batchnorm'] + + self.pad_size = int(self.crop_size / 2) + self.padding = np.zeros((3, 2), dtype=int) + self.padding[:2, :] = self.pad_size + + in_shape = np.array(in_shape) + in_shape = tuple(in_shape) + self.in_shape = in_shape + + # Crop before network (default for Transporters CoRL 2020). + self.kernel_shape = (self.crop_size, self.crop_size, self.in_shape[2]) + + if not hasattr(self, 'output_dim'): + self.output_dim = 3 + if not hasattr(self, 'kernel_dim'): + self.kernel_dim = 3 + + self.rotator = utils.ImageRotator(self.n_rotations) + + self._build_nets() + + def _build_nets(self): + stream_one_fcn, _ = self.stream_fcn + model = models.names[stream_one_fcn] + self.key_resnet = model(self.in_shape, self.output_dim, self.cfg, self.device) + self.query_resnet = model(self.in_shape, self.kernel_dim, self.cfg, self.device) + self.goal_resnet = model(self.in_shape, self.output_dim, self.cfg, self.device) + print(f"Transport FCN: {stream_one_fcn}") + + def correlate(self, in0, in1, softmax): + """Correlate two input tensors.""" + output = F.conv2d(in0, in1, padding=(self.pad_size, self.pad_size)) + output = F.interpolate(output, size=(in0.shape[-2], in0.shape[-1]), mode='bilinear') + output = output[:,:,self.pad_size:-self.pad_size, self.pad_size:-self.pad_size] + if softmax: + output_shape = output.shape + output = output.reshape((1, np.prod(output.shape))) + output = F.softmax(output, dim=-1) + output = output.reshape(output_shape[1:]) + return output + + def forward(self, inp_img, goal_img, p, softmax=True): + """Forward pass.""" + + # Input image. + img_unprocessed = np.pad(inp_img, self.padding, mode='constant') + input_data = img_unprocessed + in_shape = (1,) + input_data.shape + input_data = input_data.reshape(in_shape) + in_tensor = torch.from_numpy(input_data.copy()).to(dtype=torch.float, device=self.device) + in_tensor = in_tensor.permute(0, 3, 1, 2) + + # Goal image. + goal_tensor = np.pad(goal_img, self.padding, mode='constant') + goal_shape = (1,) + goal_tensor.shape + goal_tensor = goal_tensor.reshape(goal_shape) + goal_tensor = torch.from_numpy(goal_tensor.copy()).to(dtype=torch.float, device=self.device) + goal_tensor = goal_tensor.permute(0, 3, 1, 2) + + # Rotation pivot. + pv = np.array([p[0], p[1]]) + self.pad_size + hcrop = self.pad_size + + # Cropped input features. + in_crop = in_tensor.repeat(self.n_rotations, 1, 1, 1) + in_crop = self.rotator(in_crop, pivot=pv) + in_crop = torch.cat(in_crop, dim=0) + in_crop = in_crop[:, :, pv[0]-hcrop:pv[0]+hcrop, pv[1]-hcrop:pv[1]+hcrop] + + # Cropped goal features. + goal_crop = goal_tensor.repeat(self.n_rotations, 1, 1, 1) + goal_crop = self.rotator(goal_crop, pivot=pv) + goal_crop = torch.cat(goal_crop, dim=0) + goal_crop = goal_crop[:, :, pv[0]-hcrop:pv[0]+hcrop, pv[1]-hcrop:pv[1]+hcrop] + + in_logits = self.key_resnet(in_tensor) + goal_logits = self.goal_resnet(goal_tensor) + kernel_crop = self.query_resnet(in_crop) + goal_crop = self.goal_resnet(goal_crop) + + # Fuse Goal and Transport features + goal_x_in_logits = goal_logits + in_logits # Mohit: why doesn't multiply work? :( + goal_x_kernel = goal_crop + kernel_crop + + # TODO(Mohit): Crop after network. Broken for now + # in_logits = self.key_resnet(in_tensor) + # kernel_nocrop_logits = self.query_resnet(in_tensor) + # goal_logits = self.goal_resnet(goal_tensor) + + # goal_x_in_logits = in_logits + # goal_x_kernel_logits = goal_logits * kernel_nocrop_logits + + # goal_crop = goal_x_kernel_logits.repeat(self.n_rotations, 1, 1, 1) + # goal_crop = self.rotator(goal_crop, pivot=pv) + # goal_crop = torch.cat(goal_crop, dim=0) + # goal_crop = goal_crop[:, :, pv[0]-hcrop:pv[0]+hcrop, pv[1]-hcrop:pv[1]+hcrop] + + return self.correlate(goal_x_in_logits, goal_x_kernel, softmax) + diff --git a/cliport/models/core/unet.py b/cliport/models/core/unet.py new file mode 100644 index 0000000000000000000000000000000000000000..5aa28000fb2a35281e2291b31ae6e13b7c66fa59 --- /dev/null +++ b/cliport/models/core/unet.py @@ -0,0 +1,78 @@ +# Credit: https://github.com/milesial/Pytorch-UNet/ + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class DoubleConv(nn.Module): + """(convolution => [BN] => ReLU) * 2""" + + def __init__(self, in_channels, out_channels, mid_channels=None): + super().__init__() + if not mid_channels: + mid_channels = out_channels + self.double_conv = nn.Sequential( + nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1), + nn.BatchNorm2d(mid_channels), # (Mohit): argh... forgot to remove this batchnorm + nn.ReLU(inplace=True), + nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1), + nn.BatchNorm2d(out_channels), # (Mohit): argh... forgot to remove this batchnorm + nn.ReLU(inplace=True) + ) + + def forward(self, x): + return self.double_conv(x) + + +class Down(nn.Module): + """Downscaling with maxpool then double conv""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.maxpool_conv = nn.Sequential( + nn.MaxPool2d(2), + DoubleConv(in_channels, out_channels) + ) + + def forward(self, x): + return self.maxpool_conv(x) + + +class Up(nn.Module): + """Upscaling then double conv""" + + def __init__(self, in_channels, out_channels, bilinear=True): + super().__init__() + + # if bilinear, use the normal convolutions to reduce the number of channels + if bilinear: + self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) + self.conv = DoubleConv(in_channels, out_channels, in_channels // 2) + else: + self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2) + self.conv = DoubleConv(in_channels, out_channels) + + + def forward(self, x1, x2): + x1 = self.up(x1) + # input is CHW + diffY = x2.size()[2] - x1.size()[2] + diffX = x2.size()[3] - x1.size()[3] + + x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, + diffY // 2, diffY - diffY // 2]) + # if you have padding issues, see + # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a + # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd + x = torch.cat([x2, x1], dim=1) + return self.conv(x) + + +class OutConv(nn.Module): + def __init__(self, in_channels, out_channels): + super(OutConv, self).__init__() + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) + + def forward(self, x): + return self.conv(x) \ No newline at end of file diff --git a/cliport/models/mdetr_lingunet_lat_fuse.py b/cliport/models/mdetr_lingunet_lat_fuse.py new file mode 100644 index 0000000000000000000000000000000000000000..1e878ad360a6cc10be06af342d6804c9efc294b2 --- /dev/null +++ b/cliport/models/mdetr_lingunet_lat_fuse.py @@ -0,0 +1,356 @@ +import torch +import torch.nn.functional as F +from typing import List, Optional +from torch import Tensor, nn +import copy +from cliport.models.resnet import IdentityBlock, ConvBlock +from cliport.models.core.unet import Up + +from cliport.models.core import fusion +from cliport.models.core.fusion import FusionConvLat +from cliport.models.backbone_full import Backbone +from cliport.models.misc import NestedTensor +from cliport.models.position_encoding import build_position_encoding +from transformers import RobertaModel, RobertaTokenizerFast + + + +class FeatureResizer(nn.Module): + """ + This class takes as input a set of embeddings of dimension C1 and outputs a set of + embedding of dimension C2, after a linear transformation, dropout and normalization (LN). + """ + + def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True): + super().__init__() + self.do_ln = do_ln + # Object feature encoding + self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True) + self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12) + self.dropout = nn.Dropout(dropout) + + def forward(self, encoder_features): + x = self.fc(encoder_features) + if self.do_ln: + x = self.layer_norm(x) + output = self.dropout(x) + return output + + +class MDETRLingUNetLat_fuse(nn.Module): + """ CLIP RN50 with U-Net skip connections and lateral connections """ + + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super(MDETRLingUNetLat_fuse, self).__init__() + self.input_shape = input_shape + self.output_dim = output_dim + self.input_dim = 2048 # penultimate layer channel-size of mdetr + self.cfg = cfg + self.device = device + self.batchnorm = self.cfg['train']['batchnorm'] + self.lang_fusion_type = self.cfg['train']['lang_fusion_type'] + self.bilinear = True + self.up_factor = 2 if self.bilinear else 1 + self.preprocess = preprocess + + self.backbone = Backbone('resnet101', True, True, False) + self.position_embedding = build_position_encoding() + self.input_proj = nn.Conv2d(2048, 256, kernel_size=1) + + self.tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base') + self.text_encoder = RobertaModel.from_pretrained('roberta-base') + self.resizer = FeatureResizer( + input_feat_size=768, + output_feat_size=256, + dropout=0.1, + ) + encoder_layer = TransformerEncoderLayer(d_model=256, nhead=8, dim_feedforward=2048, dropout=0.1, activation='relu', normalize_before=False) + self.encoder = TransformerEncoder(encoder_layer, 6, None) + mdter_checkpoint = torch.load('/home/yzc/shared/project/GPT-CLIPort/ckpts/mdetr_pretrained_resnet101_checkpoint.pth', map_location="cpu")['model'] + + checkpoint_new = {} + for param in mdter_checkpoint: + if 'transformer.text_encoder' in param or 'transformer.encoder.' in param or 'input_proj' in param or 'resizer' in param: + param_new = param.replace('transformer.','') + checkpoint_new[param_new] = mdter_checkpoint[param] + elif 'backbone.0.body' in param: + param_new = param.replace('backbone.0.body', 'backbone.body') + checkpoint_new[param_new] = mdter_checkpoint[param] + + self.load_state_dict(checkpoint_new, True) + self._build_decoder() + + + def _build_decoder(self): + # language + self.up_fuse1 = nn.UpsamplingBilinear2d(scale_factor=2) + self.up_fuse2 = nn.UpsamplingBilinear2d(scale_factor=4) + self.up_fuse3 = nn.UpsamplingBilinear2d(scale_factor=8) + + self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2) + self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4) + self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8) + + self.proj_input_dim = 768 + self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024) + self.lang_proj2 = nn.Linear(self.proj_input_dim, 512) + self.lang_proj3 = nn.Linear(self.proj_input_dim, 256) + + # vision + self.conv1 = nn.Sequential( + nn.Conv2d(self.input_dim+256, 1024, kernel_size=3, stride=1, padding=1, bias=False), + nn.ReLU(True) + ) + + self.up1 = Up(2048+256, 1024 // self.up_factor, self.bilinear) + self.lat_fusion1 = FusionConvLat(input_dim=1024+512, output_dim=512) + + self.up2 = Up(1024+256, 512 // self.up_factor, self.bilinear) + self.lat_fusion2 = FusionConvLat(input_dim=512+256, output_dim=256) + + self.up3 = Up(512+256, 256 // self.up_factor, self.bilinear) + self.lat_fusion3 = FusionConvLat(input_dim=256+128, output_dim=128) + + self.layer1 = nn.Sequential( + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion4 = FusionConvLat(input_dim=128+64, output_dim=64) + + self.layer2 = nn.Sequential( + ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion5 = FusionConvLat(input_dim=64+32, output_dim=32) + + self.layer3 = nn.Sequential( + ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion6 = FusionConvLat(input_dim=32+16, output_dim=16) + + self.conv2 = nn.Sequential( + nn.Conv2d(16, self.output_dim, kernel_size=1) + ) + + def encode_image(self, img): + img = NestedTensor.from_tensor_list(img) + with torch.no_grad(): + xs = self.backbone(img) + out = [] + pos = [] + for name, x in xs.items(): + out.append(x) + # position encoding + pos.append(self.position_embedding(x).to(x.tensors.dtype)) + return out, pos + + + def encode_text(self, x): + with torch.no_grad(): + tokenized = self.tokenizer.batch_encode_plus(x, padding="longest", return_tensors="pt").to(self.device) + encoded_text = self.text_encoder(**tokenized) + + # Transpose memory because pytorch's attention expects sequence first + text_memory = encoded_text.last_hidden_state.transpose(0, 1) + text_memory_mean = torch.mean(text_memory, 0) + # Invert attention mask that we get from huggingface because its the opposite in pytorch transformer + text_attention_mask = tokenized.attention_mask.ne(1).bool() + # Resize the encoder hidden states to be of the same d_model as the decoder + text_memory_resized = self.resizer(text_memory) + return text_memory_resized, text_attention_mask, text_memory_mean + + def forward(self, x, lat, l): + + x = self.preprocess(x, dist='mdetr') + + in_type = x.dtype + in_shape = x.shape + x = x[:,:3] # select RGB + + x = x.permute(0, 1, 3, 2) + + + with torch.no_grad(): + features, pos = self.encode_image(x) + x1, mask = features[-1].decompose() + x2, _ = features[-2].decompose() + x3, _ = features[-3].decompose() + x4, _ = features[-4].decompose() + #print(x1.shape, x2.shape, x3.shape, x4.shape) + src = self.input_proj(x1) + pos_embed = pos[-1] + bs, c, h, w = src.shape + src = src.flatten(2).permute(2, 0, 1) + device = self.device + pos_embed = pos_embed.flatten(2).permute(2, 0, 1) + mask = mask.flatten(1) + if x.shape[0] == 1 or x.shape[0] == 36: + l = [l] + text_memory_resized, text_attention_mask, l_input = self.encode_text(l) + else: + text_memory_resized, text_attention_mask, l_input = self.encode_text(l) + # l_input = l_input.view(1, -1) + # text_memory_resized = text_memory_resized.repeat(1, src.shape[1], 1) + # text_attention_mask = text_attention_mask.repeat(src.shape[1], 1) + #print(src.shape, text_memory_resized.shape, mask.shape, text_attention_mask.shape) + if (x.shape[0] > 8) and ((x.shape[0] % 36) == 0): + text_memory_resized = text_memory_resized.repeat_interleave(36, dim=1) + l_input = l_input.repeat_interleave(36, dim=0) + text_attention_mask = text_attention_mask.repeat_interleave(36, dim=0) + src = torch.cat([src, text_memory_resized], dim=0) + # For mask, sequence dimension is second + mask = torch.cat([mask, text_attention_mask], dim=1) + # Pad the pos_embed with 0 so that the addition will be a no-op for the text tokens + pos_embed = torch.cat([pos_embed, torch.zeros_like(text_memory_resized)], dim=0) + img_memory, img_memory_all = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) + + dim = img_memory.shape[-1] + fuse1 = img_memory_all[-1][:h*w].permute(1,2,0).reshape(bs, dim, h, w) + fuse2 = self.up_fuse1(img_memory_all[-2][:h*w].permute(1,2,0).reshape(bs, dim, h, w)) + fuse3 = self.up_fuse2(img_memory_all[-3][:h*w].permute(1,2,0).reshape(bs, dim, h, w)) + fuse4 = self.up_fuse3(img_memory_all[-4][:h*w].permute(1,2,0).reshape(bs, dim, h, w)) + + assert x1.shape[1] == self.input_dim + + x1 = torch.cat((x1, fuse1), 1) + x2 = torch.cat((x2, fuse2), 1) + x3 = torch.cat((x3, fuse3), 1) + x4 = torch.cat((x4, fuse4), 1) + + x = self.conv1(x1) + x = self.lang_fuser1(x, l_input, x2_mask=None, x2_proj=self.lang_proj1) + x = self.up1(x, x2) + x = self.lat_fusion1(x, lat[-6].permute(0, 1, 3, 2)) + + x = self.lang_fuser2(x, l_input, x2_mask=None, x2_proj=self.lang_proj2) + + x = self.up2(x, x3) + x = self.lat_fusion2(x, lat[-5].permute(0, 1, 3, 2)) + + x = self.lang_fuser3(x, l_input, x2_mask=None, x2_proj=self.lang_proj3) + x = self.up3(x, x4) + x = self.lat_fusion3(x, lat[-4].permute(0, 1, 3, 2)) + x = self.layer1(x) + x = self.lat_fusion4(x, lat[-3].permute(0, 1, 3, 2)) + + x = self.layer2(x) + x = self.lat_fusion5(x, lat[-2].permute(0, 1, 3, 2)) + + x = self.layer3(x) + x = self.lat_fusion6(x, lat[-1].permute(0, 1, 3, 2)) + + x = self.conv2(x) + + x = F.interpolate(x, size=(in_shape[-1], in_shape[-2]), mode='bilinear') + x = x.permute(0, 1, 3, 2) + return x + + +class TransformerEncoder(nn.Module): + def __init__(self, encoder_layer, num_layers, norm=None): + super().__init__() + self.layers = _get_clones(encoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + + def forward( + self, + src, + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + + output = src + output_all = [] + for layer in self.layers: + output = layer(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos) + output_all.append(output) + if self.norm is not None: + output = self.norm(output) + + return output, output_all + +class TransformerEncoderLayer(nn.Module): + def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + print(self.normalize_before) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + q = k = self.with_pos_embed(src, pos) + src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + return src + + def forward_pre( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + src2 = self.norm1(src) + q = k = self.with_pos_embed(src2, pos) + src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] + src = src + self.dropout1(src2) + src2 = self.norm2(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) + src = src + self.dropout2(src2) + return src + + def forward( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + if self.normalize_before: + return self.forward_pre(src, src_mask, src_key_padding_mask, pos) + return self.forward_post(src, src_mask, src_key_padding_mask, pos) + + +def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(f"activation should be relu/gelu, not {activation}.") + diff --git a/cliport/models/misc.py b/cliport/models/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..2d326fab6b57317110af5fa7b722a33d4ffe7908 --- /dev/null +++ b/cliport/models/misc.py @@ -0,0 +1,162 @@ +# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Misc functions, including distributed helpers. + +Mostly copy-paste from torchvision references. +""" +import os +import subprocess +from typing import Any, Dict, List, Optional + +import torch +import torchvision +from torch import Tensor + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode("ascii").strip() + + sha = "N/A" + diff = "clean" + branch = "N/A" + try: + sha = _run(["git", "rev-parse", "HEAD"]) + subprocess.check_output(["git", "diff"], cwd=cwd) + diff = _run(["git", "diff-index", "HEAD"]) + diff = "has uncommited changes" if diff else "clean" + branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"]) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +def collate_fn(do_round, batch): + batch = list(zip(*batch)) + final_batch = {} + final_batch["samples"] = NestedTensor.from_tensor_list(batch[0], do_round) + final_batch["targets"] = batch[1] + if "positive_map" in batch[1][0]: + # we batch the positive maps here + # Since in general each batch element will have a different number of boxes, + # we collapse a single batch dimension to avoid padding. This is sufficient for our purposes. + max_len = max([v["positive_map"].shape[1] for v in batch[1]]) + nb_boxes = sum([v["positive_map"].shape[0] for v in batch[1]]) + batched_pos_map = torch.zeros((nb_boxes, max_len), dtype=torch.bool) + cur_count = 0 + for v in batch[1]: + cur_pos = v["positive_map"] + batched_pos_map[cur_count : cur_count + len(cur_pos), : cur_pos.shape[1]] = cur_pos + cur_count += len(cur_pos) + + assert cur_count == len(batched_pos_map) + # assert batched_pos_map.sum().item() == sum([v["positive_map"].sum().item() for v in batch[1]]) + final_batch["positive_map"] = batched_pos_map.float() + if "positive_map_eval" in batch[1][0]: + # we batch the positive maps here + # Since in general each batch element will have a different number of boxes, + # we collapse a single batch dimension to avoid padding. This is sufficient for our purposes. + max_len = max([v["positive_map_eval"].shape[1] for v in batch[1]]) + nb_boxes = sum([v["positive_map_eval"].shape[0] for v in batch[1]]) + batched_pos_map = torch.zeros((nb_boxes, max_len), dtype=torch.bool) + cur_count = 0 + for v in batch[1]: + cur_pos = v["positive_map_eval"] + batched_pos_map[cur_count : cur_count + len(cur_pos), : cur_pos.shape[1]] = cur_pos + cur_count += len(cur_pos) + + assert cur_count == len(batched_pos_map) + # assert batched_pos_map.sum().item() == sum([v["positive_map"].sum().item() for v in batch[1]]) + final_batch["positive_map_eval"] = batched_pos_map.float() + if "answer" in batch[1][0] or "answer_type" in batch[1][0]: + answers = {} + for f in batch[1][0].keys(): + if "answer" not in f: + continue + answers[f] = torch.stack([b[f] for b in batch[1]]) + final_batch["answers"] = answers + + return final_batch + + +class NestedTensor(object): + def __init__(self, tensors, mask): + self.tensors = tensors + self.mask = mask + + def to(self, *args, **kwargs): + cast_tensor = self.tensors.to(*args, **kwargs) + cast_mask = self.mask.to(*args, **kwargs) if self.mask is not None else None + return type(self)(cast_tensor, cast_mask) + + def decompose(self): + return self.tensors, self.mask + + @classmethod + def from_tensor_list(cls, tensor_list, do_round=False): + # TODO make this more general + if tensor_list[0].ndim == 3: + # TODO make it support different-sized images + max_size = tuple(max(s) for s in zip(*[img.shape for img in tensor_list])) + # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) + batch_shape = (len(tensor_list),) + max_size + b, c, h, w = batch_shape + if do_round: + # Round to an even size to avoid rounding issues in fpn + p = 128 + h = h if h % p == 0 else (h // p + 1) * p + w = w if w % p == 0 else (w // p + 1) * p + batch_shape = b, c, h, w + + dtype = tensor_list[0].dtype + device = tensor_list[0].device + tensor = torch.zeros(batch_shape, dtype=dtype, device=device) + mask = torch.ones((b, h, w), dtype=torch.bool, device=device) + for img, pad_img, m in zip(tensor_list, tensor, mask): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + m[: img.shape[1], : img.shape[2]] = False + else: + raise ValueError("not supported") + return cls(tensor, mask) + + def __repr__(self): + return repr(self.tensors) + + +def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): + # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor + """ + Equivalent to nn.functional.interpolate, but with support for empty channel sizes. + """ + if input.numel() > 0: + return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners) + + assert input.shape[0] != 0 or input.shape[1] != 0, "At least one of the two first dimensions must be non zero" + + if input.shape[1] == 0: + # Pytorch doesn't support null dimension on the channel dimension, so we transpose to fake a null batch dim + return torch.nn.functional.interpolate(input.transpose(0, 1), size, scale_factor, mode, align_corners).transpose(0, 1) + + # empty batch dimension is now supported in pytorch + return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners) + + + +def targets_to(targets: List[Dict[str, Any]], device): + """Moves the target dicts to the given device.""" + excluded_keys = [ + "questionId", + "tokens_positive", + "tokens", + "dataset_name", + "sentence_id", + "original_img_id", + "nb_eval", + "task_id", + "original_id", + ] + return [{k: v.to(device) if k not in excluded_keys else v for k, v in t.items() if k != "caption"} for t in targets] diff --git a/cliport/models/position_encoding.py b/cliport/models/position_encoding.py new file mode 100644 index 0000000000000000000000000000000000000000..97d59c800327eea0c3c3f500c37f09afb260c542 --- /dev/null +++ b/cliport/models/position_encoding.py @@ -0,0 +1,92 @@ +# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Various positional encodings for the transformer. +""" +import math + +import torch +from torch import nn + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, tensor_list): + x = tensor_list.tensors + mask = tensor_list.mask + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +class PositionEmbeddingLearned(nn.Module): + """ + Absolute pos embedding, learned. + """ + + def __init__(self, num_pos_feats=256): + super().__init__() + self.row_embed = nn.Embedding(50, num_pos_feats) + self.col_embed = nn.Embedding(50, num_pos_feats) + self.reset_parameters() + + def reset_parameters(self): + nn.init.uniform_(self.row_embed.weight) + nn.init.uniform_(self.col_embed.weight) + + def forward(self, tensor_list): + x = tensor_list.tensors + h, w = x.shape[-2:] + i = torch.arange(w, device=x.device) + j = torch.arange(h, device=x.device) + x_emb = self.col_embed(i) + y_emb = self.row_embed(j) + pos = ( + torch.cat( + [ + x_emb.unsqueeze(0).repeat(h, 1, 1), + y_emb.unsqueeze(1).repeat(1, w, 1), + ], + dim=-1, + ) + .permute(2, 0, 1) + .unsqueeze(0) + .repeat(x.shape[0], 1, 1, 1) + ) + return pos + + +def build_position_encoding(hidden_dim=256): + N_steps = hidden_dim // 2 + position_embedding = PositionEmbeddingSine(N_steps, normalize=True) + + return position_embedding diff --git a/cliport/models/pretrain_resnet.py b/cliport/models/pretrain_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..75b167a9ce9026425f20049b51ebddd3e3997329 --- /dev/null +++ b/cliport/models/pretrain_resnet.py @@ -0,0 +1,144 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils + +from cliport.models.resnet import ConvBlock, IdentityBlock +from torchvision.models import resnet18, resnet34, resnet50 + +class PretrainedResNet18(nn.Module): + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super(PretrainedResNet18, self).__init__() + self.input_shape = input_shape + self.input_dim = input_shape[-1] + self.output_dim = output_dim + self.cfg = cfg + self.device = device + self.batchnorm = self.cfg['train']['batchnorm'] + self.preprocess = preprocess + self.pretrained_model = resnet18(pretrained=True) + self.pretrained_model.avgpool = nn.Identity() + self.pretrained_model.fc = nn.Identity() + # self.pretrained_model.eval() + self.pretrained_model.conv1 = nn.Conv2d(self.input_dim, 64, kernel_size=2, stride=1, padding=3, bias=False) + # import IPython; IPython.embed() + for param in self.pretrained_model.parameters(): + param.requires_grad = False + self.pretrained_model.conv1.weight.requires_grad = True + + self._make_layers() + + def _make_layers(self): + # conv1 + # self.conv1 = nn.Sequential( + # nn.Conv2d(self.input_dim, 64, stride=1, kernel_size=3, padding=1), + # nn.BatchNorm2d(64) if self.batchnorm else nn.Identity(), + # nn.ReLU(True), + # ) + + # # fcn + # self.layer1 = nn.Sequential( + # ConvBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # ) + + # self.layer2 = nn.Sequential( + # ConvBlock(64, [128, 128, 128], kernel_size=3, stride=2, batchnorm=self.batchnorm), + # IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # ) + + # self.layer3 = nn.Sequential( + # ConvBlock(128, [256, 256, 256], kernel_size=3, stride=2, batchnorm=self.batchnorm), + # IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # ) + + # self.layer4 = nn.Sequential( + # ConvBlock(256, [512, 512, 512], kernel_size=3, stride=2, batchnorm=self.batchnorm), + # IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # ) + + # self.layer5 = nn.Sequential( + # ConvBlock(512, [1024, 1024, 1024], kernel_size=3, stride=2, batchnorm=self.batchnorm), + # IdentityBlock(1024, [1024, 1024, 1024], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # ) + + # # head + # self.layer6 = nn.Sequential( + # ConvBlock(1024, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # nn.UpsamplingBilinear2d(scale_factor=2), + # ) + + self.layer7 = nn.Sequential( + ConvBlock(512, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer8 = nn.Sequential( + ConvBlock(256, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer9 = nn.Sequential( + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer10 = nn.Sequential( + ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + # conv2 + self.conv2 = nn.Sequential( + ConvBlock(128, [16, 16, self.output_dim], kernel_size=3, stride=1, + final_relu=False, batchnorm=self.batchnorm), + IdentityBlock(self.output_dim, [16, 16, self.output_dim], kernel_size=3, stride=1, + final_relu=False, batchnorm=self.batchnorm) + ) + + def forward(self, x): + x = self.preprocess(x, dist='transporter') + in_shape = x.shape + + # # encoder + # for layer in [self.conv1, self.layer1, self.layer2, self.layer3, self.layer4, self.layer5]: + # x = layer(x) + + # # decoder + # im = [] + # for layer in [self.layer6, self.layer7, self.layer8, self.layer9, self.layer10, self.conv2]: + # im.append(x) + # x = layer(x) + + # encoder + # for layer in [self.conv1, self.layer1, self.layer2, self.layer3, self.layer4]: + # x = layer(x) + # x = x[:, :3, :, :] + x = self.pretrained_model.conv1(x) + for name, module in self.pretrained_model._modules.items(): + if name == 'conv1': + continue + x = module(x) + if name == 'layer4': + break + # with torch.no_grad(): + # x = self.pretrained_model(x) + # import ipdb;ipdb.set_trace() + + + x = F.interpolate(x, size=(8, 8), mode='bilinear') + + # decoder + im = [] + for layer in [self.layer7, self.layer8, self.conv2]: + im.append(x) + x = layer(x) + + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x, im \ No newline at end of file diff --git a/cliport/models/resnet.py b/cliport/models/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..079b10a53fddbdcd292e72e6903091fa6545fbb2 --- /dev/null +++ b/cliport/models/resnet.py @@ -0,0 +1,122 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils + + +class IdentityBlock(nn.Module): + def __init__(self, in_planes, filters, kernel_size, stride=1, final_relu=True, batchnorm=True): + super(IdentityBlock, self).__init__() + self.final_relu = final_relu + self.batchnorm = batchnorm + + filters1, filters2, filters3 = filters + self.conv1 = nn.Conv2d(in_planes, filters1, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(filters1) if self.batchnorm else nn.Identity() + self.conv2 = nn.Conv2d(filters1, filters2, kernel_size=kernel_size, dilation=1, + stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(filters2) if self.batchnorm else nn.Identity() + self.conv3 = nn.Conv2d(filters2, filters3, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(filters3) if self.batchnorm else nn.Identity() + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out += x + if self.final_relu: + out = F.relu(out) + return out + + +class ConvBlock(nn.Module): + def __init__(self, in_planes, filters, kernel_size, stride=1, final_relu=True, batchnorm=True): + super(ConvBlock, self).__init__() + self.final_relu = final_relu + self.batchnorm = batchnorm + + filters1, filters2, filters3 = filters + self.conv1 = nn.Conv2d(in_planes, filters1, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(filters1) if self.batchnorm else nn.Identity() + self.conv2 = nn.Conv2d(filters1, filters2, kernel_size=kernel_size, dilation=1, + stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(filters2) if self.batchnorm else nn.Identity() + self.conv3 = nn.Conv2d(filters2, filters3, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(filters3) if self.batchnorm else nn.Identity() + + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, filters3, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(filters3) if self.batchnorm else nn.Identity() + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out += self.shortcut(x) + if self.final_relu: + out = F.relu(out) + return out + + +class ResNet43_8s(nn.Module): + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super(ResNet43_8s, self).__init__() + self.input_shape = input_shape + self.input_dim = input_shape[-1] + self.output_dim = output_dim + self.cfg = cfg + self.device = device + self.batchnorm = self.cfg['train']['batchnorm'] + self.preprocess = preprocess + + self.layers = self._make_layers() + + def _make_layers(self): + layers = nn.Sequential( + # conv1 + nn.Conv2d(self.input_dim, 64, stride=1, kernel_size=3, padding=1), + nn.BatchNorm2d(64) if self.batchnorm else nn.Identity(), + nn.ReLU(True), + + # fcn + ConvBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + + ConvBlock(64, [128, 128, 128], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + + ConvBlock(128, [256, 256, 256], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + + ConvBlock(256, [512, 512, 512], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + + # head + ConvBlock(512, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + + ConvBlock(256, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + + # conv2 + ConvBlock(64, [16, 16, self.output_dim], kernel_size=3, stride=1, + final_relu=False, batchnorm=self.batchnorm), + IdentityBlock(self.output_dim, [16, 16, self.output_dim], kernel_size=3, stride=1, + final_relu=False, batchnorm=self.batchnorm), + ) + return layers + + def forward(self, x): + x = self.preprocess(x, dist='transporter') + + out = self.layers(x) + return out \ No newline at end of file diff --git a/cliport/models/resnet_lang.py b/cliport/models/resnet_lang.py new file mode 100644 index 0000000000000000000000000000000000000000..0d28cf6b2b859c9750f7061b12cd6fec07176257 --- /dev/null +++ b/cliport/models/resnet_lang.py @@ -0,0 +1,118 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils +from transformers import DistilBertTokenizer, DistilBertModel +from cliport.models.core import fusion +from cliport.models.resnet import ConvBlock, IdentityBlock + + +class ResNet43_8s_lang(nn.Module): + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super(ResNet43_8s_lang, self).__init__() + self.input_shape = input_shape + self.input_dim = input_shape[-1] + self.output_dim = output_dim + self.cfg = cfg + self.device = device + self.batchnorm = self.cfg['train']['batchnorm'] + self.lang_fusion_type = self.cfg['train']['lang_fusion_type'] + self.preprocess = preprocess + + self._make_layers() + + def _make_layers(self): + self.conv1 = nn.Sequential( + # conv1 + nn.Conv2d(self.input_dim, 64, stride=1, kernel_size=3, padding=1), + nn.BatchNorm2d(64) if self.batchnorm else nn.Identity(), + nn.ReLU(True), + + # fcn + ConvBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + + ConvBlock(64, [128, 128, 128], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + + ConvBlock(128, [256, 256, 256], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + + ConvBlock(256, [512, 512, 512], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + + # decoders + self.decoder1 = nn.Sequential( + ConvBlock(512, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.decoder2 = nn.Sequential( + ConvBlock(256, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.decoder3 = nn.Sequential( + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.conv2 = nn.Sequential( + # conv2 + ConvBlock(64, [16, 16, self.output_dim], kernel_size=3, stride=1, + final_relu=False, batchnorm=self.batchnorm), + IdentityBlock(self.output_dim, [16, 16, self.output_dim], kernel_size=3, stride=1, + final_relu=False, batchnorm=self.batchnorm), + ) + + self.tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') + self.text_encoder = DistilBertModel.from_pretrained('distilbert-base-uncased') + self.text_fc = nn.Linear(768, 1024) + + self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2) + self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4) + self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8) + + self.proj_input_dim = 512 if 'word' in self.lang_fusion_type else 1024 + self.lang_proj1 = nn.Linear(self.proj_input_dim, 512) + self.lang_proj2 = nn.Linear(self.proj_input_dim, 256) + self.lang_proj3 = nn.Linear(self.proj_input_dim, 128) + + def encode_text(self, l): + with torch.no_grad(): + inputs = self.tokenizer(l, return_tensors='pt') + input_ids, attention_mask = inputs['input_ids'].to(self.device), inputs['attention_mask'].to(self.device) + text_embeddings = self.text_encoder(input_ids, attention_mask) + text_encodings = text_embeddings.last_hidden_state.mean(1) + text_feat = self.text_fc(text_encodings) + text_mask = torch.ones_like(input_ids) # [1, max_token_len] + return text_feat, text_embeddings.last_hidden_state, text_mask + + def forward(self, x, l): + x = self.preprocess(x, dist='transporter') + + # encode language + l_enc, l_emb, l_mask = self.encode_text(l) + l_input = l_emb if 'word' in self.lang_fusion_type else l_enc + l_input = l_input.to(dtype=x.dtype) + + x = self.conv1(x) + + x = self.lang_fuser1(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj1) + x = self.decoder1(x) + + x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2) + x = self.decoder2(x) + + x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3) + x = self.decoder3(x) + + out = self.conv2(x) + + return out \ No newline at end of file diff --git a/cliport/models/resnet_lat.py b/cliport/models/resnet_lat.py new file mode 100644 index 0000000000000000000000000000000000000000..b7ca39891552f4eb51a5fe25776cb72d534126e6 --- /dev/null +++ b/cliport/models/resnet_lat.py @@ -0,0 +1,121 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils + +from cliport.models.resnet import ConvBlock, IdentityBlock + +class ResNet45_10s(nn.Module): + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super(ResNet45_10s, self).__init__() + self.input_shape = input_shape + self.input_dim = input_shape[-1] + self.output_dim = output_dim + self.cfg = cfg + self.device = device + self.batchnorm = self.cfg['train']['batchnorm'] + self.preprocess = preprocess + # import IPython; IPython.embed() + + self._make_layers() + + def _make_layers(self): + # conv1 + self.conv1 = nn.Sequential( + nn.Conv2d(self.input_dim, 64, stride=1, kernel_size=3, padding=1), + nn.BatchNorm2d(64) if self.batchnorm else nn.Identity(), + nn.ReLU(True), + ) + + # fcn + self.layer1 = nn.Sequential( + ConvBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + self.layer2 = nn.Sequential( + ConvBlock(64, [128, 128, 128], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + self.layer3 = nn.Sequential( + ConvBlock(128, [256, 256, 256], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + self.layer4 = nn.Sequential( + ConvBlock(256, [512, 512, 512], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + # self.layer5 = nn.Sequential( + # ConvBlock(512, [1024, 1024, 1024], kernel_size=3, stride=2, batchnorm=self.batchnorm), + # IdentityBlock(1024, [1024, 1024, 1024], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # ) + + # # head + # self.layer6 = nn.Sequential( + # ConvBlock(1024, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # nn.UpsamplingBilinear2d(scale_factor=2), + # ) + + self.layer7 = nn.Sequential( + ConvBlock(512, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer8 = nn.Sequential( + ConvBlock(256, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer9 = nn.Sequential( + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer10 = nn.Sequential( + ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + # conv2 + self.conv2 = nn.Sequential( + ConvBlock(32, [16, 16, self.output_dim], kernel_size=3, stride=1, + final_relu=False, batchnorm=self.batchnorm), + IdentityBlock(self.output_dim, [16, 16, self.output_dim], kernel_size=3, stride=1, + final_relu=False, batchnorm=self.batchnorm) + ) + + def forward(self, x): + x = self.preprocess(x, dist='transporter') + in_shape = x.shape + + # # encoder + # for layer in [self.conv1, self.layer1, self.layer2, self.layer3, self.layer4, self.layer5]: + # x = layer(x) + + # # decoder + # im = [] + # for layer in [self.layer6, self.layer7, self.layer8, self.layer9, self.layer10, self.conv2]: + # im.append(x) + # x = layer(x) + + # encoder + for layer in [self.conv1, self.layer1, self.layer2, self.layer3, self.layer4]: + x = layer(x) + + # decoder + im = [] + for layer in [self.layer7, self.layer8, self.layer9, self.layer10, self.conv2]: + im.append(x) + x = layer(x) + + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x, im \ No newline at end of file diff --git a/cliport/models/resnet_lat_origin.py b/cliport/models/resnet_lat_origin.py new file mode 100644 index 0000000000000000000000000000000000000000..9b2776979b2b6e11d416ab0aef30de5384a7d8d3 --- /dev/null +++ b/cliport/models/resnet_lat_origin.py @@ -0,0 +1,110 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils + +from cliport.models.resnet import ConvBlock, IdentityBlock + +class ResNet45_10s_origin(nn.Module): + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super(ResNet45_10s_origin, self).__init__() + self.input_shape = input_shape + self.input_dim = input_shape[-1] + self.output_dim = output_dim + self.cfg = cfg + self.device = device + self.batchnorm = self.cfg['train']['batchnorm'] + self.preprocess = preprocess + + self._make_layers() + + def _make_layers(self): + # conv1 + self.conv1 = nn.Sequential( + nn.Conv2d(self.input_dim, 64, stride=1, kernel_size=3, padding=1), + nn.BatchNorm2d(64) if self.batchnorm else nn.Identity(), + nn.ReLU(True), + ) + + # fcn + self.layer1 = nn.Sequential( + ConvBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + self.layer2 = nn.Sequential( + ConvBlock(64, [128, 128, 128], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + self.layer3 = nn.Sequential( + ConvBlock(128, [256, 256, 256], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + self.layer4 = nn.Sequential( + ConvBlock(256, [512, 512, 512], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + self.layer5 = nn.Sequential( + ConvBlock(512, [1024, 1024, 1024], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(1024, [1024, 1024, 1024], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + # head + self.layer6 = nn.Sequential( + ConvBlock(1024, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer7 = nn.Sequential( + ConvBlock(512, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer8 = nn.Sequential( + ConvBlock(256, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer9 = nn.Sequential( + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer10 = nn.Sequential( + ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + # conv2 + self.conv2 = nn.Sequential( + ConvBlock(32, [16, 16, self.output_dim], kernel_size=3, stride=1, + final_relu=False, batchnorm=self.batchnorm), + IdentityBlock(self.output_dim, [16, 16, self.output_dim], kernel_size=3, stride=1, + final_relu=False, batchnorm=self.batchnorm) + ) + + def forward(self, x): + x = self.preprocess(x, dist='transporter') + in_shape = x.shape + + # encoder + for layer in [self.conv1, self.layer1, self.layer2, self.layer3, self.layer4, self.layer5]: + x = layer(x) + + # decoder + im = [] + for layer in [self.layer6, self.layer7, self.layer8, self.layer9, self.layer10, self.conv2]: + im.append(x) + x = layer(x) + + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x, im \ No newline at end of file diff --git a/cliport/models/resnet_lat_reduce.py b/cliport/models/resnet_lat_reduce.py new file mode 100644 index 0000000000000000000000000000000000000000..8a64d7e0665ed471c71441deb0fdb393940c8f73 --- /dev/null +++ b/cliport/models/resnet_lat_reduce.py @@ -0,0 +1,119 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils + +from cliport.models.resnet import ConvBlock, IdentityBlock + +class ResNet45_Reduced_10s(nn.Module): + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super(ResNet45_Reduced_10s, self).__init__() + self.input_shape = input_shape + self.input_dim = input_shape[-1] + self.output_dim = output_dim + self.cfg = cfg + self.device = device + self.batchnorm = self.cfg['train']['batchnorm'] + self.preprocess = preprocess + # import IPython; IPython.embed() + + self._make_layers() + + def _make_layers(self): + # conv1 + self.conv1 = nn.Sequential( + nn.Conv2d(self.input_dim, 64, stride=1, kernel_size=3, padding=1), + nn.BatchNorm2d(64) if self.batchnorm else nn.Identity(), + nn.ReLU(True), + ) + + # fcn + self.layer1 = nn.Sequential( + ConvBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + self.layer2 = nn.Sequential( + ConvBlock(64, [128, 128, 128], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + self.layer3 = nn.Sequential( + ConvBlock(128, [256, 256, 256], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + self.layer4 = nn.Sequential( + ConvBlock(256, [512, 512, 512], kernel_size=3, stride=2, batchnorm=self.batchnorm), + IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + ) + + # self.layer5 = nn.Sequential( + # ConvBlock(512, [1024, 1024, 1024], kernel_size=3, stride=2, batchnorm=self.batchnorm), + # IdentityBlock(1024, [1024, 1024, 1024], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # ) + + # # head + # self.layer6 = nn.Sequential( + # ConvBlock(1024, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # nn.UpsamplingBilinear2d(scale_factor=2), + # ) + + self.layer7 = nn.Sequential( + ConvBlock(512, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer8 = nn.Sequential( + ConvBlock(256, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + # self.layer9 = nn.Sequential( + # ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # nn.UpsamplingBilinear2d(scale_factor=2), + # ) + + # self.layer10 = nn.Sequential( + # ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + # nn.UpsamplingBilinear2d(scale_factor=2), + # ) + + # conv2 + self.conv2 = nn.Sequential( + ConvBlock(128, [16, 16, self.output_dim], kernel_size=3, stride=1, + final_relu=False, batchnorm=self.batchnorm), + IdentityBlock(self.output_dim, [16, 16, self.output_dim], kernel_size=3, stride=1, + final_relu=False, batchnorm=self.batchnorm) + ) # change the input channel to the 128 + + def forward(self, x): + x = self.preprocess(x, dist='transporter') + in_shape = x.shape + + # # encoder + # for layer in [self.conv1, self.layer1, self.layer2, self.layer3, self.layer4, self.layer5]: + # x = layer(x) + + # # decoder + # im = [] + # for layer in [self.layer6, self.layer7, self.layer8, self.layer9, self.layer10, self.conv2]: + # im.append(x) + # x = layer(x) + # encoder + for layer in [self.conv1, self.layer1, self.layer2, self.layer3, self.layer4]: + x = layer(x) + # decoder + im = [] + for layer in [self.layer7, self.layer8, self.conv2]: + im.append(x) + x = layer(x) + + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x, im \ No newline at end of file diff --git a/cliport/models/rn50_bert_lingunet.py b/cliport/models/rn50_bert_lingunet.py new file mode 100644 index 0000000000000000000000000000000000000000..3b72b982b8351b48f856bc2ddbfdb846748cc0a1 --- /dev/null +++ b/cliport/models/rn50_bert_lingunet.py @@ -0,0 +1,79 @@ +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils +from cliport.models.resnet import IdentityBlock, ConvBlock +from cliport.models.core.unet import Up + +from cliport.models.rn50_bert_lingunet_lat import RN50BertLingUNetLat + + +class RN50BertLingUNet(RN50BertLingUNetLat): + """ ImageNet RN50 & Bert with U-Net skip connections but without lateral connections """ + + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super().__init__(input_shape, output_dim, cfg, device, preprocess) + + def _build_decoder(self): + self.conv1 = nn.Sequential( + nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False), + nn.ReLU(True) + ) + self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear) + self.up2 = Up(1024, 512 // self.up_factor, self.bilinear) + self.up3 = Up(512, 256 // self.up_factor, self.bilinear) + + self.layer1 = nn.Sequential( + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer2 = nn.Sequential( + ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer3 = nn.Sequential( + ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.conv2 = nn.Sequential( + nn.Conv2d(16, self.output_dim, kernel_size=1) + ) + + def forward(self, x, l): + x = self.preprocess(x, dist='clip') + + in_type = x.dtype + in_shape = x.shape + x = x[:,:3] # select RGB + x, im = self.encode_image(x) + x = x.to(in_type) + + # encode language + l_enc, l_emb, l_mask = self.encode_text(l) + l_input = l_emb if 'word' in self.lang_fusion_type else l_enc + l_input = l_input.to(dtype=x.dtype) + + # encode image + assert x.shape[1] == self.input_dim + x = self.conv1(x) + + x = self.lang_fuser1(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj1) + x = self.up1(x, im[-2]) + + x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2) + x = self.up2(x, im[-3]) + + x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3) + x = self.up3(x, im[-4]) + + for layer in [self.layer1, self.layer2, self.layer3, self.conv2]: + x = layer(x) + + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x \ No newline at end of file diff --git a/cliport/models/rn50_bert_lingunet_lat.py b/cliport/models/rn50_bert_lingunet_lat.py new file mode 100644 index 0000000000000000000000000000000000000000..df0c39d59ce60f33348eaffb264202cb731dff92 --- /dev/null +++ b/cliport/models/rn50_bert_lingunet_lat.py @@ -0,0 +1,159 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.models as models + +import cliport.utils.utils as utils +from transformers import DistilBertTokenizer, DistilBertModel +from cliport.models.resnet import IdentityBlock, ConvBlock +from cliport.models.core.unet import Up + +from cliport.models.core import fusion +from cliport.models.core.fusion import FusionConvLat + + +class RN50BertLingUNetLat(nn.Module): + """ ImageNet RN50 & Bert with U-Net skip connections """ + + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super(RN50BertLingUNetLat, self).__init__() + self.input_shape = input_shape + self.output_dim = output_dim + self.input_dim = 2048 + self.cfg = cfg + self.batchnorm = self.cfg['train']['batchnorm'] + self.lang_fusion_type = self.cfg['train']['lang_fusion_type'] + self.bilinear = True + self.up_factor = 2 if self.bilinear else 1 + self.device = device + self.preprocess = preprocess + + self._load_vision_fcn() + self._load_lang_enc() + self._build_decoder() + + def _load_vision_fcn(self): + resnet50 = models.resnet50(pretrained=True) + modules = list(resnet50.children())[:-2] + + self.stem = nn.Sequential(*modules[:4]) + self.layer1 = modules[4] + self.layer2 = modules[5] + self.layer3 = modules[6] + self.layer4 = modules[7] + + def _load_lang_enc(self): + self.tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') + self.text_encoder = DistilBertModel.from_pretrained('distilbert-base-uncased') + self.text_fc = nn.Linear(768, 1024) + + self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2) + self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4) + self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8) + + self.proj_input_dim = 512 if 'word' in self.lang_fusion_type else 1024 + self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024) + self.lang_proj2 = nn.Linear(self.proj_input_dim, 512) + self.lang_proj3 = nn.Linear(self.proj_input_dim, 256) + + def _build_decoder(self): + self.conv1 = nn.Sequential( + nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False), + nn.ReLU(True) + ) + self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear) + self.lat_fusion1 = FusionConvLat(input_dim=1024+512, output_dim=512) + + self.up2 = Up(1024, 512 // self.up_factor, self.bilinear) + self.lat_fusion2 = FusionConvLat(input_dim=512+256, output_dim=256) + + self.up3 = Up(512, 256 // self.up_factor, self.bilinear) + self.lat_fusion3 = FusionConvLat(input_dim=256+128, output_dim=128) + + self.layer1 = nn.Sequential( + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion4 = FusionConvLat(input_dim=128+64, output_dim=64) + + self.layer2 = nn.Sequential( + ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion5 = FusionConvLat(input_dim=64+32, output_dim=32) + + self.layer3 = nn.Sequential( + ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + self.lat_fusion6 = FusionConvLat(input_dim=32+16, output_dim=16) + + self.conv2 = nn.Sequential( + nn.Conv2d(16, self.output_dim, kernel_size=1) + ) + + def resnet50(self, x): + im = [] + for layer in [self.stem, self.layer1, self.layer2, self.layer3, self.layer4]: + x = layer(x) + im.append(x) + return x, im + + def encode_image(self, img): + with torch.no_grad(): + img_encoding, img_im = self.resnet50(img) + return img_encoding, img_im + + def encode_text(self, x): + with torch.no_grad(): + inputs = self.tokenizer(x, return_tensors='pt') + input_ids, attention_mask = inputs['input_ids'].to(self.device), inputs['attention_mask'].to(self.device) + text_embeddings = self.text_encoder(input_ids, attention_mask) + text_encodings = text_embeddings.last_hidden_state.mean(1) + text_feat = self.text_fc(text_encodings) + text_mask = torch.ones_like(input_ids) # [1, max_token_len] + return text_feat, text_embeddings.last_hidden_state, text_mask + + def forward(self, x, lat, l): + x = self.preprocess(x, dist='clip') + + in_type = x.dtype + in_shape = x.shape + x = x[:,:3] # select RGB + x, im = self.encode_image(x) + x = x.to(in_type) + + l_enc, l_emb, l_mask = self.encode_text(l) + l_input = l_emb if 'word' in self.lang_fusion_type else l_enc + l_input = l_input.to(dtype=x.dtype) + + assert x.shape[1] == self.input_dim + x = self.conv1(x) + + x = self.lang_fuser1(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj1) + x = self.up1(x, im[-2]) + x = self.lat_fusion1(x, lat[-6]) + + x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2) + x = self.up2(x, im[-3]) + x = self.lat_fusion2(x, lat[-5]) + + x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3) + x = self.up3(x, im[-4]) + x = self.lat_fusion3(x, lat[-4]) + + x = self.layer1(x) + x = self.lat_fusion4(x, lat[-3]) + + x = self.layer2(x) + x = self.lat_fusion5(x, lat[-2]) + + x = self.layer3(x) + x = self.lat_fusion6(x, lat[-1]) + + x = self.conv2(x) + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x \ No newline at end of file diff --git a/cliport/models/rn50_bert_unet.py b/cliport/models/rn50_bert_unet.py new file mode 100644 index 0000000000000000000000000000000000000000..2072f8a582875dac27b129ce29288acd18b45d9e --- /dev/null +++ b/cliport/models/rn50_bert_unet.py @@ -0,0 +1,69 @@ +import torch.nn as nn +import torch.nn.functional as F + +import cliport.utils.utils as utils +from cliport.models.resnet import IdentityBlock, ConvBlock +from cliport.models.core.unet import Up +from cliport.models.rn50_bert_lingunet import RN50BertLingUNet + + +class RN50BertUNet(RN50BertLingUNet): + """ ImageNet RN50 & Bert with U-Net skip connections without language""" + + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super().__init__(input_shape, output_dim, cfg, device, preprocess) + + def _build_decoder(self): + self.conv1 = nn.Sequential( + nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False), + nn.ReLU(True) + ) + + self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear) + + self.up2 = Up(1024, 512 // self.up_factor, self.bilinear) + + self.up3 = Up(512, 256 // self.up_factor, self.bilinear) + + self.layer1 = nn.Sequential( + ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer2 = nn.Sequential( + ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.layer3 = nn.Sequential( + ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), + nn.UpsamplingBilinear2d(scale_factor=2), + ) + + self.conv2 = nn.Sequential( + nn.Conv2d(16, self.output_dim, kernel_size=1) + ) + + def forward(self, x): + x = self.preprocess(x, dist='clip') + + in_type = x.dtype + in_shape = x.shape + x = x[:,:3] # select RGB + x, im = self.encode_image(x) + x = x.to(in_type) + + x = self.conv1(x) + + x = self.up1(x, im[-2]) + x = self.up2(x, im[-3]) + x = self.up3(x, im[-4]) + + for layer in [self.layer1, self.layer2, self.layer3, self.conv2]: + x = layer(x) + + x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') + return x \ No newline at end of file diff --git a/cliport/models/streams/one_stream_attention_lang_fusion.py b/cliport/models/streams/one_stream_attention_lang_fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..0e433a6d60d933ae259ee84ef2d0371bdfb3a20e --- /dev/null +++ b/cliport/models/streams/one_stream_attention_lang_fusion.py @@ -0,0 +1,23 @@ +"""Attention module.""" + +import cliport.models as models +from cliport.models.streams.two_stream_attention_lang_fusion import TwoStreamAttentionLangFusion + + +class OneStreamAttentionLangFusion(TwoStreamAttentionLangFusion): + """Attention (a.k.a Pick) module with language features fused at the bottleneck.""" + + def __init__(self, stream_fcn, in_shape, n_rotations, preprocess, cfg, device): + self.fusion_type = cfg['train']['attn_stream_fusion_type'] + super().__init__(stream_fcn, in_shape, n_rotations, preprocess, cfg, device) + + def _build_nets(self): + stream_one_fcn, _ = self.stream_fcn + stream_one_model = models.names[stream_one_fcn] + + self.attn_stream_one = stream_one_model(self.in_shape, 1, self.cfg, self.device, self.preprocess) + print(f"Attn FCN: {stream_one_fcn}") + + def attend(self, x, l): + x = self.attn_stream_one(x, l) + return x \ No newline at end of file diff --git a/cliport/models/streams/one_stream_transport_lang_fusion.py b/cliport/models/streams/one_stream_transport_lang_fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..91fb81297ba83710db36fe41635c3b7ac05312aa --- /dev/null +++ b/cliport/models/streams/one_stream_transport_lang_fusion.py @@ -0,0 +1,24 @@ +import cliport.models as models +from cliport.models.streams.two_stream_transport_lang_fusion import TwoStreamTransportLangFusion + + +class OneStreamTransportLangFusion(TwoStreamTransportLangFusion): + """Transport (a.k.a) Place module with language features fused at the bottleneck""" + + def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device): + self.fusion_type = cfg['train']['trans_stream_fusion_type'] + super().__init__(stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device) + + def _build_nets(self): + stream_one_fcn, _ = self.stream_fcn + stream_one_model = models.names[stream_one_fcn] + + self.key_stream_one = stream_one_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess) + self.query_stream_one = stream_one_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess) + + print(f"Transport FCN: {stream_one_fcn}") + + def transport(self, in_tensor, crop, l): + logits = self.key_stream_one(in_tensor, l) + kernel = self.query_stream_one(crop, l) + return logits, kernel diff --git a/cliport/models/streams/two_stream_attention.py b/cliport/models/streams/two_stream_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..4aa4ccb0ee15e6d1a7f43d1b364d7a8e5ec7a525 --- /dev/null +++ b/cliport/models/streams/two_stream_attention.py @@ -0,0 +1,40 @@ +import cliport.models as models +import cliport.models.core.fusion as fusion +from cliport.models.core.attention import Attention + + +class TwoStreamAttention(Attention): + """Two Stream Attention (a.k.a Pick) module""" + + def __init__(self, stream_fcn, in_shape, n_rotations, preprocess, cfg, device): + self.fusion_type = cfg['train']['attn_stream_fusion_type'] + super().__init__(stream_fcn, in_shape, n_rotations, preprocess, cfg, device) + + def _build_nets(self): + stream_one_fcn, stream_two_fcn = self.stream_fcn + stream_one_model = models.names[stream_one_fcn] + stream_two_model = models.names[stream_two_fcn] + + self.attn_stream_one = stream_one_model(self.in_shape, 1, self.cfg, self.device, self.preprocess) + self.attn_stream_two = stream_two_model(self.in_shape, 1, self.cfg, self.device, self.preprocess) + self.fusion = fusion.names[self.fusion_type](input_dim=1) + print(f"Attn FCN - Stream One: {stream_one_fcn}, Stream Two: {stream_two_fcn}, Stream Fusion: {self.fusion_type}") + + def attend(self, x): + x1 = self.attn_stream_one(x) + x2 = self.attn_stream_two(x) + x = self.fusion(x1, x2) + return x + + +class TwoStreamAttentionLat(TwoStreamAttention): + """Two Stream Attention (a.k.a Pick) module with lateral connections""" + + def __init__(self, stream_fcn, in_shape, n_rotations, preprocess, cfg, device): + super().__init__(stream_fcn, in_shape, n_rotations, preprocess, cfg, device) + + def attend(self, x): + x1, lat = self.attn_stream_one(x) + x2 = self.attn_stream_two(x, lat) + x = self.fusion(x1, x2) + return x \ No newline at end of file diff --git a/cliport/models/streams/two_stream_attention_lang_fusion.py b/cliport/models/streams/two_stream_attention_lang_fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..bb18915a965f55357751d59bb8d5a8ca487bebce --- /dev/null +++ b/cliport/models/streams/two_stream_attention_lang_fusion.py @@ -0,0 +1,115 @@ +import numpy as np +import torch +import torch.nn.functional as F + +from cliport.models.core.attention import Attention +import cliport.models as models +import cliport.models.core.fusion as fusion + + +class TwoStreamAttentionLangFusion(Attention): + """Two Stream Language-Conditioned Attention (a.k.a Pick) module.""" + + def __init__(self, stream_fcn, in_shape, n_rotations, preprocess, cfg, device): + self.fusion_type = cfg['train']['attn_stream_fusion_type'] + super().__init__(stream_fcn, in_shape, n_rotations, preprocess, cfg, device) + + def _build_nets(self): + stream_one_fcn, stream_two_fcn = self.stream_fcn + stream_one_model = models.names[stream_one_fcn] # resnet_lat.REsNet45_10s + stream_two_model = models.names[stream_two_fcn] # clip_ligunet_lat.CLIP_LIGUnet_lat + + self.attn_stream_one = stream_one_model(self.in_shape, 1, self.cfg, self.device, self.preprocess) + self.attn_stream_two = stream_two_model(self.in_shape, 1, self.cfg, self.device, self.preprocess) + self.fusion = fusion.names[self.fusion_type](input_dim=1) + + print(f"Attn FCN - Stream One: {stream_one_fcn}, Stream Two: {stream_two_fcn}, Stream Fusion: {self.fusion_type}") + + def attend(self, x, l): + x1 = self.attn_stream_one(x) + x2 = self.attn_stream_two(x, l) + x = self.fusion(x1, x2) + return x + + def forward(self, inp_img, lang_goal, softmax=True): + """Forward pass.""" + if len(inp_img.shape) < 4: + inp_img = inp_img[None] + + if type(inp_img) is not torch.Tensor: + in_data = inp_img # .reshape(in_shape) + in_tens = torch.from_numpy(in_data.copy()).to(dtype=torch.float, device=self.device) # [B W H 6] + else: + in_data = inp_img + in_tens = in_data + + # [B W H 6] + in_tens = torch.nn.functional.pad(in_tens, tuple(self.padding[[2,1,0]].reshape(-1)), mode='constant') + + # Rotation pivot. + pv = np.array(in_tens.shape[1:3]) // 2 + + # Rotate input. + in_tens = in_tens.permute(0, 3, 1, 2) # [B 6 W H] + + # in_tens = in_tens.repeat(self.n_rotations, 1, 1, 1) + # make n copies, but keep batchsize + in_tens = [in_tens] * self.n_rotations + in_tens = self.rotator(in_tens, pivot=pv) + + # Forward pass. + logits = self.attend(torch.cat(in_tens, dim=0), lang_goal) + + # Rotate back output. + logits = self.rotator([logits], reverse=True, pivot=pv) + logits = torch.cat(logits, dim=0) + c0 = self.padding[:2, 0] + c1 = c0 + inp_img[0].shape[:2] + logits = logits[:, :, c0[0]:c1[0], c0[1]:c1[1]] + output_shape = logits.shape + + # logits = logits.permute(1, 2, 3, 0) # [B W H 1] + output = logits.reshape(len(logits), -1) + if softmax: + output = F.softmax(output, dim=-1) + return output.view(output_shape) + + +class TwoStreamAttentionLangFusionLat(TwoStreamAttentionLangFusion): + """Language-Conditioned Attention (a.k.a Pick) module with lateral connections.""" + + def __init__(self, stream_fcn, in_shape, n_rotations, preprocess, cfg, device): + self.fusion_type = cfg['train']['attn_stream_fusion_type'] + super().__init__(stream_fcn, in_shape, n_rotations, preprocess, cfg, device) + + def attend(self, x, l): + x1, lat = self.attn_stream_one(x) + x2 = self.attn_stream_two(x, lat, l) + x = self.fusion(x1, x2) + return x + + + +class TwoStreamAttentionLangFusionLatReduce(TwoStreamAttentionLangFusion): + """Language-Conditioned Attention (a.k.a Pick) module with lateral connections.""" + + def __init__(self, stream_fcn, in_shape, n_rotations, preprocess, cfg, device): + self.fusion_type = cfg['train']['attn_stream_fusion_type'] + super().__init__(stream_fcn, in_shape, n_rotations, preprocess, cfg, device) + + del self.attn_stream_one + del self.attn_stream_two + + stream_one_fcn = 'plain_resnet_reduce_lat' + stream_one_model = models.names[stream_one_fcn] + stream_two_fcn = 'clip_ling' + stream_two_model = models.names[stream_two_fcn] + + self.attn_stream_one = stream_one_model(self.in_shape, 1, self.cfg, self.device, self.preprocess) + self.attn_stream_two = stream_two_model(self.in_shape, 1, self.cfg, self.device, self.preprocess) + + def attend(self, x, l): + x1, lat = self.attn_stream_one(x) + x2 = self.attn_stream_two(x, lat, l) + x = self.fusion(x1, x2) + return x \ No newline at end of file diff --git a/cliport/models/streams/two_stream_transport.py b/cliport/models/streams/two_stream_transport.py new file mode 100644 index 0000000000000000000000000000000000000000..54e44241c1a00ee8d024bf0b1e78357aa3d4d6ec --- /dev/null +++ b/cliport/models/streams/two_stream_transport.py @@ -0,0 +1,49 @@ +import cliport.models as models +import cliport.models.core.fusion as fusion +from cliport.models.core.transport import Transport + + +class TwoStreamTransport(Transport): + """Two Stream Transport (a.k.a Place) module""" + + def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device): + self.fusion_type = cfg['train']['trans_stream_fusion_type'] + super().__init__(stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device) + + def _build_nets(self): + stream_one_fcn, stream_two_fcn = self.stream_fcn + stream_one_model = models.names[stream_one_fcn] + stream_two_model = models.names[stream_two_fcn] + + self.key_stream_one = stream_one_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess) + self.key_stream_two = stream_two_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess) + self.query_stream_one = stream_one_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess) + self.query_stream_two = stream_two_model(self.in_shape, self.kernel_dim, self.cfg, self.device, self.preprocess) + + self.fusion_key = fusion.names[self.fusion_type](input_dim=self.kernel_dim) + self.fusion_query = fusion.names[self.fusion_type](input_dim=self.kernel_dim) + + print(f"Transport FCN - Stream One: {stream_one_fcn}, Stream Two: {stream_two_fcn}, Stream Fusion: {self.fusion_type}") + + def transport(self, in_tensor, crop): + logits = self.fusion_key(self.key_stream_one(in_tensor), self.key_stream_two(in_tensor)) + kernel = self.fusion_query(self.query_stream_one(crop), self.query_stream_two(crop)) + return logits, kernel + + +class TwoStreamTransportLat(TwoStreamTransport): + """Two Stream Transport (a.k.a Place) module with lateral connections""" + + def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device): + super().__init__(stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device) + + def transport(self, in_tensor, crop): + key_out_one, key_lat_one = self.key_stream_one(in_tensor) + key_out_two = self.key_stream_two(in_tensor, key_lat_one) + logits = self.fusion_key(key_out_one, key_out_two) + + query_out_one, query_lat_one = self.query_stream_one(crop) + query_out_two = self.query_stream_two(crop, query_lat_one) + kernel = self.fusion_query(query_out_one, query_out_two) + + return logits, kernel \ No newline at end of file diff --git a/cliport/models/streams/two_stream_transport_lang_fusion.py b/cliport/models/streams/two_stream_transport_lang_fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..b20a28c446071ed50dad3ce7977ae6c9b459fec3 --- /dev/null +++ b/cliport/models/streams/two_stream_transport_lang_fusion.py @@ -0,0 +1,196 @@ +import torch +import numpy as np + +import cliport.models as models +import cliport.models.core.fusion as fusion +from cliport.models.core.transport import Transport + + +class TwoStreamTransportLangFusion(Transport): + """Two Stream Transport (a.k.a Place) module""" + + def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device): + self.fusion_type = cfg['train']['trans_stream_fusion_type'] + super().__init__(stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device) + + def _build_nets(self): + stream_one_fcn, stream_two_fcn = self.stream_fcn + stream_one_model = models.names[stream_one_fcn] + stream_two_model = models.names[stream_two_fcn] + + self.key_stream_one = stream_one_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess) + self.key_stream_two = stream_two_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess) + self.query_stream_one = stream_one_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess) + self.query_stream_two = stream_two_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess) + self.fusion_key = fusion.names[self.fusion_type](input_dim=self.kernel_dim) + self.fusion_query = fusion.names[self.fusion_type](input_dim=self.kernel_dim) + + print(f"Transport FCN - Stream One: {stream_one_fcn}, Stream Two: {stream_two_fcn}, Stream Fusion: {self.fusion_type}") + + def transport2(self, in_tensor, crop, l): + logits = self.fusion_key(self.key_stream_one(in_tensor), self.key_stream_two(in_tensor, l)) + kernel = self.fusion_query(self.query_stream_one(crop), self.query_stream_two(crop, l)) + return logits, kernel + + def forward(self, inp_img, p, lang_goal, softmax=True): + """Forward pass.""" + if len(inp_img.shape) < 4: + inp_img = inp_img[None] + + if type(inp_img) is not torch.Tensor: + in_data = inp_img # .reshape(in_shape) + in_tens = torch.from_numpy(in_data).to(dtype=torch.float, device=self.device) # [B W H 6] + else: + in_data = inp_img + in_tens = in_data + + in_tensor = torch.nn.functional.pad(in_tens, tuple(self.padding[[2,1,0]].reshape(-1)), mode='constant') + if type(p[0]) is not torch.Tensor: + p = torch.FloatTensor(p)[None] + + in_tensors = [] + crops = [] + + # this for loop is fast. + for i in range(len(in_tensor)): + in_tensor_i = in_tensor[[i]] + # Rotation pivot. + pv = p[i] + self.pad_size + + # Crop before network (default for Transporters CoRL 2020). + hcrop = self.pad_size + in_tensor_i = in_tensor_i.permute(0, 3, 1, 2) + + crop = [in_tensor_i] * self.n_rotations + crop = self.rotator(crop, pivot=pv.float()) + crop = torch.cat(crop, dim=0) + crop = crop[:, :, int(pv[0]-hcrop):int(pv[0]+hcrop), int(pv[1]-hcrop):int(pv[1]+hcrop)] + + in_tensors.append(in_tensor_i) + crops.append(crop) + + logits, kernels = self.transport(torch.cat(in_tensors,dim=0), torch.cat(crops, dim=0), lang_goal) #crops.shape:(8, 36, 6, 64, 64) + res = self.correlate(logits, kernels, softmax) + return res + +class TwoStreamTransportLangFusionLat(TwoStreamTransportLangFusion): + """Two Stream Transport (a.k.a Place) module with lateral connections""" + + def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device): + + self.fusion_type = cfg['train']['trans_stream_fusion_type'] + super().__init__(stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device) + + def transport(self, in_tensor, crop, l): + key_out_one, key_lat_one = self.key_stream_one(in_tensor) + key_out_two = self.key_stream_two(in_tensor, key_lat_one, l) + logits = self.fusion_key(key_out_one, key_out_two) + + query_out_one, query_lat_one = self.query_stream_one(crop) + query_out_two = self.query_stream_two(crop, query_lat_one, l) + kernel = self.fusion_query(query_out_one, query_out_two) + + return logits, kernel + + +class TwoStreamTransportLangFusionLatReduce(TwoStreamTransportLangFusionLat): + """Two Stream Transport (a.k.a Place) module with lateral connections""" + + def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device): + + self.fusion_type = cfg['train']['trans_stream_fusion_type'] + super().__init__(stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device) + + del self.query_stream_one + del self.query_stream_two + # del self.key_stream_one + # del self.key_stream_two + + stream_one_fcn = 'plain_resnet_reduce_lat' + stream_one_model = models.names[stream_one_fcn] + stream_two_fcn = 'clip_ling' + stream_two_model = models.names[stream_two_fcn] + + + + # self.key_stream_one = stream_one_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess) + # self.key_stream_two = stream_two_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess) + + self.query_stream_one = stream_one_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess) + self.query_stream_two = stream_two_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess) + + def transport(self, in_tensor, crop, l): + key_out_one, key_lat_one = self.key_stream_one(in_tensor) + key_out_two = self.key_stream_two(in_tensor, key_lat_one, l) + logits = self.fusion_key(key_out_one, key_out_two) + + query_out_one, query_lat_one = self.query_stream_one(crop) + query_out_two = self.query_stream_two(crop, query_lat_one, l) + kernel = self.fusion_query(query_out_one, query_out_two) + + return logits, kernel + + + + + +class TwoStreamTransportLangFusionLatReduceOneStream(TwoStreamTransportLangFusionLatReduce): + """Two Stream Transport (a.k.a Place) module with lateral connections""" + + def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device): + + self.fusion_type = cfg['train']['trans_stream_fusion_type'] + super().__init__(stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device) + + del self.query_stream_one + del self.query_stream_two + + + + def transport(self, in_tensor, crop, l): + key_out_one, key_lat_one = self.key_stream_one(in_tensor) + key_out_two = self.key_stream_two(in_tensor, key_lat_one, l) + logits = self.fusion_key(key_out_one, key_out_two) + + query_out_one, query_lat_one = self.key_stream_one(crop) + query_out_two = self.key_stream_two(crop, query_lat_one, l) + kernel = self.fusion_query(query_out_one, query_out_two) + + return logits, kernel + + + + +class TwoStreamTransportLangFusionLatPretrained18(TwoStreamTransportLangFusionLat): + """Two Stream Transport (a.k.a Place) module with lateral connections""" + + def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device): + + self.fusion_type = cfg['train']['trans_stream_fusion_type'] + super().__init__(stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device) + + del self.query_stream_one + del self.query_stream_two + # del self.key_stream_one + # del self.key_stream_two + stream_one_fcn = 'pretrained_resnet18' + stream_one_model = models.names[stream_one_fcn] + stream_two_fcn = 'clip_ling' + stream_two_model = models.names[stream_two_fcn] + + # self.key_stream_one = stream_one_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess) + # self.key_stream_two = stream_two_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess) + + self.query_stream_one = stream_one_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess) + self.query_stream_two = stream_two_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess) + + def transport(self, in_tensor, crop, l): + key_out_one, key_lat_one = self.key_stream_one(in_tensor) + key_out_two = self.key_stream_two(in_tensor, key_lat_one, l) + logits = self.fusion_key(key_out_one, key_out_two) + + query_out_one, query_lat_one = self.query_stream_one(crop) + query_out_two = self.query_stream_two(crop, query_lat_one, l) + kernel = self.fusion_query(query_out_one, query_out_two) + + return logits, kernel \ No newline at end of file diff --git a/cliport/models/untrained_rn50_bert_lingunet.py b/cliport/models/untrained_rn50_bert_lingunet.py new file mode 100644 index 0000000000000000000000000000000000000000..69bf583e8ea9d2bf505ce3387131ff4c8ff617eb --- /dev/null +++ b/cliport/models/untrained_rn50_bert_lingunet.py @@ -0,0 +1,39 @@ +import torch.nn as nn +import torchvision.models as models + +from transformers import DistilBertTokenizer, DistilBertModel, DistilBertConfig +from cliport.models.core import fusion +from cliport.models.rn50_bert_lingunet import RN50BertLingUNet + + +class UntrainedRN50BertLingUNet(RN50BertLingUNet): + """ Untrained ImageNet RN50 & Bert with U-Net skip connections """ + + def __init__(self, input_shape, output_dim, cfg, device, preprocess): + super().__init__(input_shape, output_dim, cfg, device, preprocess) + + def _load_vision_fcn(self): + resnet50 = models.resnet50(pretrained=False) + modules = list(resnet50.children())[:-2] + + self.stem = nn.Sequential(*modules[:4]) + self.layer1 = modules[4] + self.layer2 = modules[5] + self.layer3 = modules[6] + self.layer4 = modules[7] + + def _load_lang_enc(self): + self.tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') # only Tokenizer is pre-trained + distilbert_config = DistilBertConfig() + self.text_encoder = DistilBertModel(distilbert_config) + + self.text_fc = nn.Linear(768, 1024) + + self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2) + self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4) + self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8) + + self.proj_input_dim = 512 if 'word' in self.lang_fusion_type else 1024 + self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024) + self.lang_proj2 = nn.Linear(self.proj_input_dim, 512) + self.lang_proj3 = nn.Linear(self.proj_input_dim, 256) diff --git a/cliport/tasks/README.md b/cliport/tasks/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3aefc423c9166783f309221b0cb1480bab8a61d5 --- /dev/null +++ b/cliport/tasks/README.md @@ -0,0 +1,67 @@ +# Tasks + +### Descriptions + +This folder contains a total of 10 goal-conditioned (language or image) and 8 demo-conditioned (original TransporterNets) tasks. 8 out of the 10 goal-conditioned tasks contain two splits: **seen** and **unseen**. The **full** version is a union of both **seen** and **unseen** attributes made specifically for multi-attr training. Sequential tasks that involve following instructions in a specific order are indicated by **seq** in their names. + +See [__init__.py](__init__.py) for the full list of demo-conditioned and goal-conditioned (language or image) tasks: + +```python +# demo conditioned +'align-box-corner': AlignBoxCorner, +'assembling-kits': AssemblingKits, +'assembling-kits-easy': AssemblingKitsEasy, +'block-insertion': BlockInsertion, +'block-insertion-easy': BlockInsertionEasy, +'block-insertion-nofixture': BlockInsertionNoFixture, +'block-insertion-sixdof': BlockInsertionSixDof, +'block-insertion-translation': BlockInsertionTranslation, +'manipulating-rope': ManipulatingRope, +'packing-boxes': PackingBoxes, +'palletizing-boxes': PalletizingBoxes, +'place-red-in-green': PlaceRedInGreen, +'stack-block-pyramid': StackBlockPyramid, +'sweeping-piles': SweepingPiles, +'towers-of-hanoi': TowersOfHanoi, + +# goal conditioned +'align-rope': AlignRope, +'assembling-kits-seq-seen-colors': AssemblingKitsSeqSeenColors, +'assembling-kits-seq-unseen-colors': AssemblingKitsSeqUnseenColors, +'assembling-kits-seq-full': AssemblingKitsSeqFull, +'packing-shapes': PackingShapes, +'packing-boxes-pairs-seen-colors': PackingBoxesPairsSeenColors, +'packing-boxes-pairs-unseen-colors': PackingBoxesPairsUnseenColors, +'packing-boxes-pairs-full': PackingBoxesPairsFull, +'packing-seen-google-objects-seq': PackingSeenGoogleObjectsSeq, +'packing-unseen-google-objects-seq': PackingUnseenGoogleObjectsSeq, +'packing-seen-google-objects-group': PackingSeenGoogleObjectsGroup, +'packing-unseen-google-objects-group': PackingUnseenGoogleObjectsGroup, +'put-block-in-bowl-seen-colors': PutBlockInBowlSeenColors, +'put-block-in-bowl-unseen-colors': PutBlockInBowlUnseenColors, +'put-block-in-bowl-full': PutBlockInBowlFull, +'stack-block-pyramid-seq-seen-colors': StackBlockPyramidSeqSeenColors, +'stack-block-pyramid-seq-unseen-colors': StackBlockPyramidSeqUnseenColors, +'stack-block-pyramid-seq-full': StackBlockPyramidSeqFull, +'separating-piles-seen-colors': SeparatingPilesSeenColors, +'separating-piles-unseen-colors': SeparatingPilesUnseenColors, +'separating-piles-full': SeparatingPilesFull, +'towers-of-hanoi-seq-seen-colors': TowersOfHanoiSeqSeenColors, +'towers-of-hanoi-seq-unseen-colors': TowersOfHanoiSeqUnseenColors, +'towers-of-hanoi-seq-full': TowersOfHanoiSeqFull, +``` + +### Generated Tasks by GPT +1. All of them should be automatically imported and exists in `generated_tasks` + +### Adding New Tasks + +See [put_block_in_bowl.py](put_block_in_bowl.py) for an example on how a task is specified. Creating a new task involves: (1) setting up a scene with the desired objects, (2) specifying goals with a language instruction and target "zones" or "poses", (3) defining an evaluation metric that is either sequential or non-sequential. See the original [Ravens codebase](https://github.com/google-research/ravens) for more details on task specification and organization. + +### Correcting COM for Google Scanned Objects + +By default all [Google Scanned Objects](https://app.ignitionrobotics.org/GoogleResearch/fuel/collections/Google%20Scanned%20Objects) have COMs (Center of Mass) at the base of the object, which leads to weird behavior with the physics engine. To correct this, I manually edited the COM of each `.obj` file to be the geometric center of the mesh with [Blender](https://www.blender.org/). See this [guide on editing COMs](https://blender.stackexchange.com/questions/14294/how-to-recenter-an-objects-origin) for reference. After correction, the original `.obj` can be overwritten using Blender's Export option. + +## Credit + +All demo-conditioned are from [Ravens](https://github.com/google-research/ravens). The language-conditioned tasks were built-off the same PyBullet environments. \ No newline at end of file diff --git a/cliport/tasks/__init__.py b/cliport/tasks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bffa0271b0451ca134f7de08953bf68a6d91f831 --- /dev/null +++ b/cliport/tasks/__init__.py @@ -0,0 +1,80 @@ +"""Ravens tasks.""" + +from cliport.tasks.align_box_corner import AlignBoxCorner +from cliport.tasks.assembling_kits import AssemblingKits +from cliport.tasks.assembling_kits_seq import AssemblingKitsSeq +from cliport.tasks.block_insertion import BlockInsertion +from cliport.tasks.manipulating_rope import ManipulatingRope +from cliport.tasks.align_rope import AlignRope +from cliport.tasks.packing_boxes import PackingBoxes +from cliport.tasks.packing_shapes import PackingShapes +from cliport.tasks.packing_boxes_pairs import PackingBoxesPairs +from cliport.tasks.packing_google_objects import PackingSeenGoogleObjectsSeq +from cliport.tasks.palletizing_boxes import PalletizingBoxes +from cliport.tasks.place_red_in_green import PlaceRedInGreen +from cliport.tasks.put_block_in_bowl import PutBlockInBowl +from cliport.tasks.stack_block_pyramid import StackBlockPyramid +from cliport.tasks.stack_block_pyramid_seq import StackBlockPyramidSeq +from cliport.tasks.sweeping_piles import SweepingPiles +from cliport.tasks.separating_piles import SeparatingPiles +from cliport.tasks.task import Task +from cliport.tasks.towers_of_hanoi import TowersOfHanoi +from cliport.tasks.towers_of_hanoi_seq import TowersOfHanoiSeq +from cliport.tasks.generated_task import GeneratedTask +from cliport.tasks.extended_tasks import * + +names = { + # demo conditioned + 'align-box-corner': AlignBoxCorner, + 'assembling-kits': AssemblingKits, + 'assembling-kits-easy': AssemblingKitsEasy, + 'block-insertion': BlockInsertion, + 'block-insertion-easy': BlockInsertionEasy, + 'block-insertion-nofixture': BlockInsertionNoFixture, + 'block-insertion-sixdof': BlockInsertionSixDof, + 'block-insertion-translation': BlockInsertionTranslation, + 'manipulating-rope': ManipulatingRope, + 'packing-boxes': PackingBoxes, + 'palletizing-boxes': PalletizingBoxes, + 'place-red-in-green': PlaceRedInGreen, + 'stack-block-pyramid': StackBlockPyramid, + 'sweeping-piles': SweepingPiles, + 'towers-of-hanoi': TowersOfHanoi, + 'gen-task': GeneratedTask, + + # goal conditioned + 'align-rope': AlignRope, + 'assembling-kits-seq': AssemblingKitsSeq, + 'assembling-kits-seq-seen-colors': AssemblingKitsSeqSeenColors, + 'assembling-kits-seq-unseen-colors': AssemblingKitsSeqUnseenColors, + 'assembling-kits-seq-full': AssemblingKitsSeqFull, + 'packing-shapes': PackingShapes, + 'packing-boxes-pairs': PackingBoxesPairsSeenColors, + 'packing-boxes-pairs-seen-colors': PackingBoxesPairsSeenColors, + 'packing-boxes-pairs-unseen-colors': PackingBoxesPairsUnseenColors, + 'packing-boxes-pairs-full': PackingBoxesPairsFull, + 'packing-seen-google-objects-seq': PackingSeenGoogleObjectsSeq, + 'packing-unseen-google-objects-seq': PackingUnseenGoogleObjectsSeq, + 'packing-seen-google-objects-group': PackingSeenGoogleObjectsGroup, + 'packing-unseen-google-objects-group': PackingUnseenGoogleObjectsGroup, + 'put-block-in-bowl': PutBlockInBowlSeenColors, + 'put-block-in-bowl-seen-colors': PutBlockInBowlSeenColors, + 'put-block-in-bowl-unseen-colors': PutBlockInBowlUnseenColors, + 'put-block-in-bowl-full': PutBlockInBowlFull, + 'stack-block-pyramid-seq': StackBlockPyramidSeqSeenColors, + 'stack-block-pyramid-seq-seen-colors': StackBlockPyramidSeqSeenColors, + 'stack-block-pyramid-seq-unseen-colors': StackBlockPyramidSeqUnseenColors, + 'stack-block-pyramid-seq-full': StackBlockPyramidSeqFull, + 'separating-piles': SeparatingPilesSeenColors, + 'separating-piles-seen-colors': SeparatingPilesSeenColors, + 'separating-piles-unseen-colors': SeparatingPilesUnseenColors, + 'separating-piles-full': SeparatingPilesFull, + 'towers-of-hanoi-seq': TowersOfHanoiSeqSeenColors, + 'towers-of-hanoi-seq-seen-colors': TowersOfHanoiSeqSeenColors, + 'towers-of-hanoi-seq-unseen-colors': TowersOfHanoiSeqUnseenColors, + 'towers-of-hanoi-seq-full': TowersOfHanoiSeqFull, +} + + +from cliport.generated_tasks import new_names +names.update(new_names) \ No newline at end of file diff --git a/cliport/tasks/align_box_corner.py b/cliport/tasks/align_box_corner.py new file mode 100644 index 0000000000000000000000000000000000000000..6890e1cd755013e83beff8c1265367da4cd3cda8 --- /dev/null +++ b/cliport/tasks/align_box_corner.py @@ -0,0 +1,59 @@ +import os +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class AlignBoxCorner(Task): + """Pick up the randomly sized box and align one of its corners to the L-shaped marker on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 3 + self.lang_template = "align the brown box with the green corner" + self.task_completed_desc = "done with alignment" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Generate randomly shaped box. + box_size = self.get_random_size(0.05, 0.15, 0.05, 0.15, 0.01, 0.06) + + # Add corner. + dimx = (box_size[0] / 2 - 0.025 + 0.0025, box_size[0] / 2 + 0.0025) + dimy = (box_size[1] / 2 + 0.0025, box_size[1] / 2 - 0.025 + 0.0025) + corner_template = 'corner/corner-template.urdf' + replace = {'DIMX': dimx, 'DIMY': dimy} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + corner_urdf = self.fill_template(corner_template, replace) + corner_size = (box_size[0], box_size[1], 0) + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, 'fixed') + + # Add possible placing poses. + theta = utils.quatXYZW_to_eulerXYZ(corner_pose[1])[2] + fip_rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta + np.pi)) + pose1 = (corner_pose[0], fip_rot) + alt_x = (box_size[0] / 2) - (box_size[1] / 2) + alt_y = (box_size[1] / 2) - (box_size[0] / 2) + alt_pos = (alt_x, alt_y, 0) + alt_rot0 = utils.eulerXYZ_to_quatXYZW((0, 0, np.pi / 2)) + alt_rot1 = utils.eulerXYZ_to_quatXYZW((0, 0, 3 * np.pi / 2)) + pose2 = utils.multiply(corner_pose, (alt_pos, alt_rot0)) + pose3 = utils.multiply(corner_pose, (alt_pos, alt_rot1)) + + # Add box. + box_template = 'box/box-template.urdf' + + # IMPORTANT: REPLACE THE TEMPLATE URDF + box_urdf = self.fill_template(box_template, {'DIM': np.float32(box_size)}) + box_pose = self.get_random_pose(env, box_size) + box_id = env.add_object(box_urdf, box_pose) + self.color_random_brown(box_id) + + # Goal: box is aligned with corner (1 of 4 possible poses). + self.add_goal(objs=[box_id], matches=np.int32([[1, 1, 1, 1]]), targ_poses=[corner_pose, pose1, pose2, pose3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, symmetries=[2 * np.pi], + language_goal=self.lang_template) diff --git a/cliport/tasks/align_rope.py b/cliport/tasks/align_rope.py new file mode 100644 index 0000000000000000000000000000000000000000..677247c76828f6d836747b252bc14f306eabfacd --- /dev/null +++ b/cliport/tasks/align_rope.py @@ -0,0 +1,70 @@ +import os + +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.task import Task +from cliport.utils import utils + +import random +import pybullet as p + + +class AlignRope(Task): + """Manipulate a deformable rope to connect its end-points between two + corners of a 3-sided square.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "align the rope from {direction}" + self.task_completed_desc = "done aligning the rope." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + n_parts = 20 + radius = 0.005 + length = 2 * radius * n_parts * np.sqrt(2) + + # Add 3-sided square. + square_size = (length, length, 0) + square_pose = self.get_random_pose(env, square_size) + square_template = 'square/square-template.urdf' + replace = {'DIM': (length,), 'HALF': (np.float32(length) / 2 - 0.005,)} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(square_template, replace) + env.add_object(urdf, square_pose, 'fixed') + + # Get four corner points of square. + corner0 = ( length / 2, length / 2, 0.001) + corner1 = (-length / 2, length / 2, 0.001) + corner2 = ( length / 2, -length / 2, 0.001) + corner3 = (-length / 2, -length / 2, 0.001) + + corner0 = utils.apply(square_pose, corner0) + corner1 = utils.apply(square_pose, corner1) + corner2 = utils.apply(square_pose, corner2) + corner3 = utils.apply(square_pose, corner3) + + # Four possible alignment tasks. + task_descs = [ + ((corner0, corner1), "front left tip to front right tip"), + ((corner0, corner2), "front right tip to back right corner"), + ((corner1, corner3), "front left tip to back left corner"), + ((corner3, corner2), "back right corner to back left corner") + ] + chosen_task = np.random.choice(len(task_descs), 1)[0] + (corner_0, corner_1), direction = task_descs[chosen_task] + + # IMPORTANT: use `make_ropes` to add cable (series of articulated small blocks). + objects, targets, matches = self.make_ropes(env, corners=(corner_0, corner_1)) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1., + language_goal=[self.lang_template.format(direction=direction)] * len(objects)) + + # wait for the scene to settle down + for i in range(480): + p.stepSimulation() \ No newline at end of file diff --git a/cliport/tasks/assembling_kits.py b/cliport/tasks/assembling_kits.py new file mode 100644 index 0000000000000000000000000000000000000000..1f33979885c09627c63dc78aa9f22efaa85d849f --- /dev/null +++ b/cliport/tasks/assembling_kits.py @@ -0,0 +1,62 @@ +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class AssemblingKits(Task): + """pick up different objects and arrange them on a board marked with corresponding silhouettes.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.homogeneous = False + + self.lang_template = "put all the blocks inside the holes they fit in" + self.task_completed_desc = "done assembling blocks." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add kit. + kit_size = (0.28, 0.2, 0.005) + kit_urdf = 'kitting/kit.urdf' + kit_pose = self.get_random_pose(env, kit_size) + env.add_object(kit_urdf, kit_pose, 'fixed') + + n_objects = 5 + obj_shapes = self.get_kitting_shapes(n_objects) + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['red'] + ] + + # Build kit. + targets = [] + targ_pos = [[-0.09, 0.045, 0.0014], [0, 0.045, 0.0014], + [0.09, 0.045, 0.0014], [-0.045, -0.045, 0.0014], + [0.045, -0.045, 0.0014]] + template = 'kitting/object-template.urdf' + + for i in range(n_objects): + shape = os.path.join(self.assets_root, 'kitting', + f'{obj_shapes[i]:02d}.obj') + scale = [0.003, 0.003, 0.0001] # .0005 + pos = utils.apply(kit_pose, targ_pos[i]) + theta = np.random.rand() * 2 * np.pi + rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': [0.2, 0.2, 0.2]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + env.add_object(urdf, (pos, rot), 'fixed') + targets.append((pos, rot)) + + # Add objects. + objects, matches = self.make_kitting_objects(env, targets=targets, obj_shapes=obj_shapes, n_objects=n_objects, colors=colors) + matches = np.int32(matches) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + \ No newline at end of file diff --git a/cliport/tasks/assembling_kits_seq.py b/cliport/tasks/assembling_kits_seq.py new file mode 100644 index 0000000000000000000000000000000000000000..390f4c253e039913aff69c22f6c32eab375a84ae --- /dev/null +++ b/cliport/tasks/assembling_kits_seq.py @@ -0,0 +1,90 @@ +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class AssemblingKitsSeq(Task): + """ Precisely place each specified shape in the specified hole following the order prescribed in the +language instruction at each timestep.""" + + def __init__(self): + super().__init__() + self.max_steps = 7 + self.homogeneous = False + + self.lang_template = "put the {color} {obj} in the {loc}{obj} hole" + self.task_completed_desc = "done assembling kit." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add kit. + kit_size = (0.28, 0.2, 0.005) + kit_urdf = 'kitting/kit.urdf' + kit_pose = self.get_random_pose(env, kit_size) + env.add_object(kit_urdf, kit_pose, 'fixed') + + # Shape Names: + shapes = utils.assembling_kit_shapes + n_objects = 5 + obj_shapes = self.get_kitting_shapes(n_objects) + colors, color_names = utils.get_colors(mode=self.mode) + + # Build kit. + targets = [] + targets_spatial_desc = [] + targ_pos = [[-0.09, 0.045, 0.0014], [0, 0.045, 0.0014], + [0.09, 0.045, 0.0014], [-0.045, -0.045, 0.0014], + [0.045, -0.045, 0.0014]] + template = 'kitting/object-template.urdf' + + for i in range(n_objects): + shape = os.path.join(self.assets_root, 'kitting', + f'{obj_shapes[i]:02d}.obj') + scale = [0.003, 0.003, 0.0001] # .0005 + pos = utils.apply(kit_pose, targ_pos[i]) + theta = np.random.rand() * 2 * np.pi + rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': [0.2, 0.2, 0.2]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + env.add_object(urdf, (pos, rot), 'fixed') + targets.append((pos, rot)) + + # Decide spatial description based on the location of the hole (top-down view). + shape_type = obj_shapes[i] + if list(obj_shapes).count(obj_shapes[i]) > 1: + duplicate_shapes = [j for j, o in enumerate(obj_shapes) if i != j and o == shape_type] + other_poses = [utils.apply(kit_pose, targ_pos[d]) for d in duplicate_shapes] + + if all(pos[0] < op[0] and abs(pos[0]-op[0]) > abs(pos[1]-op[1]) for op in other_poses): + spatial_desc = "top " + elif all(pos[0] > op[0] and abs(pos[0]-op[0]) > abs(pos[1]-op[1]) for op in other_poses): + spatial_desc = "bottom " + elif all(pos[1] < op[1] for op in other_poses): + spatial_desc = "left " + elif all(pos[1] > op[1] for op in other_poses): + spatial_desc = "right " + else: + spatial_desc = "middle " + + targets_spatial_desc.append(spatial_desc) + else: + targets_spatial_desc.append("") + + # Add objects. + objects, matches = self.make_kitting_objects(env, targets=targets, obj_shapes=obj_shapes, n_objects=n_objects, colors=colors) + target_idxs = list(range(n_objects)) + np.random.shuffle(target_idxs) + for i in target_idxs: + language_goal = (self.lang_template.format(color=color_names[i], + obj=shapes[obj_shapes[i]], + loc=targets_spatial_desc[i])) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[targets[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / n_objects, language_goal=language_goal) + + self.max_steps = n_objects diff --git a/cliport/tasks/block_insertion.py b/cliport/tasks/block_insertion.py new file mode 100644 index 0000000000000000000000000000000000000000..cd58049fab90eff1b68ee143efe59fb4137de84e --- /dev/null +++ b/cliport/tasks/block_insertion.py @@ -0,0 +1,40 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class BlockInsertion(Task): + """pick up the L-shaped red block and place it into the L-shaped fixture.""" + + def __init__(self): + super().__init__() + self.max_steps = 3 + self.lang_template = "put the L shape block in the L shape hole" + self.task_completed_desc = "done with insertion." + self.additional_reset() + + def get_random_pose(self, env, obj_size): + pose = super().get_random_pose(env, obj_size) + pos, rot = pose + rot = utils.eulerXYZ_to_quatXYZW((0, 0, np.pi / 2)) + return pos, rot + + def reset(self, env): + super().reset(env) + + """Add L-shaped block.""" + size = (0.1, 0.1, 0.04) + urdf = 'insertion/ell.urdf' + pose = self.get_random_pose(env, size) + block_id = env.add_object(urdf, pose) + + """Add L-shaped fixture to place block.""" + size = (0.1, 0.1, 0.04) + urdf = 'insertion/fixture.urdf' + targ_pose = self.get_random_pose(env, size) + env.add_object(urdf, targ_pose, 'fixed') + + self.add_goal(objs=[block_id], matches=np.int32([[1]]), targ_poses=[targ_pose], replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1, symmetries=[2 * np.pi], + language_goal=self.lang_template) diff --git a/cliport/tasks/cameras.py b/cliport/tasks/cameras.py new file mode 100644 index 0000000000000000000000000000000000000000..882c8b7e4e0a24925da393ac1bf4ea0381f832c4 --- /dev/null +++ b/cliport/tasks/cameras.py @@ -0,0 +1,107 @@ +"""Camera configs.""" + +import numpy as np +import pybullet as p + + +class RealSenseD415(): + """Default configuration with 3 RealSense RGB-D cameras.""" + + # Mimic RealSense D415 RGB-D camera parameters. + image_size = (480, 640) + intrinsics = (450., 0, 320., 0, 450., 240., 0, 0, 1) + + # Set default camera poses. + front_position = (1., 0, 0.75) + front_rotation = (np.pi / 4, np.pi, -np.pi / 2) + front_rotation = p.getQuaternionFromEuler(front_rotation) + left_position = (0, 0.5, 0.75) + left_rotation = (np.pi / 4.5, np.pi, np.pi / 4) + left_rotation = p.getQuaternionFromEuler(left_rotation) + right_position = (0, -0.5, 0.75) + right_rotation = (np.pi / 4.5, np.pi, 3 * np.pi / 4) + right_rotation = p.getQuaternionFromEuler(right_rotation) + + # Default camera configs. + CONFIG = [{ + 'image_size': image_size, + 'intrinsics': intrinsics, + 'position': front_position, + 'rotation': front_rotation, + 'zrange': (0.01, 10.), + 'noise': False + }, { + 'image_size': image_size, + 'intrinsics': intrinsics, + 'position': left_position, + 'rotation': left_rotation, + 'zrange': (0.01, 10.), + 'noise': False + }, { + 'image_size': image_size, + 'intrinsics': intrinsics, + 'position': right_position, + 'rotation': right_rotation, + 'zrange': (0.01, 10.), + 'noise': False + }] + + +class Oracle(): + """Top-down noiseless image used only by the oracle demonstrator.""" + + # Near-orthographic projection. + image_size = (480, 640) + intrinsics = (63e4, 0, 320., 0, 63e4, 240., 0, 0, 1) + position = (0.5, 0, 1000.) + rotation = p.getQuaternionFromEuler((0, np.pi, -np.pi / 2)) + + # Camera config. + CONFIG = [{ + 'image_size': image_size, + 'intrinsics': intrinsics, + 'position': position, + 'rotation': rotation, + 'zrange': (999.7, 1001.), + 'noise': False + }] + + +class RS200Gazebo(): + """Gazebo Camera""" + + # Near-orthographic projection. + image_size = (480, 640) + intrinsics = (554.3826904296875, 0.0, 320.0, 0.0, 554.3826904296875, 240.0, 0.0, 0.0, 1.0) + position = (0.5, 0, 1.0) + rotation = p.getQuaternionFromEuler((0, np.pi, np.pi / 2)) + + # Camera config. + CONFIG = [{ + 'image_size': image_size, + 'intrinsics': intrinsics, + 'position': position, + 'rotation': rotation, + 'zrange': (0.01, 10.), + 'noise': False + }] + + +class KinectFranka(): + """Kinect Franka Camera""" + + # Near-orthographic projection. + image_size = (424,512) + intrinsics = (365.57489013671875, 0.0, 257.5205078125, 0.0, 365.57489013671875, 205.26710510253906, 0.0, 0.0, 1.0) + position = (1.082, -0.041, 1.027) + rotation = p.getQuaternionFromEuler((-2.611, 0.010, 1.553)) + + # Camera config. + CONFIG = [{ + 'image_size': image_size, + 'intrinsics': intrinsics, + 'position': position, + 'rotation': rotation, + 'zrange': (0.01, 10.), + 'noise': False + }] \ No newline at end of file diff --git a/cliport/tasks/extended_tasks.py b/cliport/tasks/extended_tasks.py new file mode 100644 index 0000000000000000000000000000000000000000..4e00eb0a41db74c3f2d1092ce2c5b0475812bbe0 --- /dev/null +++ b/cliport/tasks/extended_tasks.py @@ -0,0 +1,259 @@ +from cliport.tasks.align_box_corner import AlignBoxCorner +from cliport.tasks.assembling_kits import AssemblingKits +from cliport.tasks.assembling_kits_seq import AssemblingKitsSeq +from cliport.tasks.block_insertion import BlockInsertion +from cliport.tasks.manipulating_rope import ManipulatingRope +from cliport.tasks.align_rope import AlignRope +from cliport.tasks.packing_boxes import PackingBoxes +from cliport.tasks.packing_shapes import PackingShapes +from cliport.tasks.packing_boxes_pairs import PackingBoxesPairs +from cliport.tasks.packing_google_objects import PackingSeenGoogleObjectsSeq +from cliport.tasks.palletizing_boxes import PalletizingBoxes +from cliport.tasks.place_red_in_green import PlaceRedInGreen +from cliport.tasks.put_block_in_bowl import PutBlockInBowl +from cliport.tasks.stack_block_pyramid import StackBlockPyramid +from cliport.tasks.stack_block_pyramid_seq import StackBlockPyramidSeq +from cliport.tasks.sweeping_piles import SweepingPiles +from cliport.tasks.separating_piles import SeparatingPiles +from cliport.tasks.task import Task +from cliport.tasks.towers_of_hanoi import TowersOfHanoi +from cliport.tasks.towers_of_hanoi_seq import TowersOfHanoiSeq +from cliport.tasks.generated_task import GeneratedTask + +import pybullet as p +import os +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +##################### block insertion +class BlockInsertionTranslation(BlockInsertion): + """Insertion Task - Translation Variant.""" + + def get_random_pose(self, env, obj_size): + pose = super(BlockInsertionTranslation, self).get_random_pose(env, obj_size) + pos, rot = pose + rot = utils.eulerXYZ_to_quatXYZW((0, 0, np.pi / 2)) + return pos, rot + +class BlockInsertionEasy(BlockInsertionTranslation): + """Insertion Task - Easy Variant.""" + + def add_block(self, env): + """Add L-shaped block in fixed position.""" + # size = (0.1, 0.1, 0.04) + urdf = 'insertion/ell.urdf' + pose = ((0.5, 0, 0.02), p.getQuaternionFromEuler((0, 0, np.pi / 2))) + return env.add_object(urdf, pose) + +class BlockInsertionSixDof(BlockInsertion): + """Insertion Task - 6DOF Variant.""" + + def __init__(self): + super().__init__() + self.sixdof = True + self.pos_eps = 0.02 + + def add_fixture(self, env): + """Add L-shaped fixture to place block.""" + size = (0.1, 0.1, 0.04) + urdf = 'insertion/fixture.urdf' + pose = self.get_random_pose_6dof(env, size) + env.add_object(urdf, pose, 'fixed') + return pose + + def get_random_pose_6dof(self, env, obj_size): + pos, rot = super(BlockInsertionSixDof, self).get_random_pose(env, obj_size) + z = (np.random.rand() / 10) + 0.03 + pos = (pos[0], pos[1], obj_size[2] / 2 + z) + roll = (np.random.rand() - 0.5) * np.pi / 2 + pitch = (np.random.rand() - 0.5) * np.pi / 2 + yaw = np.random.rand() * 2 * np.pi + rot = utils.eulerXYZ_to_quatXYZW((roll, pitch, yaw)) + return pos, rot + + +class BlockInsertionNoFixture(BlockInsertion): + """Insertion Task - No Fixture Variant.""" + + def add_fixture(self, env): + """Add target pose to place block.""" + size = (0.1, 0.1, 0.04) + # urdf = 'insertion/fixture.urdf' + pose = self.get_random_pose(env, size) + return pose + +# AssemblingKits +class AssemblingKitsSeqUnseenColors(AssemblingKitsSeq): + """Kitting Task - Easy variant.""" + def __init__(self): + super().__init__() + self.mode = 'test' + +class AssemblingKitsSeqSeenColors(AssemblingKitsSeqUnseenColors): + """Kitting Task - Easy variant.""" + def __init__(self): + super().__init__() + self.mode = 'train' + +class AssemblingKitsSeqFull(AssemblingKitsSeqUnseenColors): + """Kitting Task - Easy variant.""" + def __init__(self): + super().__init__() + self.mode = 'full' + + +class AssemblingKitsEasy(AssemblingKits): + """Kitting Task - Easy variant.""" + + def __init__(self): + super().__init__() + self.rot_eps = np.deg2rad(30) + self.train_set = np.int32( + [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19]) + self.test_set = np.int32([3, 11]) + self.homogeneous = True + + +# PackingBoxesPairs +class PackingBoxesPairsUnseenColors(PackingBoxesPairs): + def __init__(self): + super().__init__() + self.mode = 'test' + +class PackingBoxesPairsSeenColors(PackingBoxesPairsUnseenColors): + def __init__(self): + super().__init__() + self.mode = 'train' + +class PackingBoxesPairsFull(PackingBoxesPairsUnseenColors): + def __init__(self): + super().__init__() + self.mode = 'all' + + +# PackingUnseenGoogleObjects +class PackingUnseenGoogleObjectsSeq(PackingSeenGoogleObjectsSeq): + """Packing Unseen Google Objects Sequence task.""" + + def __init__(self): + super().__init__() + + def get_object_names(self): + return utils.google_seen_obj_shapes + +class PackingSeenGoogleObjectsGroup(PackingSeenGoogleObjectsSeq): + """Packing Seen Google Objects Group task.""" + + def __init__(self): + super().__init__() + self.lang_template = "pack all the {obj} objects in the brown box" + self.max_steps = 3 + + def choose_objects(self, object_names, k): + # Randomly choose a category to repeat. + chosen_objects = np.random.choice(object_names, k, replace=True) + repeat_category, distractor_category = np.random.choice(chosen_objects, 2, replace=False) + num_repeats = np.random.randint(2, 3) + chosen_objects[:num_repeats] = repeat_category + chosen_objects[num_repeats:2*num_repeats] = distractor_category + + return chosen_objects, repeat_category + + def set_goals(self, object_descs, object_ids, object_points, repeat_category, zone_pose, zone_size): + # Pack all objects of the chosen (repeat) category. + num_pack_objs = object_descs.count(repeat_category) + true_poses = [] + + chosen_obj_pts = dict() + chosen_obj_ids = [] + for obj_idx, (object_id, info) in enumerate(object_ids): + if object_descs[obj_idx] == repeat_category: + true_poses.append(zone_pose) + chosen_obj_pts[object_id] = object_points[object_id] + chosen_obj_ids.append((object_id, info)) + + self.goals.append(( + chosen_obj_ids, np.eye(len(chosen_obj_ids)), true_poses, False, True, 'zone', + (chosen_obj_pts, [(zone_pose, zone_size)]), 1)) + self.lang_goals.append(self.lang_template.format(obj=repeat_category)) + + # Only one mistake allowed. + self.max_steps = num_pack_objs+1 + +class PackingUnseenGoogleObjectsGroup(PackingSeenGoogleObjectsGroup): + """Packing Unseen Google Objects Group task.""" + + def __init__(self): + super().__init__() + + def get_object_names(self): + return utils.google_unseen_obj_shapes + + +# PutBlockInBowl +class PutBlockInBowlUnseenColors(PutBlockInBowl): + def __init__(self): + super().__init__() + self.mode = 'test' + +class PutBlockInBowlSeenColors(PutBlockInBowlUnseenColors): + def __init__(self): + super().__init__() + self.mode = 'train' + +class PutBlockInBowlFull(PutBlockInBowlUnseenColors): + def __init__(self): + super().__init__() + self.mode = 'full' + +# SeparatingPiles +class SeparatingPilesUnseenColors(SeparatingPiles): + def __init__(self): + super().__init__() + self.mode = 'test' + +class SeparatingPilesSeenColors(SeparatingPilesUnseenColors): + def __init__(self): + super().__init__() + self.mode = 'train' + +class SeparatingPilesFull(SeparatingPilesUnseenColors): + def __init__(self): + super().__init__() + self.mode = 'full' + + +# StackBlockPyramid +class StackBlockPyramidSeqUnseenColors(StackBlockPyramidSeq): + def __init__(self): + super().__init__() + self.mode = 'test' + + +class StackBlockPyramidSeqSeenColors(StackBlockPyramidSeqUnseenColors): + def __init__(self): + super().__init__() + self.mode = 'train' + +class StackBlockPyramidSeqFull(StackBlockPyramidSeqUnseenColors): + def __init__(self): + super().__init__() + self.mode = 'full' + +# TowersOfHanoiSeq + +class TowersOfHanoiSeqUnseenColors(TowersOfHanoiSeq): + def __init__(self): + super().__init__() + self.mode = 'test' + +class TowersOfHanoiSeqSeenColors(TowersOfHanoiSeqUnseenColors): + def __init__(self): + super().__init__() + self.mode = 'train' + +class TowersOfHanoiSeqFull(TowersOfHanoiSeqUnseenColors): + def __init__(self): + super().__init__() + self.mode = 'full' \ No newline at end of file diff --git a/cliport/tasks/generated_task.py b/cliport/tasks/generated_task.py new file mode 100644 index 0000000000000000000000000000000000000000..6f0f252e7def331252d76049410f75a3f54a5d45 --- /dev/null +++ b/cliport/tasks/generated_task.py @@ -0,0 +1,65 @@ +import numpy as np + +from cliport.tasks import Task + +from cliport.tasks.grippers import Spatula +import random + +from cliport.utils import utils + +import pybullet as p +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class GeneratedTask(Task): + """Build a car using blocks.""" + + + """Stack 5 cylinders of different colors (red, blue, green, yellow, and orange) on top of each other in a pyramid shape on a table.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "stack 5 cylinders of different colors (red, blue, green, yellow, and orange) on top of each other in a pyramid shape on a table" + self.task_completed_desc = "done stacking cylinders." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add table. + table_size = (0.4, 0.4, 0.02) + table_urdf = 'table/table.urdf' + table_pose = self.get_random_pose(env, table_size) + env.add_object(table_urdf, table_pose, 'fixed') + + # Cylinder colors. + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow'], utils.COLORS['orange']] + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for i in range(5): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=colors[i]) + cylinders.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.1, 0.02), (0, 0.1, 0.02), (0, -0.05, 0.06), (0, 0.05, 0.06), (0, 0, 0.1)] + targs = [(utils.apply(cylinder_pose, pos), cylinder_pose[1]) for pos in place_pos] + + # Goal: cylinders are stacked in a pyramid shape. + self.add_goal(objs=cylinders, matches=np.ones((5, 5)), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, symmetries=[np.pi/2]*5, + language_goal=self.lang_template) diff --git a/cliport/tasks/grippers.py b/cliport/tasks/grippers.py new file mode 100644 index 0000000000000000000000000000000000000000..f6f8d55292eb7f394a825ceee668898b58aae11f --- /dev/null +++ b/cliport/tasks/grippers.py @@ -0,0 +1,249 @@ +"""Classes to handle gripper dynamics.""" + +import os + +import numpy as np +from cliport.utils import pybullet_utils + +import pybullet as p + +SPATULA_BASE_URDF = 'ur5/spatula/spatula-base.urdf' +SUCTION_BASE_URDF = 'ur5/suction/suction-base.urdf' +SUCTION_HEAD_URDF = 'ur5/suction/suction-head.urdf' + + +class Gripper: + """Base gripper class.""" + + def __init__(self, assets_root): + self.assets_root = assets_root + self.activated = False + + def step(self): + """This function can be used to create gripper-specific behaviors.""" + return + + def activate(self, objects): + del objects + return + + def release(self): + return + + +class Spatula(Gripper): + """Simulate simple spatula for pushing.""" + + def __init__(self, assets_root=None, robot=None, ee=None, obj_ids=None): + """Creates spatula and 'attaches' it to the robot.""" + if assets_root is None: + return + super().__init__(assets_root) + + # Load spatula model. + pose = ((0.487, 0.109, 0.438), p.getQuaternionFromEuler((np.pi, 0, 0))) + self.base_urdf_path = os.path.join(self.assets_root, SPATULA_BASE_URDF) + + base = pybullet_utils.load_urdf( + p, self.base_urdf_path, pose[0], pose[1]) + self.base = base + p.createConstraint( + parentBodyUniqueId=robot, + parentLinkIndex=ee, + childBodyUniqueId=base, + childLinkIndex=-1, + jointType=p.JOINT_FIXED, + jointAxis=(0, 0, 0), + parentFramePosition=(0, 0, 0), + childFramePosition=(0, 0, 0.01)) + + +class Suction(Gripper): + """Simulate simple suction dynamics.""" + + def __init__(self, assets_root, robot, ee, obj_ids): + """Creates suction and 'attaches' it to the robot. + + Has special cases when dealing with rigid vs deformables. For rigid, + only need to check contact_constraint for any constraint. For soft + bodies (i.e., cloth or bags), use cloth_threshold to check distances + from gripper body (self.body) to any vertex in the cloth mesh. We + need correct code logic to handle gripping potentially a rigid or a + deformable (and similarly for releasing). + + To be clear on terminology: 'deformable' here should be interpreted + as a PyBullet 'softBody', which includes cloths and bags. There's + also cables, but those are formed by connecting rigid body beads, so + they can use standard 'rigid body' grasping code. + + To get the suction gripper pose, use p.getLinkState(self.body, 0), + and not p.getBasePositionAndOrientation(self.body) as the latter is + about z=0.03m higher and empirically seems worse. + + Args: + assets_root: str for root directory with assets. + robot: int representing PyBullet ID of robot. + ee: int representing PyBullet ID of end effector link. + obj_ids: list of PyBullet IDs of all suctionable objects in the env. + """ + super().__init__(assets_root) + + # Load suction gripper base model (visual only). + pose = ((0.487, 0.109, 0.438), p.getQuaternionFromEuler((np.pi, 0, 0))) + self.base_urdf_path = os.path.join(self.assets_root, SUCTION_BASE_URDF) + + base = pybullet_utils.load_urdf( + p, self.base_urdf_path, pose[0], pose[1]) + self.base = base + p.createConstraint( + parentBodyUniqueId=robot, + parentLinkIndex=ee, + childBodyUniqueId=base, + childLinkIndex=-1, + jointType=p.JOINT_FIXED, + jointAxis=(0, 0, 0), + parentFramePosition=(0, 0, 0), + childFramePosition=(0, 0, 0.01)) + + # Load suction tip model (visual and collision) with compliance. + # urdf = 'assets/ur5/suction/suction-head.urdf' + pose = ((0.487, 0.109, 0.347), p.getQuaternionFromEuler((np.pi, 0, 0))) + self.urdf_path = os.path.join(self.assets_root, SUCTION_HEAD_URDF) + self.body = pybullet_utils.load_urdf( + p, self.urdf_path, pose[0], pose[1]) + constraint_id = p.createConstraint( + parentBodyUniqueId=robot, + parentLinkIndex=ee, + childBodyUniqueId=self.body, + childLinkIndex=-1, + jointType=p.JOINT_FIXED, + jointAxis=(0, 0, 0), + parentFramePosition=(0, 0, 0), + childFramePosition=(0, 0, -0.08)) + p.changeConstraint(constraint_id, maxForce=100) + + # Reference to object IDs in environment for simulating suction. + self.obj_ids = obj_ids + + # Indicates whether gripper is gripping anything (rigid or def). + self.activated = False + + # For gripping and releasing rigid objects. + self.contact_constraint = None + + # Defaults for deformable parameters, and can override in tasks. + self.def_ignore = 0.035 # TODO(daniel) check if this is needed + self.def_threshold = 0.030 + self.def_nb_anchors = 1 + + # Track which deformable is being gripped (if any), and anchors. + self.def_grip_item = None + self.def_grip_anchors = [] + + # Determines release when gripped deformable touches a rigid/def. + # TODO(daniel) should check if the code uses this -- not sure? + self.def_min_vetex = None + self.def_min_distance = None + + # Determines release when a gripped rigid touches defs (e.g. cloth-cover). + self.init_grip_distance = None + self.init_grip_item = None + + def activate(self): + """Simulate suction using a rigid fixed constraint to contacted object.""" + # TODO(andyzeng): check deformables logic. + # del def_ids + + if not self.activated: + points = p.getContactPoints(bodyA=self.body, linkIndexA=0) + # print(points) + if points: + + # Handle contact between suction with a rigid object. + for point in points: + obj_id, contact_link = point[2], point[4] + if obj_id in self.obj_ids['rigid']: + body_pose = p.getLinkState(self.body, 0) + obj_pose = p.getBasePositionAndOrientation(obj_id) + world_to_body = p.invertTransform(body_pose[0], body_pose[1]) + obj_to_body = p.multiplyTransforms(world_to_body[0], + world_to_body[1], + obj_pose[0], obj_pose[1]) + self.contact_constraint = p.createConstraint( + parentBodyUniqueId=self.body, + parentLinkIndex=0, + childBodyUniqueId=obj_id, + childLinkIndex=contact_link, + jointType=p.JOINT_FIXED, + jointAxis=(0, 0, 0), + parentFramePosition=obj_to_body[0], + parentFrameOrientation=obj_to_body[1], + childFramePosition=(0, 0, 0), + childFrameOrientation=(0, 0, 0)) + + self.activated = True + + def release(self): + """Release gripper object, only applied if gripper is 'activated'. + + If suction off, detect contact between gripper and objects. + If suction on, detect contact between picked object and other objects. + + To handle deformables, simply remove constraints (i.e., anchors). + Also reset any relevant variables, e.g., if releasing a rigid, we + should reset init_grip values back to None, which will be re-assigned + in any subsequent grasps. + """ + if self.activated: + self.activated = False + + # Release gripped rigid object (if any). + if self.contact_constraint is not None: + try: + p.removeConstraint(self.contact_constraint) + self.contact_constraint = None + except: # pylint: disable=bare-except + pass + self.init_grip_distance = None + self.init_grip_item = None + + # Release gripped deformable object (if any). + if self.def_grip_anchors: + for anchor_id in self.def_grip_anchors: + p.removeConstraint(anchor_id) + self.def_grip_anchors = [] + self.def_grip_item = None + self.def_min_vetex = None + self.def_min_distance = None + + def detect_contact(self): + """Detects a contact with a rigid object.""" + body, link = self.body, 0 + if self.activated and self.contact_constraint is not None: + try: + info = p.getConstraintInfo(self.contact_constraint) + body, link = info[2], info[3] + except: # pylint: disable=bare-except + self.contact_constraint = None + pass + + # Get all contact points between the suction and a rigid body. + points = p.getContactPoints(bodyA=body, linkIndexA=link) + # print(points) + # exit() + if self.activated: + points = [point for point in points if point[2] != self.body] + + # # We know if len(points) > 0, contact is made with SOME rigid item. + if points: + return True + + return False + + def check_grasp(self): + """Check a grasp (object in contact?) for picking success.""" + + suctioned_object = None + if self.contact_constraint is not None: + suctioned_object = p.getConstraintInfo(self.contact_constraint)[2] + return suctioned_object is not None diff --git a/cliport/tasks/manipulating_rope.py b/cliport/tasks/manipulating_rope.py new file mode 100644 index 0000000000000000000000000000000000000000..ef4b16c02338316b47ce7502716611438608efe6 --- /dev/null +++ b/cliport/tasks/manipulating_rope.py @@ -0,0 +1,51 @@ +import os + +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.task import Task +from cliport.utils import utils + +import pybullet as p + + +class ManipulatingRope(Task): + """rearrange a deformable rope such that it connects the two endpoints of a 3-sided square.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "manipulate the rope to complete the square" + self.task_completed_desc = "done manipulating the rope." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + n_parts = 20 + radius = 0.005 + length = 2 * radius * n_parts * np.sqrt(2) + + # Add 3-sided square. + square_size = (length, length, 0) + square_pose = self.get_random_pose(env, square_size) + square_template = 'square/square-template.urdf' + + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + replace = {'DIM': (length,), 'HALF': (np.float32(length) / 2 - 0.005,)} + urdf = self.fill_template(square_template, replace) + env.add_object(urdf, square_pose, 'fixed') + + # compute corners + corner0 = (length / 2, length / 2, 0.001) + corner1 = (-length / 2, length / 2, 0.001) + corner_0 = utils.apply(square_pose, corner0) + corner_1 = utils.apply(square_pose, corner1) + + # IMPORTANT: use `make_ropes` to add cable (series of articulated small blocks). + objects, targets, matches = self.make_ropes(env, corners=(corner_0, corner_1)) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1., lang_goal=self.lang_template) + + for i in range(480): + p.stepSimulation() diff --git a/cliport/tasks/packing_boxes.py b/cliport/tasks/packing_boxes.py new file mode 100644 index 0000000000000000000000000000000000000000..e7bc1f2e36083268a845b8b5650432b49f723031 --- /dev/null +++ b/cliport/tasks/packing_boxes.py @@ -0,0 +1,82 @@ +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +import pybullet as p + + +class PackingBoxes(Task): + """pick up randomly sized boxes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "pack all the boxes inside the brown box" + self.task_completed_desc = "done packing boxes." + + self.zone_bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.08]]) + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container box. + zone_size = self.get_random_size(0.05, 0.3, 0.05, 0.3, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + margin = 0.01 + min_object_dim = 0.05 + bboxes = [] + + # Split container space with KD trees. + stack_size = np.array(zone_size) + stack_size[0] -= 0.01 + stack_size[1] -= 0.01 + root_size = (0.01, 0.01, 0) + tuple(stack_size) + root = utils.TreeNode(None, [], bbox=np.array(root_size)) + utils.KDTree(root, min_object_dim, margin, bboxes) + + colors = [utils.COLORS[c] for c in utils.COLORS if c != 'brown'] + + # Add objects in container. + object_ids = [] + bboxes = np.array(bboxes) + object_template = 'box/box-template.urdf' + + # Compute object points that are needed for zone + for bbox in bboxes: + size = bbox[3:] - bbox[:3] + position = size / 2. + bbox[:3] + position[0] += -zone_size[0] / 2 + position[1] += -zone_size[1] / 2 + pose = (position, (0, 0, 0, 1)) + pose = utils.multiply(zone_pose, pose) + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(object_template, {'DIM': size}) + icolor = np.random.choice(range(len(colors)), 1).squeeze() + box_id = env.add_object(urdf, pose, color=colors[icolor]) + object_ids.append(box_id) + + # Randomly select object in box and save ground truth pose. + object_volumes = [] + true_poses = [] + + for object_id in object_ids: + true_pose = p.getBasePositionAndOrientation(object_id) + object_size = p.getVisualShapeData(object_id)[0][3] + object_volumes.append(np.prod(np.array(object_size) * 100)) + pose = self.get_random_pose(env, object_size) + p.resetBasePositionAndOrientation(object_id, pose[0], pose[1]) + true_poses.append(true_pose) + + self.add_goal(objs=object_ids, matches=np.eye(len(object_ids)), targ_poses=true_poses, replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/tasks/packing_boxes_pairs.py b/cliport/tasks/packing_boxes_pairs.py new file mode 100644 index 0000000000000000000000000000000000000000..508c2eb81cb8fe5de3e7890d0c9087655c7219a7 --- /dev/null +++ b/cliport/tasks/packing_boxes_pairs.py @@ -0,0 +1,112 @@ +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +import pybullet as p + + +class PackingBoxesPairs(Task): + """Tightly pack all the boxes of two specified colors inside the brown box.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "pack all the {colors} blocks into the brown box" # should have called it boxes :( + self.task_completed_desc = "done packing blocks." + + # Tight z-bound (0.0525) to discourage stuffing everything into the brown box + self.zone_bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.0525]]) + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container box. + zone_size = self.get_random_size(0.05, 0.3, 0.05, 0.3, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + margin = 0.01 + min_object_dim = 0.05 + bboxes = [] + + # Split container space with KD trees. + stack_size = np.array(zone_size) + stack_size[0] -= 0.01 + stack_size[1] -= 0.01 + root_size = (0.01, 0.01, 0) + tuple(stack_size) + root = utils.TreeNode(None, [], bbox=np.array(root_size)) + utils.KDTree(root, min_object_dim, margin, bboxes) + + # select colors + all_colors, all_color_names = utils.get_colors(mode=self.mode) + selected_idx = np.random.choice(range(len(all_colors)), 2, replace=False) + + relevant_color_names = [c for idx, c in enumerate(all_color_names) if idx in selected_idx] + distractor_colors = [c for idx, c in enumerate(all_color_names) if idx not in selected_idx] + + pack_colors = [c for idx, c in enumerate(all_colors) if idx in selected_idx] + distractor_colors = [c for idx, c in enumerate(all_colors) if idx not in selected_idx] + + # Add objects in container. + object_ids = [] + bboxes = np.array(bboxes) + object_template = 'box/box-template.urdf' + for bbox in bboxes: + size = bbox[3:] - bbox[:3] + position = size / 2. + bbox[:3] + position[0] += -zone_size[0] / 2 + position[1] += -zone_size[1] / 2 + pose = (position, (0, 0, 0, 1)) + pose = utils.multiply(zone_pose, pose) + urdf = self.fill_template(object_template, {'DIM': size}) + box_id = env.add_object(urdf, pose) + + object_ids.append(box_id) + icolor = np.random.choice(range(len(pack_colors)), 1).squeeze() + p.changeVisualShape(box_id, -1, rgbaColor=pack_colors[icolor] + [1]) + + # Randomly select object in box and save ground truth pose. + object_volumes = [] + true_poses = [] + for object_id in object_ids: + true_pose = p.getBasePositionAndOrientation(object_id) + object_size = p.getVisualShapeData(object_id)[0][3] + object_volumes.append(np.prod(np.array(object_size) * 100)) + pose = self.get_random_pose(env, object_size) + p.resetBasePositionAndOrientation(object_id, pose[0], pose[1]) + true_poses.append(true_pose) + + # Add distractor objects + num_distractor_objects = 4 + distractor_bbox_idxs = np.random.choice(len(bboxes), num_distractor_objects) + for bbox_idx in distractor_bbox_idxs: + bbox = bboxes[bbox_idx] + size = bbox[3:] - bbox[:3] + position = size / 2. + bbox[:3] + position[0] += -zone_size[0] / 2 + position[1] += -zone_size[1] / 2 + + pose = self.get_random_pose(env, size) + urdf = self.fill_template(object_template, {'DIM': size}) + box_id = env.add_object(urdf, pose) + + icolor = np.random.choice(range(len(distractor_colors)), 1).squeeze() + if box_id: + p.changeVisualShape(box_id, -1, rgbaColor=distractor_colors[icolor] + [1]) + + # Some scenes might contain just one relevant block that fits in the box. + if len(relevant_color_names) > 1: + relevant_desc = f'{relevant_color_names[0]} and {relevant_color_names[1]}' + else: + relevant_desc = f'{relevant_color_names[0]}' + + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(colors=relevant_desc) + self.add_goal(objs=object_ids, matches=np.eye(len(object_ids)), targ_poses=true_poses, replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=language_goal) \ No newline at end of file diff --git a/cliport/tasks/packing_google_objects.py b/cliport/tasks/packing_google_objects.py new file mode 100644 index 0000000000000000000000000000000000000000..3716d33e5dbbdff3a313642eacd3fcbb672a9295 --- /dev/null +++ b/cliport/tasks/packing_google_objects.py @@ -0,0 +1,127 @@ +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +import pybullet as p + + +class PackingSeenGoogleObjectsSeq(Task): + """: Place the specified objects in the brown box following the order prescribed in the language +instruction at each timestep.""" + + def __init__(self): + super().__init__() + self.max_steps = 6 + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing objects." + self.object_names = self.get_object_names() + self.additional_reset() + + def get_object_names(self): + return utils.google_all_shapes + + def reset(self, env): + super().reset(env) + + # object names + object_names = self.object_names[self.mode] + + # Add container box. + zone_size = self.get_random_size(0.2, 0.35, 0.2, 0.35, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template_DIM_HALF.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + margin = 0.01 + min_object_dim = 0.08 + bboxes = [] + + # Split container space with KD trees. + stack_size = np.array(zone_size) + stack_size[0] -= 0.01 + stack_size[1] -= 0.01 + root_size = (0.01, 0.01, 0) + tuple(stack_size) + root = utils.TreeNode(None, [], bbox=np.array(root_size)) + utils.KDTree(root, min_object_dim, margin, bboxes) + + # Add Google Scanned Objects to scene. + object_ids = [] + bboxes = np.array(bboxes) + scale_factor = 5 + object_template = 'google/object-template_FNAME_COLOR_SCALE.urdf' + chosen_objs, repeat_category = self.choose_objects(object_names, len(bboxes)) + object_descs = [] + for i, bbox in enumerate(bboxes): + size = bbox[3:] - bbox[:3] + max_size = size.max() + position = size / 2. + bbox[:3] + position[0] += -zone_size[0] / 2 + position[1] += -zone_size[1] / 2 + shape_size = max_size * scale_factor + pose = self.get_random_pose(env, size) + + # Add object only if valid pose found. + if pose[0] is not None: + # Initialize with a slightly tilted pose so that the objects aren't always erect. + slight_tilt = utils.q_mult(pose[1], (-0.1736482, 0, 0, 0.9848078)) + ps = ((pose[0][0], pose[0][1], pose[0][2]+0.05), slight_tilt) + + object_name = chosen_objs[i] + object_name_with_underscore = object_name.replace(" ", "_") + mesh_file = os.path.join(self.assets_root, + 'google', + 'meshes_fixed', + f'{object_name_with_underscore}.obj') + texture_file = os.path.join(self.assets_root, + 'google', + 'textures', + f'{object_name_with_underscore}.png') + + try: + replace = {'FNAME': (mesh_file,), + 'SCALE': [shape_size, shape_size, shape_size], + 'COLOR': (0.2, 0.2, 0.2)} + urdf = self.fill_template(object_template, replace) + box_id = env.add_object(urdf, ps) + object_ids.append((box_id, (0, None))) + + texture_id = p.loadTexture(texture_file) + p.changeVisualShape(box_id, -1, textureUniqueId=texture_id) + p.changeVisualShape(box_id, -1, rgbaColor=[1, 1, 1, 1]) + + object_descs.append(object_name) + + except Exception as e: + print("Failed to load Google Scanned Object in PyBullet") + print(object_name_with_underscore, mesh_file, texture_file) + print(f"Exception: {e}") + + self.set_goals(object_descs, object_ids, repeat_category, zone_pose, zone_size) + + for i in range(480): + p.stepSimulation() + + def choose_objects(self, object_names, k): + repeat_category = None + return np.random.choice(object_names, k, replace=False), repeat_category + + def set_goals(self, object_descs, object_ids, repeat_category, zone_pose, zone_size): + # Random picking sequence. + num_pack_objs = np.random.randint(1, len(object_ids)) + + object_ids = object_ids[:num_pack_objs] + true_poses = [] + for obj_idx, (object_id, _) in enumerate(object_ids): + true_poses.append(zone_pose) + language_goal = self.lang_template.format(obj=object_descs[obj_idx]) + self.add_goal(objs=[object_id], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / len(object_ids), + language_goal=language_goal) + + # Only mistake allowed. + self.max_steps = len(object_ids)+1 + diff --git a/cliport/tasks/packing_shapes.py b/cliport/tasks/packing_shapes.py new file mode 100644 index 0000000000000000000000000000000000000000..0c9be44c748fc39d09ebc96296e2d13ad8d758f7 --- /dev/null +++ b/cliport/tasks/packing_shapes.py @@ -0,0 +1,76 @@ +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) \ No newline at end of file diff --git a/cliport/tasks/palletizing_boxes.py b/cliport/tasks/palletizing_boxes.py new file mode 100644 index 0000000000000000000000000000000000000000..a045826ea261b676ca2fdcf315808969b21fdda7 --- /dev/null +++ b/cliport/tasks/palletizing_boxes.py @@ -0,0 +1,85 @@ +import os +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PalletizingBoxes(Task): + """Pick up homogeneous fixed-sized boxes and stack them in transposed layers on the pallet.""" + + def __init__(self): + super().__init__() + self.max_steps = 30 + self.lang_template = "stack all the boxes on the pallet" + self.task_completed_desc = "done stacking boxes." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + zone_size = (0.3, 0.25, 0.25) + zone_urdf = 'pallet/pallet.urdf' + rotation = utils.eulerXYZ_to_quatXYZW((0, 0, 0)) + zone_pose = ((0.5, 0.25, 0.02), rotation) + env.add_object(zone_urdf, zone_pose, 'fixed') + + # Add stack of boxes on pallet. + margin = 0.01 + object_ids = [] + + # x, y, z dimensions for the asset size + stack_size = (0.19, 0.19, 0.19) + box_template = 'box/box-template.urdf' + stack_dim = np.int32([2, 3, 3]) + + box_size = (stack_size - (stack_dim - 1) * margin) / stack_dim + for z in range(stack_dim[2]): + + # Transpose every layer. + stack_dim[0], stack_dim[1] = stack_dim[1], stack_dim[0] + box_size[0], box_size[1] = box_size[1], box_size[0] + + # IMPORTANT: Compute object points and store as a dictionary for the `goal` + for y in range(stack_dim[1]): + for x in range(stack_dim[0]): + position = list((x + 0.5, y + 0.5, z + 0.5) * box_size) + position[0] += x * margin - stack_size[0] / 2 + position[1] += y * margin - stack_size[1] / 2 + position[2] += z * margin + 0.03 + pose = (position, (0, 0, 0, 1)) + pose = utils.multiply(zone_pose, pose) + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(box_template, {'DIM': box_size}) + box_id = env.add_object(urdf, pose) + object_ids.append(box_id) + self.color_random_brown(box_id) + + # Randomly select top box on pallet and save ground truth pose. + targets = [] + self.steps = [] + boxes = object_ids[:] # make copy + while boxes: + _, height, object_mask = self.get_true_image(env) + top = np.argwhere(height > (np.max(height) - 0.03)) + rpixel = top[int(np.floor(np.random.random() * len(top)))] # y, x + box_id = int(object_mask[rpixel[0], rpixel[1]]) + if box_id in boxes: + position, rotation = p.getBasePositionAndOrientation(box_id) + rposition = np.float32(position) + np.float32([0, -10, 0]) + p.resetBasePositionAndOrientation(box_id, rposition, rotation) + self.steps.append(box_id) + targets.append((position, rotation)) + boxes.remove(box_id) + + self.steps.reverse() # Time-reversed depalletizing. + self.add_goal(objs=object_ids, matches=np.eye(len(object_ids)), targ_poses=targets, replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) + self.spawn_box() + + def reward(self): + reward, info = super().reward() + self.spawn_box() + return reward, info \ No newline at end of file diff --git a/cliport/tasks/place_red_in_green.py b/cliport/tasks/place_red_in_green.py new file mode 100644 index 0000000000000000000000000000000000000000..d49dbfab7da57f7fbd8c79f32b776307891cab69 --- /dev/null +++ b/cliport/tasks/place_red_in_green.py @@ -0,0 +1,61 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 diff --git a/cliport/tasks/primitives.py b/cliport/tasks/primitives.py new file mode 100644 index 0000000000000000000000000000000000000000..de6d4da015622d54c160f65dc9a1682bab649267 --- /dev/null +++ b/cliport/tasks/primitives.py @@ -0,0 +1,113 @@ +"""Motion primitives.""" + +import numpy as np +from cliport.utils import utils + + +class PickPlace(): + """Pick and place primitive.""" + + def __init__(self, height=0.32, speed=0.01): + self.height, self.speed = height, speed + + def __call__(self, movej, movep, ee, pose0, pose1): + """Execute pick and place primitive. + + Args: + movej: function to move robot joints. + movep: function to move robot end effector pose. + ee: robot end effector. + pose0: SE(3) picking pose. + pose1: SE(3) placing pose. + + Returns: + timeout: robot movement timed out if True. + """ + + pick_pose, place_pose = pose0, pose1 + + # Execute picking primitive. + prepick_to_pick = ((0, 0, 0.32), (0, 0, 0, 1)) + postpick_to_pick = ((0, 0, self.height), (0, 0, 0, 1)) + prepick_pose = utils.multiply(pick_pose, prepick_to_pick) + postpick_pose = utils.multiply(pick_pose, postpick_to_pick) + timeout = movep(prepick_pose) + + # Move towards pick pose until contact is detected. + delta = (np.float32([0, 0, -0.001]), + utils.eulerXYZ_to_quatXYZW((0, 0, 0))) + targ_pose = prepick_pose + while not ee.detect_contact(): # and target_pose[2] > 0: + targ_pose = utils.multiply(targ_pose, delta) + timeout |= movep(targ_pose) + if timeout: + return True + + # Activate end effector, move up, and check picking success. + ee.activate() + timeout |= movep(postpick_pose, self.speed) + pick_success = ee.check_grasp() + + # Execute placing primitive if pick is successful. + if pick_success: + preplace_to_place = ((0, 0, self.height), (0, 0, 0, 1)) + postplace_to_place = ((0, 0, 0.32), (0, 0, 0, 1)) + preplace_pose = utils.multiply(place_pose, preplace_to_place) + postplace_pose = utils.multiply(place_pose, postplace_to_place) + targ_pose = preplace_pose + while not ee.detect_contact(): + targ_pose = utils.multiply(targ_pose, delta) + timeout |= movep(targ_pose, self.speed) + if timeout: + return True + ee.release() + timeout |= movep(postplace_pose) + + # Move to prepick pose if pick is not successful. + else: + ee.release() + timeout |= movep(prepick_pose) + + return timeout + + +def push(movej, movep, ee, pose0, pose1): # pylint: disable=unused-argument + """Execute pushing primitive. + + Args: + movej: function to move robot joints. + movep: function to move robot end effector pose. + ee: robot end effector. + pose0: SE(3) starting pose. + pose1: SE(3) ending pose. + + Returns: + timeout: robot movement timed out if True. + """ + + # Adjust push start and end positions. + pos0 = np.float32((pose0[0][0], pose0[0][1], 0.005)) + pos1 = np.float32((pose1[0][0], pose1[0][1], 0.005)) + vec = np.float32(pos1) - np.float32(pos0) + length = np.linalg.norm(vec) + vec = vec / length + pos0 -= vec * 0.02 + pos1 -= vec * 0.05 + + # Align spatula against push direction. + theta = np.arctan2(vec[1], vec[0]) + rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + + over0 = (pos0[0], pos0[1], 0.31) + over1 = (pos1[0], pos1[1], 0.31) + + # Execute push. + timeout = movep((over0, rot)) + timeout |= movep((pos0, rot)) + n_push = np.int32(np.floor(np.linalg.norm(pos1 - pos0) / 0.01)) + for _ in range(n_push): + target = pos0 + vec * n_push * 0.01 + timeout |= movep((target, rot), speed=0.003) + timeout |= movep((pos1, rot), speed=0.003) + timeout |= movep((over1, rot)) + return timeout diff --git a/cliport/tasks/put_block_in_bowl.py b/cliport/tasks/put_block_in_bowl.py new file mode 100644 index 0000000000000000000000000000000000000000..8d0649b3d07fca52aa361f48afc998d9e5607241 --- /dev/null +++ b/cliport/tasks/put_block_in_bowl.py @@ -0,0 +1,69 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import random +import pybullet as p + + +class PutBlockInBowl(Task): + """Place all blocks of a specified color in a bowl of specified color.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the {pick} blocks in a {place} bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + colors, selected_color_names = utils.get_colors(mode=self.mode, n_colors=2) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_id = env.add_object(bowl_urdf, bowl_pose, category='fixed', color=colors[1]) + bowl_poses.append(bowl_pose) + + # Add blocks. + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[0]) + blocks.append(block_id) + + # Goal: put each block in a different bowl. + language_goal = (self.lang_template.format(pick=selected_color_names[0], place=selected_color_names[1])) + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=language_goal) + + + # Only one mistake allowed. + self.max_steps = len(blocks) + 1 + + # Colors of distractor objects. + distractor_bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c not in selected_color_names] + distractor_block_colors = [utils.COLORS[c] for c in utils.COLORS if c not in selected_color_names] + + # Add distractors. + n_distractors = 0 + max_distractors = 6 + while n_distractors < max_distractors: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = distractor_block_colors if is_block else distractor_bowl_colors + pose = self.get_random_pose(env, size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 \ No newline at end of file diff --git a/cliport/tasks/separating_piles.py b/cliport/tasks/separating_piles.py new file mode 100644 index 0000000000000000000000000000000000000000..bf6d414f4ccc05d173d61bd176ad3d09564ec955 --- /dev/null +++ b/cliport/tasks/separating_piles.py @@ -0,0 +1,53 @@ +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +import random +import pybullet as p + + +class SeparatingPiles(Task): + """Sweep the pile of blocks into the specified zone. Each scene contains two square zones: one +relevant to the task, another as a distractor.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of {block_color} blocks into the {square_color} square" + self.task_completed_desc = "done separating pile." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # sample colors + (zone1_color, zone2_color, block_color), color_names = utils.get_colors(mode=self.mode, n_colors=3) + + # Add goal zone. + zone_size = (0.15, 0.15, 0) + zone1_pose = self.get_random_pose(env, zone_size) + zone2_pose = self.get_random_pose(env, zone_size) + while np.linalg.norm(np.array(zone2_pose[0]) - np.array(zone1_pose[0])) < 0.2: + zone2_pose = self.get_random_pose(env, zone_size) + + zone1_obj_id = env.add_object('zone/zone.urdf', zone1_pose, 'fixed') + p.changeVisualShape(zone1_obj_id, -1, rgbaColor=zone1_color + [1]) + zone2_obj_id = env.add_object('zone/zone.urdf', zone2_pose, 'fixed') + p.changeVisualShape(zone2_obj_id, -1, rgbaColor=zone2_color + [1]) + + # Choose zone + zone_target_idx = random.randint(0, 1) + zone_target = [zone1_pose, zone2_pose][zone_target_idx] + zone_target_color = [color_names[0], color_names[1]][zone_target_idx] + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env, block_color=block_color) + + # Goal: all small blocks must be in the correct zone. + language_goal = self.lang_template.format(block_color=color_names[2], square_color=zone_target_color) + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_target], replace=True, + rotations=False, metric='zone', params=[(zone_target, zone_size)], step_max_reward=1, language_goal=language_goal) \ No newline at end of file diff --git a/cliport/tasks/stack_block_pyramid.py b/cliport/tasks/stack_block_pyramid.py new file mode 100644 index 0000000000000000000000000000000000000000..d528b32cbac3b789e4cb9a0b099a640cf63811f8 --- /dev/null +++ b/cliport/tasks/stack_block_pyramid.py @@ -0,0 +1,60 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) \ No newline at end of file diff --git a/cliport/tasks/stack_block_pyramid_seq.py b/cliport/tasks/stack_block_pyramid_seq.py new file mode 100644 index 0000000000000000000000000000000000000000..e474f89e7cdb83f0d6dc859bb5d79509c74785b6 --- /dev/null +++ b/cliport/tasks/stack_block_pyramid_seq.py @@ -0,0 +1,73 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +import pybullet as p +import random + +class StackBlockPyramidSeq(Task): + """Stacking Block Pyramid Sequence base class.""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "put the {pick} block on {place}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + # x, y, z dimensions for the asset size + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, 'fixed') + + # Block colors. + colors, color_names = utils.get_colors(self.mode) + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: make bottom row. + language_goal = (self.lang_template.format(pick=color_names[0], place="the lightest brown block")) + self.add_goal(objs=[objs[0]], matches=np.ones((1, 1)), targ_poses=[targs[0]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2], language_goal=language_goal) + + language_goal = (self.lang_template.format(pick=color_names[1], place="the middle brown block")) + self.add_goal(objs=[objs[1]], matches=np.ones((1, 1)), targ_poses=[targs[1]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2], language_goal=language_goal) + + language_goal = (self.lang_template.format(pick=color_names[2], place="the darkest brown block")) + self.add_goal(objs=[objs[2]], matches=np.ones((1, 1)), targ_poses=[targs[2]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2], language_goal=language_goal) + + # Goal: make middle row. + language_goal = (self.lang_template.format(pick=color_names[3], place=f"the {color_names[0]} and {color_names[1]} blocks")) + self.add_goal(objs=[objs[3]], matches=np.ones((1, 1)), targ_poses=[targs[3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2], language_goal=language_goal) + + language_goal = (self.lang_template.format(pick=color_names[4], place=f"the {color_names[1]} and {color_names[2]} blocks")) + self.add_goal(objs=[objs[4]], matches=np.ones((1, 1)), targ_poses=[targs[4]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2], language_goal=language_goal) + + # Goal: make top row. + language_goal = (self.lang_template.format(pick=color_names[5], place=f"the {color_names[3]} and {color_names[4]} blocks")) + self.add_goal(objs=[objs[5]], matches=np.ones((1, 1)), targ_poses=[targs[5]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2], language_goal=language_goal) \ No newline at end of file diff --git a/cliport/tasks/sweeping_piles.py b/cliport/tasks/sweeping_piles.py new file mode 100644 index 0000000000000000000000000000000000000000..36096e16333b10ac5fdd5c0fed2093eff1b658f5 --- /dev/null +++ b/cliport/tasks/sweeping_piles.py @@ -0,0 +1,33 @@ +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/tasks/task.py b/cliport/tasks/task.py new file mode 100644 index 0000000000000000000000000000000000000000..a8e0fe843e469468bc5ed9b12206022919fafd05 --- /dev/null +++ b/cliport/tasks/task.py @@ -0,0 +1,655 @@ +"""Base Task class.""" + +import collections +import os +import random +import string +import tempfile + +import cv2 +import numpy as np +from cliport.tasks import cameras +from cliport.tasks import primitives +from cliport.tasks.grippers import Suction +from cliport.utils import utils +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +import pybullet as p +from typing import Tuple, List +import re + +class Task(): + """Base Task class.""" + + def __init__(self): + self.ee = Suction + self.mode = 'train' + self.sixdof = False + self.primitive = primitives.PickPlace() + self.oracle_cams = cameras.Oracle.CONFIG + + # Evaluation epsilons (for pose evaluation metric). + self.pos_eps = 0.01 + self.rot_eps = np.deg2rad(15) + + # for piles + self.num_blocks = 50 + + # Workspace bounds. + self.pix_size = 0.003125 + self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]]) + self.zone_bounds = np.copy(self.bounds) + + self.goals = [] + self.lang_goals = [] + self.obj_points_cache = {} + + self.task_completed_desc = "task completed." + self.progress = 0 + self._rewards = 0 + + self.train_set = np.arange(0, 14) + self.test_set = np.arange(14, 20) + self.assets_root = None + self.homogeneous = False + + def reset(self, env): + if not self.assets_root: + raise ValueError('assets_root must be set for task, ' + 'call set_assets_root().') + self.goals = [] + self.lang_goals = [] + self.progress = 0 # Task progression metric in range [0, 1]. + self._rewards = 0 # Cumulative returned rewards. + self.obj_points_cache = {} + + def additional_reset(self): + # Additional changes to make the environment adaptable + if 'bowl' in self.lang_template: + # IMPORTANT: increase position tolerance for bowl placement + self.pos_eps = 0.05 + + if 'piles' in self.lang_template: + # IMPORTANT: Define the primitive to be push and ee to be spatula for tasks involving piles + self.ee = Spatula + self.primitive = primitives.push + + if 'rope' in self.lang_template: + self.primitive = primitives.PickPlace(height=0.02, speed=0.001) + self.pos_eps = 0.02 + + # ------------------------------------------------------------------------- + # Oracle Agent + # ------------------------------------------------------------------------- + + def oracle(self, env): + """Oracle agent.""" + OracleAgent = collections.namedtuple('OracleAgent', ['act']) + + def act(obs, info): + """Calculate action.""" + + # Oracle uses perfect RGB-D orthographic images and segmentation masks. + _, hmap, obj_mask = self.get_true_image(env) + + # Unpack next goal step. + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] + + for j, targ in enumerate(targs): + # add default orientation if missing + if len(targ) == 3 and (type(targs[j][0]) is float or type(targs[j][0]) is np.float32): + targs[j] = (targs[j], (0,0,0,1)) + + # Match objects to targets without replacement. + if not replace: + + # Modify a copy of the match matrix. + matches = matches.copy() + + # Ignore already matched objects. + for i in range(len(objs)): + if type(objs[i]) is int: + objs[i] = (objs[i], (False, None)) + + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + if self.is_match(pose, targs[j], symmetry): + matches[i, :] = 0 + matches[:, j] = 0 + + # Get objects to be picked (prioritize farthest from nearest neighbor). + nn_dists = [] + nn_targets = [] + for i in range(len(objs)): + if type(objs[i]) is int: + objs[i] = (objs[i], (False, None)) + + object_id, (symmetry, _) = objs[i] + xyz, _ = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + if len(targets_i) > 0: + + targets_xyz = np.float32([targs[j][0] for j in targets_i]) + dists = np.linalg.norm( + targets_xyz - np.float32(xyz).reshape(1, 3), axis=1) + nn = np.argmin(dists) + nn_dists.append(dists[nn]) + nn_targets.append(targets_i[nn]) + + # Handle ignored objects. + else: + nn_dists.append(0) + nn_targets.append(-1) + order = np.argsort(nn_dists)[::-1] + + # Filter out matched objects. + order = [i for i in order if nn_dists[i] > 0] + + pick_mask = None + for pick_i in order: + pick_mask = np.uint8(obj_mask == objs[pick_i][0]) + + # Erode to avoid picking on edges. + pick_mask = cv2.erode(pick_mask, np.ones((3, 3), np.uint8)) + + if np.sum(pick_mask) > 0: + break + + # Trigger task reset if no object is visible. + if pick_mask is None or np.sum(pick_mask) == 0: + self.goals = [] + self.lang_goals = [] + print('Object for pick is not visible. Skipping demonstration.') + return + + # Get picking pose. + pick_prob = np.float32(pick_mask) + pick_pix = utils.sample_distribution(pick_prob) + # For "deterministic" demonstrations on insertion-easy, use this: + pick_pos = utils.pix_to_xyz(pick_pix, hmap, + self.bounds, self.pix_size) + pick_pose = (np.asarray(pick_pos), np.asarray((0, 0, 0, 1))) + + # Get placing pose. + targ_pose = targs[nn_targets[pick_i]] + obj_pose = p.getBasePositionAndOrientation(objs[pick_i][0]) + if not self.sixdof: + obj_euler = utils.quatXYZW_to_eulerXYZ(obj_pose[1]) + obj_quat = utils.eulerXYZ_to_quatXYZW((0, 0, obj_euler[2])) + obj_pose = (obj_pose[0], obj_quat) + world_to_pick = utils.invert(pick_pose) + obj_to_pick = utils.multiply(world_to_pick, obj_pose) + pick_to_obj = utils.invert(obj_to_pick) + + if len(targ_pose) == 3 and (type(targ_pose[0]) is float or type(targ_pose[0]) is np.float32): + # add default orientation if missing + targ_pose = (targ_pose, (0,0,0,1)) + + place_pose = utils.multiply(targ_pose, pick_to_obj) + + # Rotate end effector? + if not rotations: + place_pose = (place_pose[0], (0, 0, 0, 1)) + + place_pose = (np.asarray(place_pose[0]), np.asarray(place_pose[1])) + + return {'pose0': pick_pose, 'pose1': place_pose} + + return OracleAgent(act) + + # ------------------------------------------------------------------------- + # Reward Function and Task Completion Metrics + # ------------------------------------------------------------------------- + + def reward(self): + """Get delta rewards for current timestep. + + Returns: + A tuple consisting of the scalar (delta) reward. + """ + reward, info = 0, {} + + # Unpack next goal step. + objs, matches, targs, replace, _, metric, params, max_reward = self.goals[0] + + # Evaluate by matching object poses. + step_reward = 0 + + if metric == 'pose': + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]) + if len(targets_i) > 0: + targets_i = targets_i.reshape(-1) + for j in targets_i: + target_pose = targs[j] + if self.is_match(pose, target_pose, symmetry): + step_reward += max_reward / len(objs) + print(f"object {i} match with target {j} rew: {step_reward:.3f}") + break + + # Evaluate by measuring object intersection with zone. + elif metric == 'zone': + zone_pts, total_pts = 0, 0 + zones = params + + if len(self.obj_points_cache) == 0 or objs[0][0] not in self.obj_points_cache: + for obj_id, _ in objs: + self.obj_points_cache[obj_id] = self.get_box_object_points(obj_id) + + for zone_idx, (zone_pose, zone_size) in enumerate(zones): + # Count valid points in zone. + for (obj_id, _) in objs: + pts = self.obj_points_cache[obj_id] + obj_pose = p.getBasePositionAndOrientation(obj_id) + world_to_zone = utils.invert(zone_pose) + obj_to_zone = utils.multiply(world_to_zone, obj_pose) + pts = np.float32(utils.apply(obj_to_zone, pts)) + + if len(zone_size) > 1: + valid_pts = np.logical_and.reduce([ + pts[0, :] > -zone_size[0] / 2, pts[0, :] < zone_size[0] / 2, + pts[1, :] > -zone_size[1] / 2, pts[1, :] < zone_size[1] / 2, + pts[2, :] < self.zone_bounds[2, 1]]) + + zone_pts += np.sum(np.float32(valid_pts)) + total_pts += pts.shape[1] + + if total_pts > 0: + step_reward = max_reward * (zone_pts / total_pts) + + # Get cumulative rewards and return delta. + reward = self.progress + step_reward - self._rewards + self._rewards = self.progress + step_reward + + # Move to next goal step if current goal step is complete. + if np.abs(max_reward - step_reward) < 0.01: + self.progress += max_reward # Update task progress. + self.goals.pop(0) + if len(self.lang_goals) > 0: + self.lang_goals.pop(0) + + return reward, info + + def done(self): + """Check if the task is done or has failed. + + Returns: + True if the episode should be considered a success. + """ + return (len(self.goals) == 0) or (self._rewards > 0.99) + # return zone_done or defs_done or goal_done + + # ------------------------------------------------------------------------- + # Environment Helper Functions + # ------------------------------------------------------------------------- + + def is_match(self, pose0, pose1, symmetry): + """Check if pose0 and pose1 match within a threshold. + pose0 and pose1 should both be tuples of (translation, rotation). + Return true if the pose translation and orientation errors are below certain thresholds""" + if len(pose1) == 3 and (not hasattr(pose1[0], '__len__')): + # add default orientation if missing + pose1 = (pose1, (0,0,0,1)) + # print(len(pose1) == 3, not hasattr(pose1[0], '__len__')) + # print(pose1, pose0) + # Get translational error. + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) + dist_pos = np.linalg.norm(diff_pos) + + # Get rotational error around z-axis (account for symmetries). + diff_rot = 0 + if symmetry > 0: + rot0 = np.array(utils.quatXYZW_to_eulerXYZ(pose0[1]))[2] + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + diff_rot = np.abs(rot0 - rot1) % symmetry + if diff_rot > (symmetry / 2): + diff_rot = symmetry - diff_rot + + return (dist_pos < self.pos_eps) and (diff_rot < self.rot_eps) + + def get_true_image(self, env): + """Get RGB-D orthographic heightmaps and segmentation masks.""" + + # Capture near-orthographic RGB-D images and segmentation masks. + color, depth, segm = env.render_camera(self.oracle_cams[0]) + + # Combine color with masks for faster processing. + color = np.concatenate((color, segm[Ellipsis, None]), axis=2) + + # Reconstruct real orthographic projection from point clouds. + hmaps, cmaps = utils.reconstruct_heightmaps( + [color], [depth], self.oracle_cams, self.bounds, self.pix_size) + + # Split color back into color and masks. + cmap = np.uint8(cmaps)[0, Ellipsis, :3] + hmap = np.float32(hmaps)[0, Ellipsis] + mask = np.int32(cmaps)[0, Ellipsis, 3:].squeeze() + return cmap, hmap, mask + + def get_random_pose(self, env, obj_size=0.1, **kwargs) -> (List, List): + """ + Get random collision-free object pose within workspace bounds. + :param obj_size: (3, ) contains the object size in x,y,z dimensions + return: translation (3, ), rotation (4, ) """ + + # Get erosion size of object in pixels. + max_size = np.sqrt(obj_size[0] ** 2 + obj_size[1] ** 2) + erode_size = int(np.round(max_size / self.pix_size)) + + _, hmap, obj_mask = self.get_true_image(env) + + # Randomly sample an object pose within free-space pixels. + free = np.ones(obj_mask.shape, dtype=np.uint8) + for obj_ids in env.obj_ids.values(): + for obj_id in obj_ids: + free[obj_mask == obj_id] = 0 + free[0, :], free[:, 0], free[-1, :], free[:, -1] = 0, 0, 0, 0 + free = cv2.erode(free, np.ones((erode_size, erode_size), np.uint8)) + + # if np.sum(free) == 0: + # return None, None + + if np.sum(free) == 0: + # avoid returning None + pix = (obj_mask.shape[0] // 2, obj_mask.shape[1] // 2) + else: + pix = utils.sample_distribution(np.float32(free)) + pos = utils.pix_to_xyz(pix, hmap, self.bounds, self.pix_size) + + if len(obj_size) == 2: + print("Should have z dimension in obj_size as well.") + pos = [pos[0], pos[1], 0.05] + else: + pos = [pos[0], pos[1], obj_size[2] / 2] + theta = np.random.rand() * 2 * np.pi + rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + return pos, rot + + def get_lang_goal(self): + if len(self.lang_goals) == 0: + return self.task_completed_desc + else: + return self.lang_goals[0] + + def get_reward(self): + return float(self._rewards) + + def add_corner_anchor_for_pose(self, env, pose): + corner_template = 'corner/corner-template.urdf' + replace = {'DIMX': (0.04,), 'DIMY': (0.04,)} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + corner_urdf = self.fill_template(corner_template, replace) + if len(pose) != 2: + pose = [pose,(0,0,0,1)] + env.add_object(corner_urdf, pose, 'fixed') + + + def get_target_sample_surface_points(self, model, scale, pose, num_points=50): + import trimesh + mesh = trimesh.load_mesh(model) + points = trimesh.sample.volume_mesh(mesh, num_points * 3) + points = points[:num_points] + points = points * np.array(scale) + points = utils.apply(pose, points.T) + poses = [((x,y,z),(0,0,0,1)) for x, y, z in zip(points[0], points[1], points[2])] + return poses + # ------------------------------------------------------------------------- + # Helper Functions + # ------------------------------------------------------------------------- + def check_require_obj(self, path): + return os.path.exists(path.replace(".urdf", ".obj")) + + def fill_template(self, template, replace): + """Read a file and replace key strings. + NOTE: This function must be called if a URDF has template in its name """ + + full_template_path = os.path.join(self.assets_root, template) + if not os.path.exists(full_template_path) or (self.check_require_obj(full_template_path) and 'template' not in full_template_path): + return template + + with open(full_template_path, 'r') as file: + fdata = file.read() + + for field in replace: + # if not hasattr(replace[field], '__len__'): + # replace[field] = (replace[field], ) + + for i in range(len(replace[field])): + fdata = fdata.replace(f'{field}{i}', str(replace[field][i])) + + if field == 'COLOR': + # handle gpt + pattern = r'' + code_string = re.findall(pattern, fdata) + if type(replace[field]) is str: + replace[field] = utils.COLORS[replace[field]] + for to_replace_color in code_string: + fdata = fdata.replace(f'{to_replace_color}', " ".join([str(x) for x in list(replace[field]) + [1]])) + + alphabet = string.ascii_lowercase + string.digits + rname = ''.join(random.choices(alphabet, k=16)) + tmpdir = tempfile.gettempdir() + template_filename = os.path.split(template)[-1] + fname = os.path.join(tmpdir, f'{template_filename}.{rname}') + with open(fname, 'w') as file: + file.write(fdata) + return fname + + def get_random_size(self, min_x, max_x, min_y, max_y, min_z, max_z) -> Tuple: + """Get random box size.""" + size = np.random.rand(3) + size[0] = size[0] * (max_x - min_x) + min_x + size[1] = size[1] * (max_y - min_y) + min_y + size[2] = size[2] * (max_z - min_z) + min_z + return tuple(size) + + def get_box_object_points(self, obj): + obj_shape = p.getVisualShapeData(obj) + obj_dim = obj_shape[0][3] + obj_dim = tuple(d for d in obj_dim) + xv, yv, zv = np.meshgrid( + np.arange(-obj_dim[0] / 2, obj_dim[0] / 2, 0.02), + np.arange(-obj_dim[1] / 2, obj_dim[1] / 2, 0.02), + np.arange(-obj_dim[2] / 2, obj_dim[2] / 2, 0.02), + sparse=False, indexing='xy') + return np.vstack((xv.reshape(1, -1), yv.reshape(1, -1), zv.reshape(1, -1))) + + def get_sphere_object_points(self, obj): + return self.get_box_object_points(obj) + + def get_mesh_object_points(self, obj): + mesh = p.getMeshData(obj) + mesh_points = np.array(mesh[1]) + mesh_dim = np.vstack((mesh_points.min(axis=0), mesh_points.max(axis=0))) + xv, yv, zv = np.meshgrid( + np.arange(mesh_dim[0][0], mesh_dim[1][0], 0.02), + np.arange(mesh_dim[0][1], mesh_dim[1][1], 0.02), + np.arange(mesh_dim[0][2], mesh_dim[1][2], 0.02), + sparse=False, indexing='xy') + return np.vstack((xv.reshape(1, -1), yv.reshape(1, -1), zv.reshape(1, -1))) + + def color_random_brown(self, obj): + shade = np.random.rand() + 0.5 + color = np.float32([shade * 156, shade * 117, shade * 95, 255]) / 255 + p.changeVisualShape(obj, -1, rgbaColor=color) + + def set_assets_root(self, assets_root): + self.assets_root = assets_root + + def zip_obj_ids(self, obj_ids, symmetries): + if type(obj_ids[0]) is tuple: + return obj_ids + + if symmetries is None: + symmetries = [0.] * len(obj_ids) + objs = [] + + for obj_id, symmetry in zip(obj_ids, symmetries): + objs.append((obj_id, (symmetry, None))) + return objs + + def add_goal(self, objs, matches, targ_poses, replace, rotations, metric, params, step_max_reward, + symmetries=None, language_goal=None, **kwargs): + """ Add the goal to the environment + - objs (List of Tuple [(obj_id, (float, None))] ): object ID, (the radians that the object is symmetric over, None). Do not pass in `(object id, object pose)` as the wrong tuple. or `object id` (such as `containers[i][0]`). + - matches (Binary Matrix): a binary matrix that denotes which object is matched with which target. This matrix has dimension len(objs) x len(targ_poses). + - targ_poses (List of Poses [(translation, rotation)] ): a list of target poses of tuple (translation, rotation). Don't pass in object IDs such as `bowls[i-1][0]` or `[stands[i][0]]`. + - replace (Boolean): whether each object can match with one unique target. This is important if we have one target and multiple objects. If it's set to be false, then any object matching with the target will satisfy. + - rotations (Boolean): whether the placement action has a rotation degree of freedom. + - metric (`pose` or `zone`): `pose` or `zone` that the object needs to be transported to. Example: `pose`. + - params ([(zone_target, zone_size)])): has to be [(zone_target, zone_size)] if the metric is `zone` where obj_pts is a dictionary that maps object ID to points. + - step_max_reward (float): the maximum reward of matching all the objects with all the target poses. + """ + objs = self.zip_obj_ids(objs, symmetries) + self.goals.append((objs, matches, targ_poses, replace, rotations, + metric, params, step_max_reward)) + if language_goal is not None: + if type(language_goal) is str: + self.lang_goals.append(language_goal) + elif type(language_goal) is list: + self.lang_goals.extend(language_goal) + + def make_piles(self, env, block_color=None, *args, **kwargs): + """ + add the piles objects for tasks involving piles + """ + obj_ids = [] + for _ in range(self.num_blocks): + rx = self.bounds[0, 0] + 0.15 + np.random.rand() * 0.2 + ry = self.bounds[1, 0] + 0.4 + np.random.rand() * 0.2 + xyz = (rx, ry, 0.01) + theta = np.random.rand() * 2 * np.pi + xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + obj_id = env.add_object('block/small.urdf', (xyz, xyzw)) + if block_color is not None: + p.changeVisualShape(obj_id, -1, rgbaColor=block_color + [1]) + + obj_ids.append(obj_id) + return obj_ids + + def make_rope(self, *args, **kwargs): + return self.make_ropes(*args, **kwargs) + + def make_ropes(self, env, corners, radius=0.005, n_parts=20, color_name='red', *args, **kwargs): + """ add cables simulation for tasks that involve cables """ + # Get corner points of square. + + # radius = 0.005 + length = 2 * radius * n_parts * np.sqrt(2) + corner0, corner1 = corners + # Add cable (series of articulated small blocks). + increment = (np.float32(corner1) - np.float32(corner0)) / n_parts + position, _ = self.get_random_pose(env, (0.1, 0.1, 0.1)) + position = np.float32(position) + part_shape = p.createCollisionShape(p.GEOM_BOX, halfExtents=[radius] * 3) + part_visual = p.createVisualShape(p.GEOM_SPHERE, radius=radius * 1.5) + parent_id = -1 + targets = [] + objects = [] + + for i in range(n_parts): + position[2] += np.linalg.norm(increment) + part_id = p.createMultiBody(0.1, part_shape, part_visual, + basePosition=position) + if parent_id > -1: + constraint_id = p.createConstraint( + parentBodyUniqueId=parent_id, + parentLinkIndex=-1, + childBodyUniqueId=part_id, + childLinkIndex=-1, + jointType=p.JOINT_POINT2POINT, + jointAxis=(0, 0, 0), + parentFramePosition=(0, 0, np.linalg.norm(increment)), + childFramePosition=(0, 0, 0)) + p.changeConstraint(constraint_id, maxForce=100) + + if (i > 0) and (i < n_parts - 1): + color = utils.COLORS[color_name] + [1] + p.changeVisualShape(part_id, -1, rgbaColor=color) + + env.obj_ids['rigid'].append(part_id) + parent_id = part_id + target_xyz = np.float32(corner0) + i * increment + increment / 2 + objects.append((part_id, (0, None))) + targets.append((target_xyz, (0, 0, 0, 1))) + + if hasattr(env, 'record_cfg') and 'blender_render' in env.record_cfg and env.record_cfg['blender_render']: + sphere_template = os.path.join(self.assets_root, 'sphere/sphere_rope.urdf') + env.blender_recorder.register_object(part_id, os.path.join(self.assets_root, 'sphere/sphere_rope.urdf')) + + + matches = np.clip(np.eye(n_parts) + np.eye(n_parts)[::-1], 0, 1) + return objects, targets, matches + + + def get_kitting_shapes(self, n_objects): + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects) + + return obj_shapes + + + def make_kitting_objects(self, env, targets, obj_shapes, n_objects, colors): + symmetry = [ + 2 * np.pi, 2 * np.pi, 2 * np.pi / 3, np.pi / 2, np.pi / 2, 2 * np.pi, + np.pi, 2 * np.pi / 5, np.pi, np.pi / 2, 2 * np.pi / 5, 0, 2 * np.pi, + 2 * np.pi, 2 * np.pi, 2 * np.pi, 0, 2 * np.pi / 6, 2 * np.pi, 2 * np.pi + ] + objects = [] + matches = [] + template = 'kitting/object-template.urdf' + + for i in range(n_objects): + shape = obj_shapes[i] + size = (0.08, 0.08, 0.02) + pose = self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), 'SCALE': scale, 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append((block_id, (symmetry[shape], None))) + match = np.zeros(len(targets)) + match[np.argwhere(obj_shapes == shape).reshape(-1)] = 1 + matches.append(match) + return objects, matches + + def spawn_box(self): + """Palletizing: spawn another box in the workspace if it is empty.""" + workspace_empty = True + if self.goals: + for obj in self.goals[0][0]: + obj_pose = p.getBasePositionAndOrientation(obj[0]) + workspace_empty = workspace_empty and ((obj_pose[0][1] < -0.5) or + (obj_pose[0][1] > 0)) + if not self.steps: + self.goals = [] + print('Palletized boxes toppled. Terminating episode.') + return + + if workspace_empty: + obj = self.steps[0] + theta = np.random.random() * 2 * np.pi + rotation = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + p.resetBasePositionAndOrientation(obj, [0.5, -0.25, 0.1], rotation) + self.steps.pop(0) + + # Wait until spawned box settles. + for _ in range(480): + p.stepSimulation() + + def get_asset_full_path(self, path): + return path \ No newline at end of file diff --git a/cliport/tasks/towers_of_hanoi.py b/cliport/tasks/towers_of_hanoi.py new file mode 100644 index 0000000000000000000000000000000000000000..42a7ef65417e2011c1baaec6ae8f06b7f474baaa --- /dev/null +++ b/cliport/tasks/towers_of_hanoi.py @@ -0,0 +1,49 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class TowersOfHanoi(Task): + """Sequentially move disks from one tower to another—only smaller disks can be on top of larger ones.""" + + def __init__(self): + super().__init__() + self.max_steps = 14 + self.lang_template = "solve towers of hanoi" + self.task_completed_desc = "solved towers of hanoi." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add stand. + base_size = (0.12, 0.36, 0.01) + base_urdf = 'hanoi/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, 'fixed') + + # Rod positions in base coordinates. + rod_position = ((0, -0.12, 0.03), (0, 0, 0.03), (0, 0.12, 0.03)) + + # Add disks. + disks = [] + n_disks = 3 + for i in range(n_disks): + disk_urdf = 'hanoi/disk%d.urdf' % i + pos = utils.apply(base_pose, rod_position[0]) + z = 0.015 * (n_disks - i - 2) + pos = (pos[0], pos[1], pos[2] + z) + disks.append(env.add_object(disk_urdf, (pos, base_pose[1]))) + + # Solve Hanoi sequence with dynamic programming. + hanoi_steps = utils.solve_hanoi_all(n_disks) + + # Goal: pick and place disks using Hanoi sequence. + for step in hanoi_steps: + disk_id = disks[step[0]] + targ_position = rod_position[step[2]] + targ_position = utils.apply(base_pose, targ_position) + targ_pose = (targ_position, (0, 0, 0, 1)) + self.add_goal(objs=[disk_id], matches=np.int32([[1]]), targ_poses=[targ_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(hanoi_steps), + symmetries=[0], language_goal=self.lang_template) \ No newline at end of file diff --git a/cliport/tasks/towers_of_hanoi_seq.py b/cliport/tasks/towers_of_hanoi_seq.py new file mode 100644 index 0000000000000000000000000000000000000000..bcb5304fd853592cec978824ab59d62f9db05381 --- /dev/null +++ b/cliport/tasks/towers_of_hanoi_seq.py @@ -0,0 +1,60 @@ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +import pybullet as p +import random + +class TowersOfHanoiSeq(Task): + """Move the ring to the specified peg in the language instruction at each timestep""" + + def __init__(self): + super().__init__() + self.max_steps = 14 + self.lang_template = "move the {obj} ring to the {loc}" + self.task_completed_desc = "solved towers of hanoi." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add stand. + base_size = (0.12, 0.36, 0.01) + base_urdf = 'hanoi/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, 'fixed') + + # Choose three colors for three rings. + colors, color_names = utils.get_colors(mode=self.mode, n_colors=3) + + # Rod positions in base coordinates. + rod_pos = ((0, -0.12, 0.03), (0, 0, 0.03), (0, 0.12, 0.03)) + rod_names = ('lighter brown side', 'middle of the stand', 'darker brown side') + + # Add disks. + disks = [] + disks_names = {} + n_disks = 3 + for i in range(n_disks): + disk_urdf = 'hanoi/disk%d.urdf' % i + pos = utils.apply(base_pose, rod_pos[0]) + z = 0.015 * (n_disks - i - 2) + pos = (pos[0], pos[1], pos[2] + z) + ring_id = env.add_object(disk_urdf, (pos, base_pose[1]), color=colors[i]) + disks.append(ring_id) + disks_names[ring_id] = color_names[i] + + # Solve Hanoi sequence with dynamic programming. + hanoi_steps = utils.solve_hanoi_all(n_disks) + + # Goal: pick and place disks using Hanoi sequence. + for step in hanoi_steps: + disk_id = disks[step[0]] + targ_pos = rod_pos[step[2]] + targ_pos = utils.apply(base_pose, targ_pos) + targ_pose = (targ_pos, (0, 0, 0, 1)) + language_goal = self.lang_template.format(obj=disks_names[disk_id], + loc=rod_names[step[2]]) + self.add_goal(objs=[disk_id], matches=np.int32([[1]]), targ_poses=[targ_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(hanoi_steps), + symmetries=[0] , language_goal=language_goal) \ No newline at end of file diff --git a/cliport/tests/tasks_test.py b/cliport/tests/tasks_test.py new file mode 100644 index 0000000000000000000000000000000000000000..37868a38371ecb9434d048eb2c51cb33ac07ae83 --- /dev/null +++ b/cliport/tests/tasks_test.py @@ -0,0 +1,146 @@ +"""Integration tests for dvnets tasks.""" + +from absl.testing import absltest +from absl.testing import parameterized +from cliport import tasks +from cliport.environments import environment + +ASSETS_PATH = 'cliport/environments/assets/' + + +class TaskTest(parameterized.TestCase): + + def _create_env(self): + assets_root = ASSETS_PATH + env = environment.Environment(assets_root) + env.seed(0) + return env + + def _run_oracle_in_env(self, env): + agent = env.task.oracle(env) + obs = env.reset() + info = None + done = False + for _ in range(10): + act = agent.act(obs, info) + obs, _, done, info = env.step(act) + if done: + break + + @parameterized.named_parameters(( + # demo conditioned + 'AlignBoxCorner', + tasks.AlignBoxCorner(), + ), ( + 'AssemblingKits', + tasks.AssemblingKits(), + ), ( + 'AssemblingKitsEasy', + tasks.AssemblingKitsEasy(), + ), ( + 'BlockInsertion', + tasks.BlockInsertion(), + ), ( + 'ManipulatingRope', + tasks.ManipulatingRope(), + ), ( + 'PackingBoxes', + tasks.PackingBoxes(), + ), ( + 'PalletizingBoxes', + tasks.PalletizingBoxes(), + ), ( + 'PlaceRedInGreen', + tasks.PlaceRedInGreen(), + ), ( + 'StackBlockPyramid', + tasks.StackBlockPyramid(), + ), ( + 'SweepingPiles', + tasks.SweepingPiles(), + ), ( + 'TowersOfHanoi', + tasks.TowersOfHanoi(), + + # goal conditioned + ), ( + 'AlignRope', + tasks.AlignRope(), + ), ( + 'AssemblingKitsSeqSeenColors', + tasks.AssemblingKitsSeqSeenColors(), + ), ( + 'AssemblingKitsSeqUnseenColors', + tasks.AssemblingKitsSeqUnseenColors(), + ), ( + 'AssemblingKitsSeqFull', + tasks.AssemblingKitsSeqFull(), + ), ( + 'PackingShapes', + tasks.PackingShapes(), + ), ( + 'PackingBoxesPairsSeenColors', + tasks.PackingBoxesPairsSeenColors(), + ), ( + 'PackingBoxesPairsUnseenColors', + tasks.PackingBoxesPairsUnseenColors(), + ), ( + 'PackingBoxesPairsFull', + tasks.PackingBoxesPairsFull(), + ), ( + 'PackingSeenGoogleObjectsSeq', + tasks.PackingSeenGoogleObjectsSeq(), + ), ( + 'PackingUnseenGoogleObjectsSeq', + tasks.PackingUnseenGoogleObjectsSeq(), + ), ( + 'PackingSeenGoogleObjectsGroup', + tasks.PackingSeenGoogleObjectsGroup(), + ), ( + 'PackingUnseenGoogleObjectsGroup', + tasks.PackingUnseenGoogleObjectsGroup(), + ), ( + 'PutBlockInBowlSeenColors', + tasks.PutBlockInBowlSeenColors(), + ), ( + 'PutBlockInBowlUnseenColors', + tasks.PutBlockInBowlUnseenColors(), + ), ( + 'PutBlockInBowlFull', + tasks.PutBlockInBowlFull(), + ), ( + 'StackBlockPyramidSeqSeenColors', + tasks.StackBlockPyramidSeqSeenColors(), + ), ( + 'StackBlockPyramidSeqUnseenColors', + tasks.StackBlockPyramidSeqUnseenColors(), + ), ( + 'StackBlockPyramidSeqFull', + tasks.StackBlockPyramidSeqFull(), + ), ( + 'SeparatingPilesSeenColors', + tasks.SeparatingPilesUnseenColors(), + ), ( + 'SeparatingPilesUnseenColors', + tasks.SeparatingPilesUnseenColors(), + ), ( + 'SeparatingPilesFull', + tasks.SeparatingPilesFull(), + ), ( + 'TowersOfHanoiSeqSeenColors', + tasks.TowersOfHanoiSeqSeenColors(), + ), ( + 'TowersOfHanoiSeqUnseenColors', + tasks.TowersOfHanoiSeqUnseenColors(), + ), ( + 'TowersOfHanoiSeqFull', + tasks.TowersOfHanoiSeqFull(), + )) + def test_all_tasks(self, dvnets_task): + env = self._create_env() + env.set_task(dvnets_task) + self._run_oracle_in_env(env) + + +if __name__ == '__main__': + absltest.main() diff --git a/cliport/train.py b/cliport/train.py new file mode 100644 index 0000000000000000000000000000000000000000..27494fe1639322bac086980b833ca0b1932fe02b --- /dev/null +++ b/cliport/train.py @@ -0,0 +1,133 @@ +"""Main training script.""" + +import os +from pathlib import Path + +import torch +from cliport import agents +from cliport.dataset import RavensDataset, RavensMultiTaskDataset, RavenMultiTaskDatasetBalance + +import hydra +from pytorch_lightning import Trainer +from pytorch_lightning.callbacks import ModelCheckpoint +from pytorch_lightning.loggers import WandbLogger +import numpy as np +from torch.utils.data import DataLoader +from torch.utils.data.dataloader import default_collate +import IPython +import pytorch_lightning as pl +from pytorch_lightning.utilities import rank_zero_only +import datetime +import time +import random + + +def set_seed_everywhere(seed): + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + np.random.seed(seed) + random.seed(seed) + +@hydra.main(config_path="./cfg", config_name='train', version_base="1.2") +def main(cfg): + # Logger + set_seed_everywhere(1) + wandb_logger = None + + if cfg['train']['log']: + try: + wandb_logger = WandbLogger(name=cfg['tag']) + except: + pass + + # Checkpoint saver + hydra_dir = Path(os.getcwd()) + checkpoint_path = os.path.join(cfg['train']['train_dir'], 'checkpoints') + last_checkpoint_path = os.path.join(checkpoint_path, 'last.ckpt') + last_checkpoint = last_checkpoint_path if os.path.exists(last_checkpoint_path) and cfg['train']['load_from_last_ckpt'] else None + checkpoint_callback = [ModelCheckpoint( + # monitor=cfg['wandb']['saver']['monitor'], + dirpath=os.path.join(checkpoint_path, 'best'), + save_top_k=1, + every_n_epochs=3, + save_last=True, + # every_n_train_steps=100 + )] + + # Trainer + max_epochs = cfg['train']['n_steps'] * cfg['train']['batch_size'] // cfg['train']['n_demos'] + if cfg['train']['training_step_scale'] > 0: + # scale training time depending on the tasks to ensure coverage. + max_epochs = cfg['train']['training_step_scale'] # // cfg['train']['batch_size'] + + trainer = Trainer( + accelerator='gpu', + devices=cfg['train']['gpu'], + fast_dev_run=cfg['debug'], + logger=wandb_logger, + callbacks=checkpoint_callback, + max_epochs=max_epochs, + # check_val_every_n_epoch=max_epochs // 50, + # resume_from_checkpoint=last_checkpoint, + sync_batchnorm=True, + log_every_n_steps=30, + ) + + print(f"max epochs: {max_epochs}!") + + # Resume epoch and global_steps + if last_checkpoint: + print(f"Resuming: {last_checkpoint}") + + # Config + data_dir = cfg['train']['data_dir'] + task = cfg['train']['task'] + agent_type = cfg['train']['agent'] + n_demos = cfg['train']['n_demos'] + + if agent_type == 'mdetr': + print('======import torch.multiprocessing to avioid shared memory issue======') + import torch.multiprocessing + torch.multiprocessing.set_sharing_strategy('file_system') + + # n_demos = cfg['train']['n_demos'] + # n_demos = cfg['train']['n_demos'] + n_val = cfg['train']['n_val'] + name = '{}-{}-{}'.format(task, agent_type, n_demos) + + # Datasets + dataset_type = cfg['dataset']['type'] + if 'multi' in dataset_type: + train_ds = RavensMultiTaskDataset(data_dir, cfg, group=task, mode='train', + n_demos=n_demos, augment=True) + val_ds = RavensMultiTaskDataset(data_dir, cfg, group=task, mode='val', n_demos=n_val, augment=False) + elif 'weighted' in dataset_type: + train_ds = RavenMultiTaskDatasetBalance(data_dir, cfg, group=task, mode='train', n_demos=n_demos, augment=True) + val_ds = RavenMultiTaskDatasetBalance(data_dir, cfg, group=task, mode='val', n_demos=n_val, augment=False) + else: + train_ds = RavensDataset(os.path.join(data_dir, '{}-train'.format(task)), cfg, n_demos=n_demos, augment=True) + val_ds = RavensDataset(os.path.join(data_dir, '{}-val'.format(task)), cfg, n_demos=n_val, augment=False) + + # Initialize agent + train_loader = DataLoader(train_ds, shuffle=True, + pin_memory=True, + batch_size=cfg['train']['batch_size'], + num_workers=1 ) + test_loader = DataLoader(val_ds, shuffle=False, + num_workers=1, + batch_size=cfg['train']['batch_size'], + pin_memory=True) + + agent = agents.names[agent_type](name, cfg, train_loader, test_loader) + dt_string = datetime.datetime.now().strftime("%d_%m_%Y_%H:%M:%S") + print("current time:", dt_string) + + start_time = time.time() + # Main training loop + trainer.fit(agent, ckpt_path=last_checkpoint) + + print("current time:", time.time() - start_time) + +if __name__ == '__main__': + main() diff --git a/cliport/utils/__init__.py b/cliport/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/cliport/utils/bpe_simple_vocab_16e6.txt.gz b/cliport/utils/bpe_simple_vocab_16e6.txt.gz new file mode 100644 index 0000000000000000000000000000000000000000..36a15856e00a06a9fbed8cdd34d2393fea4a3113 --- /dev/null +++ b/cliport/utils/bpe_simple_vocab_16e6.txt.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a +size 1356917 diff --git a/cliport/utils/dataaug.py b/cliport/utils/dataaug.py new file mode 100644 index 0000000000000000000000000000000000000000..3dcd1890583b6e6d228f434126f7f4e513595d5a --- /dev/null +++ b/cliport/utils/dataaug.py @@ -0,0 +1,80 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +"""Blob helper functions.""" + +import numpy as np +import cv2 +import torch +import torch.nn as nn +import random + +def chromatic_transform(im, label=None, d_h=None, d_s=None, d_l=None): + """ + Given an image array, add the hue, saturation and luminosity to the image + """ + # Set random hue, luminosity and saturation which ranges from -0.1 to 0.1 + if d_h is None: + d_h = (np.random.rand(1) - 0.5) * 0.02 * 180 + if d_l is None: + d_l = (np.random.rand(1) - 0.5) * 0.2 * 256 + if d_s is None: + d_s = (np.random.rand(1) - 0.5) * 0.2 * 256 + # Convert the BGR to HLS + hls = cv2.cvtColor(im, cv2.COLOR_BGR2HLS) + h, l, s = cv2.split(hls) + # Add the values to the image H, L, S + new_h = (h + d_h) % 180 + new_l = np.clip(l + d_l, 0, 255) + new_s = np.clip(s + d_s, 0, 255) + # Convert the HLS to BGR + new_hls = cv2.merge((new_h, new_l, new_s)).astype('uint8') + new_im = cv2.cvtColor(new_hls, cv2.COLOR_HLS2BGR) + + if label is not None: + I = np.where(label > 0) + new_im[I[0], I[1], :] = im[I[0], I[1], :] + return new_im + + +def add_noise(image): + + # random number + r = np.random.rand(1) + + # gaussian noise + if r < 0.9: + row,col,ch= image.shape + mean = 0 + var = np.random.rand(1) * 0.3 * 256 + sigma = var**0.5 + gauss = sigma * np.random.randn(row,col) + mean + gauss = np.repeat(gauss[:, :, np.newaxis], ch, axis=2) + noisy = image + gauss + noisy = np.clip(noisy, 0, 255) + else: + # motion blur + sizes = [3, 5, 7, 9, 11, 15] + size = sizes[int(np.random.randint(len(sizes), size=1))] + kernel_motion_blur = np.zeros((size, size)) + if np.random.rand(1) < 0.5: + kernel_motion_blur[int((size-1)/2), :] = np.ones(size) + else: + kernel_motion_blur[:, int((size-1)/2)] = np.ones(size) + kernel_motion_blur = kernel_motion_blur / size + noisy = cv2.filter2D(image, -1, kernel_motion_blur) + + return noisy + + +def add_noise_depth(image, level = 0.01): + row,col,ch= image.shape + noise_level = random.uniform(0, level) + gauss = noise_level * np.random.randn(row,col) + gauss = np.repeat(gauss[:, :, np.newaxis], ch, axis=2) + noisy = image + gauss + return noisy diff --git a/cliport/utils/model_checkpoint.py b/cliport/utils/model_checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..389ef024e8b750df19e116f5a37e27c9e45fdb38 --- /dev/null +++ b/cliport/utils/model_checkpoint.py @@ -0,0 +1,43 @@ +import os +import pytorch_lightning as pl +from pytorch_lightning.callbacks.base import Callback + + +class IntervalModelCheckpoint(Callback): + """ + Save a checkpoint every N steps, instead of Lightning's default that checkpoints + based on validation loss. + """ + + def __init__( + self, + dirpath, + save_intervals, + ): + """ + Args: + save_step_frequency: how often to save in steps + prefix: add a prefix to the name, only used if + use_modelcheckpoint_filename=False + use_modelcheckpoint_filename: just use the ModelCheckpoint callback's + default filename, don't use ours. + """ + self.dirpath = dirpath + self.save_intervals = save_intervals + self.best_val_loss = 1e10 + + def on_batch_end(self, trainer: pl.Trainer, _): + """ Check if we should save a checkpoint after every train batch """ + global_step = trainer.global_step + + if (global_step + 1) in self.save_intervals: + trainer.run_evaluation() + val_loss = trainer.callback_metrics['val_loss'] + filename = f"steps={global_step+1:05d}-val_loss={val_loss:0.8f}.ckpt" + ckpt_path = os.path.join(self.dirpath, filename) + trainer.save_checkpoint(ckpt_path) + + if val_loss < self.best_val_loss: + best_ckpt_path = os.path.join(self.dirpath, 'best.ckpt') + trainer.save_checkpoint(best_ckpt_path) + self.best_val_loss = val_loss diff --git a/cliport/utils/pybullet_utils.py b/cliport/utils/pybullet_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1c22aa119da1bc3ebcd1c84a3ad6064024dac974 --- /dev/null +++ b/cliport/utils/pybullet_utils.py @@ -0,0 +1,23 @@ +"""PyBullet utilities for loading assets.""" +import os +import six +import time +import pybullet as p + + +# BEGIN GOOGLE-EXTERNAL +def load_urdf(pybullet_client, file_path, *args, **kwargs): + """Loads the given URDF filepath.""" + # Handles most general file open case. + for _ in range(6): + try: + return pybullet_client.loadURDF(file_path, *args, **kwargs) + except pybullet_client.error as e: + print("PYBULLET load urdf error!") + print(e) + time.sleep(0.1) + print("missing urdf error. use dummy block.") + urdf = 'stacking/block.urdf' + return pybullet_client.loadURDF(urdf, *args, **kwargs) + +# END GOOGLE-EXTERNAL diff --git a/cliport/utils/simple_tokenizer.py b/cliport/utils/simple_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..9311a8619dc07af56567434fef7726a382e999d5 --- /dev/null +++ b/cliport/utils/simple_tokenizer.py @@ -0,0 +1,133 @@ +import gzip +import html +import os +from functools import lru_cache + +import ftfy +import regex as re + + +@lru_cache() +def default_bpe(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a signficant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe()): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152-256-2+1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v+'' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + vocab.extend(['<|startoftext|>', '<|endoftext|>']) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'} + self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + ( token[-1] + '',) + pairs = get_pairs(word) + + if not pairs: + return token+'' + + while True: + bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word)-1 and word[i+1] == second: + new_word.append(first+second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = text[0] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') + return text \ No newline at end of file diff --git a/cliport/utils/utils.py b/cliport/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8d1ecf6a5925b7a4e7ac254b8bdbf5d3f1ed1ee4 --- /dev/null +++ b/cliport/utils/utils.py @@ -0,0 +1,1257 @@ +"""Miscellaneous utilities.""" + +import cv2 +import random +import matplotlib +import matplotlib.pyplot as plt +import meshcat +import meshcat.geometry as g +import meshcat.transformations as mtf + +import PIL +import yaml +import numpy as np +from transforms3d import euler + +import pybullet as p +import kornia +from omegaconf import OmegaConf + +import os +import torch +import torchvision + + +# ----------------------------------------------------------------------------- +# HEIGHTMAP UTILS +# ----------------------------------------------------------------------------- + +def get_heightmap(points, colors, bounds, pixel_size): + """Get top-down (z-axis) orthographic heightmap image from 3D pointcloud. + + Args: + points: HxWx3 float array of 3D points in world coordinates. + colors: HxWx3 uint8 array of values in range 0-255 aligned with points. + bounds: 3x2 float array of values (rows: X,Y,Z; columns: min,max) defining + region in 3D space to generate heightmap in world coordinates. + pixel_size: float defining size of each pixel in meters. + + Returns: + heightmap: HxW float array of height (from lower z-bound) in meters. + colormap: HxWx3 uint8 array of backprojected color aligned with heightmap. + """ + width = int(np.round((bounds[0, 1] - bounds[0, 0]) / pixel_size)) + height = int(np.round((bounds[1, 1] - bounds[1, 0]) / pixel_size)) + heightmap = np.zeros((height, width), dtype=np.float32) + colormap = np.zeros((height, width, colors.shape[-1]), dtype=np.uint8) + + # Filter out 3D points that are outside of the predefined bounds. + ix = (points[Ellipsis, 0] >= bounds[0, 0]) & (points[Ellipsis, 0] < bounds[0, 1]) + iy = (points[Ellipsis, 1] >= bounds[1, 0]) & (points[Ellipsis, 1] < bounds[1, 1]) + iz = (points[Ellipsis, 2] >= bounds[2, 0]) & (points[Ellipsis, 2] < bounds[2, 1]) + valid = ix & iy & iz + points = points[valid] + colors = colors[valid] + + # Sort 3D points by z-value, which works with array assignment to simulate + # z-buffering for rendering the heightmap image. + iz = np.argsort(points[:, -1]) + points, colors = points[iz], colors[iz] + px = np.int32(np.floor((points[:, 0] - bounds[0, 0]) / pixel_size)) + py = np.int32(np.floor((points[:, 1] - bounds[1, 0]) / pixel_size)) + px = np.clip(px, 0, width - 1) + py = np.clip(py, 0, height - 1) + heightmap[py, px] = points[:, 2] - bounds[2, 0] + for c in range(colors.shape[-1]): + colormap[py, px, c] = colors[:, c] + return heightmap, colormap + + +def get_pointcloud(depth, intrinsics): + """Get 3D pointcloud from perspective depth image. + + Args: + depth: HxW float array of perspective depth in meters. + intrinsics: 3x3 float array of camera intrinsics matrix. + + Returns: + points: HxWx3 float array of 3D points in camera coordinates. + """ + height, width = depth.shape + xlin = np.linspace(0, width - 1, width) + ylin = np.linspace(0, height - 1, height) + px, py = np.meshgrid(xlin, ylin) + px = (px - intrinsics[0, 2]) * (depth / intrinsics[0, 0]) + py = (py - intrinsics[1, 2]) * (depth / intrinsics[1, 1]) + points = np.float32([px, py, depth]).transpose(1, 2, 0) + return points + + +def transform_pointcloud(points, transform): + """Apply rigid transformation to 3D pointcloud. + + Args: + points: HxWx3 float array of 3D points in camera coordinates. + transform: 4x4 float array representing a rigid transformation matrix. + + Returns: + points: HxWx3 float array of transformed 3D points. + """ + padding = ((0, 0), (0, 0), (0, 1)) + homogen_points = np.pad(points.copy(), padding, + 'constant', constant_values=1) + for i in range(3): + points[Ellipsis, i] = np.sum(transform[i, :] * homogen_points, axis=-1) + return points + + +def reconstruct_heightmaps(color, depth, configs, bounds, pixel_size): + """Reconstruct top-down heightmap views from multiple 3D pointclouds.""" + heightmaps, colormaps = [], [] + for color, depth, config in zip(color, depth, configs): + intrinsics = np.array(config['intrinsics']).reshape(3, 3) + xyz = get_pointcloud(depth, intrinsics) + position = np.array(config['position']).reshape(3, 1) + rotation = p.getMatrixFromQuaternion(config['rotation']) + rotation = np.array(rotation).reshape(3, 3) + transform = np.eye(4) + transform[:3, :] = np.hstack((rotation, position)) + xyz = transform_pointcloud(xyz, transform) + heightmap, colormap = get_heightmap(xyz, color, bounds, pixel_size) + heightmaps.append(heightmap) + colormaps.append(colormap) + return heightmaps, colormaps + + +def pix_to_xyz(pixel, height, bounds, pixel_size, skip_height=False): + """Convert from pixel location on heightmap to 3D position.""" + u, v = pixel + x = bounds[0, 0] + v * pixel_size + y = bounds[1, 0] + u * pixel_size + if not skip_height: + z = bounds[2, 0] + height[u, v] + else: + z = 0.0 + return (x, y, z) + + +def xyz_to_pix(position, bounds, pixel_size): + """Convert from 3D position to pixel location on heightmap.""" + u = int(np.round((position[1] - bounds[1, 0]) / pixel_size)) + v = int(np.round((position[0] - bounds[0, 0]) / pixel_size)) + return (u, v) + + +def unproject_vectorized(uv_coordinates, depth_values, + intrinsic, + distortion): + """Vectorized version of unproject(), for N points. + + Args: + uv_coordinates: pixel coordinates to unproject of shape (n, 2). + depth_values: depth values corresponding index-wise to the uv_coordinates of + shape (n). + intrinsic: array of shape (3, 3). This is typically the return value of + intrinsics_to_matrix. + distortion: camera distortion parameters of shape (5,). + + Returns: + xyz coordinates in camera frame of shape (n, 3). + """ + cam_mtx = intrinsic # shape [3, 3] + cam_dist = np.array(distortion) # shape [5] + + # shape of points_undistorted is [N, 2] after the squeeze(). + points_undistorted = cv2.undistortPoints( + uv_coordinates.reshape((-1, 1, 2)), cam_mtx, cam_dist).squeeze() + + x = points_undistorted[:, 0] * depth_values + y = points_undistorted[:, 1] * depth_values + + xyz = np.vstack((x, y, depth_values)).T + return xyz + + +def unproject_depth_vectorized(im_depth, depth_dist, + camera_mtx, + camera_dist): + """Unproject depth image into 3D point cloud, using calibration. + + Args: + im_depth: raw depth image, pre-calibration of shape (height, width). + depth_dist: depth distortion parameters of shape (8,) + camera_mtx: intrinsics matrix of shape (3, 3). This is typically the return + value of intrinsics_to_matrix. + camera_dist: camera distortion parameters shape (5,). + + Returns: + numpy array of shape [3, H*W]. each column is xyz coordinates + """ + h, w = im_depth.shape + + # shape of each u_map, v_map is [H, W]. + u_map, v_map = np.meshgrid(np.linspace( + 0, w - 1, w), np.linspace(0, h - 1, h)) + + adjusted_depth = depth_dist[0] + im_depth * depth_dist[1] + + # shape after stack is [N, 2], where N = H * W. + uv_coordinates = np.stack((u_map.reshape(-1), v_map.reshape(-1)), axis=-1) + + return unproject_vectorized(uv_coordinates, adjusted_depth.reshape(-1), + camera_mtx, camera_dist) + + +# ----------------------------------------------------------------------------- +# MATH UTILS +# ----------------------------------------------------------------------------- + + +def sample_distribution(prob, n_samples=1): + """Sample data point from a custom distribution.""" + flat_prob = prob.flatten() / np.sum(prob) + rand_ind = np.random.choice( + np.arange(len(flat_prob)), n_samples, p=flat_prob, replace=False) + rand_ind_coords = np.array(np.unravel_index(rand_ind, prob.shape)).T + return np.int32(rand_ind_coords.squeeze()) + + +# ------------------------------------------------------------------------- +# Transformation Helper Functions +# ------------------------------------------------------------------------- + + +def invert(pose): + return p.invertTransform(pose[0], pose[1]) + + +def multiply(pose0, pose1): + return p.multiplyTransforms(pose0[0], pose0[1], pose1[0], pose1[1]) + + +def apply(pose, position): + position = np.float32(position) + position_shape = position.shape + position = np.float32(position).reshape(3, -1) + rotation = np.float32(p.getMatrixFromQuaternion(pose[1])).reshape(3, 3) + translation = np.float32(pose[0]).reshape(3, 1) + position = rotation @ position + translation + return tuple(position.reshape(position_shape)) + + +def eulerXYZ_to_quatXYZW(rotation): # pylint: disable=invalid-name + """Abstraction for converting from a 3-parameter rotation to quaterion. + + This will help us easily switch which rotation parameterization we use. + Quaternion should be in xyzw order for pybullet. + + Args: + rotation: a 3-parameter rotation, in xyz order tuple of 3 floats + + Returns: + quaternion, in xyzw order, tuple of 4 floats + """ + euler_zxy = (rotation[2], rotation[0], rotation[1]) + quaternion_wxyz = euler.euler2quat(*euler_zxy, axes='szxy') + q = quaternion_wxyz + quaternion_xyzw = (q[1], q[2], q[3], q[0]) + return quaternion_xyzw + + +def quatXYZW_to_eulerXYZ(quaternion_xyzw): # pylint: disable=invalid-name + """Abstraction for converting from quaternion to a 3-parameter toation. + + This will help us easily switch which rotation parameterization we use. + Quaternion should be in xyzw order for pybullet. + + Args: + quaternion_xyzw: in xyzw order, tuple of 4 floats + + Returns: + rotation: a 3-parameter rotation, in xyz order, tuple of 3 floats + """ + q = quaternion_xyzw + quaternion_wxyz = np.array([q[3], q[0], q[1], q[2]]) + euler_zxy = euler.quat2euler(quaternion_wxyz, axes='szxy') + euler_xyz = (euler_zxy[1], euler_zxy[2], euler_zxy[0]) + return euler_xyz + + +def apply_transform(transform_to_from, points_from): + r"""Transforms points (3D) into new frame. + + Using transform_to_from notation. + + Args: + transform_to_from: numpy.ndarray of shape [B,4,4], SE3 + points_from: numpy.ndarray of shape [B,3,N] + + Returns: + points_to: numpy.ndarray of shape [B,3,N] + """ + num_points = points_from.shape[-1] + + # non-batched + if len(transform_to_from.shape) == 2: + ones = np.ones((1, num_points)) + + # makes these each into homogenous vectors + points_from = np.vstack((points_from, ones)) # [4,N] + points_to = transform_to_from @ points_from # [4,N] + return points_to[0:3, :] # [3,N] + + # batched + else: + assert len(transform_to_from.shape) == 3 + batch_size = transform_to_from.shape[0] + zeros = np.ones((batch_size, 1, num_points)) + points_from = np.concatenate((points_from, zeros), axis=1) + assert points_from.shape[1] == 4 + points_to = transform_to_from @ points_from + return points_to[:, 0:3, :] + + +# ----------------------------------------------------------------------------- +# IMAGE UTILS +# ----------------------------------------------------------------------------- + + +def preprocess(img, dist='transporter'): + """Pre-process input (subtract mean, divide by std).""" + + transporter_color_mean = [0.18877631, 0.18877631, 0.18877631] + transporter_color_std = [0.07276466, 0.07276466, 0.07276466] + transporter_depth_mean = 0.00509261 + transporter_depth_std = 0.00903967 + + franka_color_mean = [0.622291933, 0.628313992, 0.623031488] + franka_color_std = [0.168154213, 0.17626014, 0.184527364] + franka_depth_mean = 0.872146842 + franka_depth_std = 0.195743116 + + clip_color_mean = [0.48145466, 0.4578275, 0.40821073] + clip_color_std = [0.26862954, 0.26130258, 0.27577711] + + # choose distribution + if dist == 'clip': + color_mean = clip_color_mean + color_std = clip_color_std + elif dist == 'mdetr': + color_mean = [0.485, 0.456, 0.406] + color_std = [0.229, 0.224, 0.225] + elif dist == 'franka': + color_mean = franka_color_mean + color_std = franka_color_std + else: + color_mean = transporter_color_mean + color_std = transporter_color_std + + if dist == 'franka': + depth_mean = franka_depth_mean + depth_std = franka_depth_std + else: + depth_mean = transporter_depth_mean + depth_std = transporter_depth_std + + # convert to pytorch tensor (if required) + if type(img) == torch.Tensor: + def cast_shape(stat, img): + tensor = torch.from_numpy(np.array(stat)).to(device=img.device, dtype=img.dtype) + tensor = tensor.unsqueeze(0).unsqueeze(-1).unsqueeze(-1) + tensor = tensor.repeat(img.shape[0], 1, img.shape[-2], img.shape[-1]) + return tensor + + color_mean = cast_shape(color_mean, img) + color_std = cast_shape(color_std, img) + depth_mean = cast_shape(depth_mean, img) + depth_std = cast_shape(depth_std, img) + + # normalize + img = img.clone() + img[:, :3, :, :] = ((img[:, :3, :, :] / 255 - color_mean) / color_std) + img[:, 3:, :, :] = ((img[:, 3:, :, :] - depth_mean) / depth_std) + else: + # normalize + img[:, :, :3] = (img[:, :, :3] / 255 - color_mean) / color_std + img[:, :, 3:] = (img[:, :, 3:] - depth_mean) / depth_std + + # if dist == 'franka' or dist == 'transporter': + # print(np.mean(img[:,:3,:,:].detach().cpu().numpy(), axis=(0,2,3)), + # np.mean(img[:,3,:,:].detach().cpu().numpy())) + + return img + +def map_kit_scale(scale): + return (scale[0] / 10, scale[1] / 10, scale[2] / 10) + +def deprocess(img): + color_mean = 0.18877631 + depth_mean = 0.00509261 + color_std = 0.07276466 + depth_std = 0.00903967 + + img[:, :, :3] = np.uint8(((img[:, :, :3] * color_std) + color_mean) * 255) + img[:, :, 3:] = np.uint8(((img[:, :, 3:] * depth_std) + depth_mean) * 255) + return img + + +def get_fused_heightmap(obs, configs, bounds, pix_size): + """Reconstruct orthographic heightmaps with segmentation masks.""" + heightmaps, colormaps = reconstruct_heightmaps( + obs['color'], obs['depth'], configs, bounds, pix_size) + colormaps = np.float32(colormaps) + heightmaps = np.float32(heightmaps) + + # Fuse maps from different views. + valid = np.sum(colormaps, axis=3) > 0 + repeat = np.sum(valid, axis=0) + repeat[repeat == 0] = 1 + cmap = np.sum(colormaps, axis=0) / repeat[Ellipsis, None] + cmap = np.uint8(np.round(cmap)) + hmap = np.max(heightmaps, axis=0) # Max to handle occlusions. + return cmap, hmap + + +def get_image_transform(theta, trans, pivot=(0, 0)): + """Compute composite 2D rigid transformation matrix.""" + # Get 2D rigid transformation matrix that rotates an image by theta (in + # radians) around pivot (in pixels) and translates by trans vector (in + # pixels) + pivot_t_image = np.array([[1., 0., -pivot[0]], [0., 1., -pivot[1]], + [0., 0., 1.]]) + image_t_pivot = np.array([[1., 0., pivot[0]], [0., 1., pivot[1]], + [0., 0., 1.]]) + transform = np.array([[np.cos(theta), -np.sin(theta), trans[0]], + [np.sin(theta), np.cos(theta), trans[1]], [0., 0., 1.]]) + return np.dot(image_t_pivot, np.dot(transform, pivot_t_image)) + + +def check_transform(image, pixel, transform): + """Valid transform only if pixel locations are still in FoV after transform.""" + new_pixel = np.flip( + np.int32( + np.round( + np.dot(transform, + np.float32([pixel[1], pixel[0], + 1.]).reshape(3, 1))))[:2].squeeze()) + valid = np.all( + new_pixel >= 0 + ) and new_pixel[0] < image.shape[0] and new_pixel[1] < image.shape[1] + return valid, new_pixel + + +def get_se3_from_image_transform(theta, trans, pivot, heightmap, bounds, + pixel_size): + """Calculate SE3 from image transform.""" + position_center = pix_to_xyz( + np.flip(np.int32(np.round(pivot))), + heightmap, + bounds, + pixel_size, + skip_height=False) + new_position_center = pix_to_xyz( + np.flip(np.int32(np.round(pivot + trans))), + heightmap, + bounds, + pixel_size, + skip_height=True) + # Don't look up the z height, it might get augmented out of frame + new_position_center = (new_position_center[0], new_position_center[1], + position_center[2]) + + delta_position = np.array(new_position_center) - np.array(position_center) + + t_world_center = np.eye(4) + t_world_center[0:3, 3] = np.array(position_center) + + t_centernew_center = np.eye(4) + euler_zxy = (-theta, 0, 0) + t_centernew_center[0:3, 0:3] = euler.euler2mat( + *euler_zxy, axes='szxy')[0:3, 0:3] + + t_centernew_center_tonly = np.eye(4) + t_centernew_center_tonly[0:3, 3] = -delta_position + t_centernew_center = t_centernew_center @ t_centernew_center_tonly + + t_world_centernew = t_world_center @ np.linalg.inv(t_centernew_center) + return t_world_center, t_world_centernew + + +def get_random_image_transform_params(image_size, theta_sigma=60): + theta = np.random.normal(0, np.deg2rad(theta_sigma)) + + trans_sigma = np.min(image_size) / 6 + trans = np.random.normal(0, trans_sigma, size=2) # [x, y] + pivot = (image_size[1] / 2, image_size[0] / 2) + return theta, trans, pivot + + +def q_mult(q1, q2): + w1, x1, y1, z1 = q1 + w2, x2, y2, z2 = q2 + w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2 + x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2 + y = w1 * y2 + y1 * w2 + z1 * x2 - x1 * z2 + z = w1 * z2 + z1 * w2 + x1 * y2 - y1 * x2 + return (w, x, y, z) + +def perturb(input_image, pixels, theta_sigma=60, add_noise=False): + """Data augmentation on images.""" + image_size = input_image.shape[:2] + + # Compute random rigid transform. + while True: + theta, trans, pivot = get_random_image_transform_params(image_size, theta_sigma=theta_sigma) + transform = get_image_transform(theta, trans, pivot) + transform_params = theta, trans, pivot + + # Ensure pixels remain in the image after transform. + is_valid = True + new_pixels = [] + new_rounded_pixels = [] + for pixel in pixels: + pixel = np.float32([pixel[1], pixel[0], 1.]).reshape(3, 1) + + rounded_pixel = np.int32(np.round(transform @ pixel))[:2].squeeze() + rounded_pixel = np.flip(rounded_pixel) + + pixel = (transform @ pixel)[:2].squeeze() + pixel = np.flip(pixel) + + in_fov_rounded = rounded_pixel[0] < image_size[0] and rounded_pixel[ + 1] < image_size[1] + in_fov = pixel[0] < image_size[0] and pixel[1] < image_size[1] + + is_valid = is_valid and np.all(rounded_pixel >= 0) and np.all( + pixel >= 0) and in_fov_rounded and in_fov + + new_pixels.append(pixel) + new_rounded_pixels.append(rounded_pixel) + if is_valid: + break + + # Apply rigid transform to image and pixel labels. + input_image = cv2.warpAffine( + input_image, + transform[:2, :], (image_size[1], image_size[0]), + flags=cv2.INTER_LINEAR) + + # Apply noise + color = np.int32(input_image[:,:,:3]) + depth = np.float32(input_image[:,:,3:]) + + if add_noise: + color += np.int32(np.random.normal(0, 3, image_size + (3,))) + color = np.uint8(np.clip(color, 0, 255)) + + depth += np.float32(np.random.normal(0, 0.003, image_size + (3,))) + + input_image = np.concatenate((color, depth), axis=2) + + # length of 5 + transform_params = np.array([theta, trans[0], trans[1], pivot[0], pivot[1]]) + return input_image, new_pixels, new_rounded_pixels, transform_params + + +def apply_perturbation(input_image, transform_params): + '''Apply data augmentation with specific transform params''' + image_size = input_image.shape[:2] + + # Apply rigid transform to image and pixel labels. + theta, trans, pivot = transform_params[0], transform_params[1:3], transform_params[3:5] + transform = get_image_transform(theta, trans, pivot) + + input_image = cv2.warpAffine( + input_image, + transform[:2, :], (image_size[1], image_size[0]), + flags=cv2.INTER_LINEAR) + return input_image + + +class ImageRotator: + """Rotate for n rotations.""" + # Reference: https://kornia.readthedocs.io/en/latest/tutorials/warp_affine.html?highlight=rotate + + def __init__(self, n_rotations): + self.angles = [] + for i in range(n_rotations): + theta = i * 2 * 180 / n_rotations + self.angles.append(theta) + + def __call__(self, x_list, pivot, reverse=False): + rot_x_list = [] + for i, angle in enumerate(self.angles): + x = x_list[i]# .unsqueeze(0) + # create transformation (rotation) + size = len(x) + alpha = angle if not reverse else (-1.0 * angle) # in degrees + angle = torch.ones(size) * alpha + + # define the rotation center + if type(pivot) is not torch.Tensor: + center = torch.FloatTensor(pivot)[...,[1,0]] + center = center.view(1,-1).repeat((size,1)) + else: + center = pivot[...,[1,0]].view(1,-1).clone().to(angle.device) + # center: torch.tensor = torch.ones(size, 2) + # center[..., 0] = int(pivot[1]) + # center[..., 1] = int(pivot[0]) + + # define the scale factor + scale = torch.ones(size, 2) + + # # compute the transformation matrix + M = kornia.geometry.get_rotation_matrix2d(center, angle, scale) + # x_warped = torchvision.transforms.functional.affine(x.float(), scale=1., + # center=[int(pivot[1]),int(pivot[0])], + # angle=alpha, translate=[0,0], shear=0, + # interpolation= torchvision.transforms.InterpolationMode.BILINEAR) + + + # apply the transformation to original image + # M = M.repeat(len(x), 1, 1) + _, _, h, w = x.shape + x_warped = kornia.geometry.transform.warp_affine(x.float(), M.to(x.device), dsize=(h, w)) + x_warped = x_warped + rot_x_list.append(x_warped) + + return rot_x_list + +# KD Tree Utils +# Construct K-D Tree to roughly estimate how many objects can fit inside the box. +class TreeNode: + + def __init__(self, parent, children, bbox): + self.parent = parent + self.children = children + self.bbox = bbox # min x, min y, min z, max x, max y, max z + +def KDTree(node, min_object_dim, margin, bboxes): + size = node.bbox[3:] - node.bbox[:3] + + # Choose which axis to split. + split = size > 2 * min_object_dim + if np.sum(split) == 0: + bboxes.append(node.bbox) + return + split = np.float32(split) / np.sum(split) + split_axis = np.random.choice(range(len(split)), 1, p=split)[0] + + # Split along chosen axis and create 2 children + cut_ind = np.random.rand() * \ + (size[split_axis] - 2 * min_object_dim) + \ + node.bbox[split_axis] + min_object_dim + child1_bbox = node.bbox.copy() + child1_bbox[3 + split_axis] = cut_ind - margin / 2. + child2_bbox = node.bbox.copy() + child2_bbox[split_axis] = cut_ind + margin / 2. + node.children = [ + TreeNode(node, [], bbox=child1_bbox), + TreeNode(node, [], bbox=child2_bbox) + ] + KDTree(node.children[0], min_object_dim, margin, bboxes) + KDTree(node.children[1], min_object_dim, margin, bboxes) + +# ----------------------------------------------------------------------------- +# Shape Name UTILS +# ----------------------------------------------------------------------------- +google_seen_obj_shapes = { + 'train': [ + 'alarm clock', + 'android toy', + 'black boot with leopard print', + 'black fedora', + 'black razer mouse', + 'black sandal', + 'black shoe with orange stripes', + 'bull figure', + 'butterfinger chocolate', + 'c clamp', + 'can opener', + 'crayon box', + 'dog statue', + 'frypan', + 'green and white striped towel', + 'grey soccer shoe with cleats', + 'hard drive', + 'honey dipper', + 'magnifying glass', + 'mario figure', + 'nintendo 3ds', + 'nintendo cartridge', + 'office depot box', + 'orca plush toy', + 'pepsi gold caffeine free box', + 'pepsi wild cherry box', + 'porcelain cup', + 'purple tape', + 'red and white flashlight', + 'rhino figure', + 'rocket racoon figure', + 'scissors', + 'silver tape', + 'spatula with purple head', + 'spiderman figure', + 'tablet', + 'toy school bus', + ], + 'val': [ + 'ball puzzle', + 'black and blue sneakers', + 'black shoe with green stripes', + 'brown fedora', + 'dinosaur figure', + 'hammer', + 'light brown boot with golden laces', + 'lion figure', + 'pepsi max box', + 'pepsi next box', + 'porcelain salad plate', + 'porcelain spoon', + 'red and white striped towel', + 'red cup', + 'screwdriver', + 'toy train', + 'unicorn toy', + 'white razer mouse', + 'yoshi figure' + ], + 'test': [ + 'ball puzzle', + 'black and blue sneakers', + 'black shoe with green stripes', + 'brown fedora', + 'dinosaur figure', + 'hammer', + 'light brown boot with golden laces', + 'lion figure', + 'pepsi max box', + 'pepsi next box', + 'porcelain salad plate', + 'porcelain spoon', + 'red and white striped towel', + 'red cup', + 'screwdriver', + 'toy train', + 'unicorn toy', + 'white razer mouse', + 'yoshi figure' + ], + } + +google_unseen_obj_shapes = { + 'train': [ + 'alarm clock', + 'android toy', + 'black boot with leopard print', + 'black fedora', + 'black razer mouse', + 'black sandal', + 'black shoe with orange stripes', + 'bull figure', + 'butterfinger chocolate', + 'c clamp', + 'can opener', + 'crayon box', + 'dog statue', + 'frypan', + 'green and white striped towel', + 'grey soccer shoe with cleats', + 'hard drive', + 'honey dipper', + 'magnifying glass', + 'mario figure', + 'nintendo 3ds', + 'nintendo cartridge', + 'office depot box', + 'orca plush toy', + 'pepsi gold caffeine free box', + 'pepsi wild cherry box', + 'porcelain cup', + 'purple tape', + 'red and white flashlight', + 'rhino figure', + 'rocket racoon figure', + 'scissors', + 'silver tape', + 'spatula with purple head', + 'spiderman figure', + 'tablet', + 'toy school bus', + ], + 'val': [ + 'ball puzzle', + 'black and blue sneakers', + 'black shoe with green stripes', + 'brown fedora', + 'dinosaur figure', + 'hammer', + 'light brown boot with golden laces', + 'lion figure', + 'pepsi max box', + 'pepsi next box', + 'porcelain salad plate', + 'porcelain spoon', + 'red and white striped towel', + 'red cup', + 'screwdriver', + 'toy train', + 'unicorn toy', + 'white razer mouse', + 'yoshi figure' + ], + 'test': [ + 'ball puzzle', + 'black and blue sneakers', + 'black shoe with green stripes', + 'brown fedora', + 'dinosaur figure', + 'hammer', + 'light brown boot with golden laces', + 'lion figure', + 'pepsi max box', + 'pepsi next box', + 'porcelain salad plate', + 'porcelain spoon', + 'red and white striped towel', + 'red cup', + 'screwdriver', + 'toy train', + 'unicorn toy', + 'white razer mouse', + 'yoshi figure' + ], + } + +google_all_shapes = { + 'train': [ + 'alarm clock', + 'android toy', + 'ball puzzle', + 'black and blue sneakers', + 'black boot with leopard print', + 'black fedora', + 'black razer mouse', + 'black sandal', + 'black shoe with green stripes', + 'black shoe with orange stripes', + 'brown fedora', + 'bull figure', + 'butterfinger chocolate', + 'c clamp', + 'can opener', + 'crayon box', + 'dinosaur figure', + 'dog statue', + 'frypan', + 'green and white striped towel', + 'grey soccer shoe with cleats', + 'hammer', + 'hard drive', + 'honey dipper', + 'light brown boot with golden laces', + 'lion figure', + 'magnifying glass', + 'mario figure', + 'nintendo 3ds', + 'nintendo cartridge', + 'office depot box', + 'orca plush toy', + 'pepsi gold caffeine free box', + 'pepsi max box', + 'pepsi next box', + 'pepsi wild cherry box', + 'porcelain cup', + 'porcelain salad plate', + 'porcelain spoon', + 'purple tape', + 'red and white flashlight', + 'red and white striped towel', + 'red cup', + 'rhino figure', + 'rocket racoon figure', + 'scissors', + 'screwdriver', + 'silver tape', + 'spatula with purple head', + 'spiderman figure', + 'tablet', + 'toy school bus', + 'toy train', + 'unicorn toy', + 'white razer mouse', + 'yoshi figure', + ], + 'val': [ + 'alarm clock', + 'android toy', + 'ball puzzle', + 'black and blue sneakers', + 'black boot with leopard print', + 'black fedora', + 'black razer mouse', + 'black sandal', + 'black shoe with green stripes', + 'black shoe with orange stripes', + 'brown fedora', + 'bull figure', + 'butterfinger chocolate', + 'c clamp', + 'can opener', + 'crayon box', + 'dinosaur figure', + 'dog statue', + 'frypan', + 'green and white striped towel', + 'grey soccer shoe with cleats', + 'hammer', + 'hard drive', + 'honey dipper', + 'light brown boot with golden laces', + 'lion figure', + 'magnifying glass', + 'mario figure', + 'nintendo 3ds', + 'nintendo cartridge', + 'office depot box', + 'orca plush toy', + 'pepsi gold caffeine free box', + 'pepsi max box', + 'pepsi next box', + 'pepsi wild cherry box', + 'porcelain cup', + 'porcelain salad plate', + 'porcelain spoon', + 'purple tape', + 'red and white flashlight', + 'red and white striped towel', + 'red cup', + 'rhino figure', + 'rocket racoon figure', + 'scissors', + 'screwdriver', + 'silver tape', + 'spatula with purple head', + 'spiderman figure', + 'tablet', + 'toy school bus', + 'toy train', + 'unicorn toy', + 'white razer mouse', + 'yoshi figure', + ], + 'test': [ + 'alarm clock', + 'android toy', + 'ball puzzle', + 'black and blue sneakers', + 'black boot with leopard print', + 'black fedora', + 'black razer mouse', + 'black sandal', + 'black shoe with green stripes', + 'black shoe with orange stripes', + 'brown fedora', + 'bull figure', + 'butterfinger chocolate', + 'c clamp', + 'can opener', + 'crayon box', + 'dinosaur figure', + 'dog statue', + 'frypan', + 'green and white striped towel', + 'grey soccer shoe with cleats', + 'hammer', + 'hard drive', + 'honey dipper', + 'light brown boot with golden laces', + 'lion figure', + 'magnifying glass', + 'mario figure', + 'nintendo 3ds', + 'nintendo cartridge', + 'office depot box', + 'orca plush toy', + 'pepsi gold caffeine free box', + 'pepsi max box', + 'pepsi next box', + 'pepsi wild cherry box', + 'porcelain cup', + 'porcelain salad plate', + 'porcelain spoon', + 'purple tape', + 'red and white flashlight', + 'red and white striped towel', + 'red cup', + 'rhino figure', + 'rocket racoon figure', + 'scissors', + 'screwdriver', + 'silver tape', + 'spatula with purple head', + 'spiderman figure', + 'tablet', + 'toy school bus', + 'toy train', + 'unicorn toy', + 'white razer mouse', + 'yoshi figure', + ], + } +assembling_kit_shapes = { + 0: "letter R shape", + 1: "letter A shape", + 2: "triangle", + 3: "square", + 4: "plus", + 5: "letter T shape", + 6: "diamond", + 7: "pentagon", + 8: "rectangle", + 9: "flower", + 10: "star", + 11: "circle", + 12: "letter G shape", + 13: "letter V shape", + 14: "letter E shape", + 15: "letter L shape", + 16: "ring", + 17: "hexagon", + 18: "heart", + 19: "letter M shape", + } + +# ----------------------------------------------------------------------------- +# COLOR AND PLOT UTILS +# ----------------------------------------------------------------------------- + + +# Colors (Tableau palette). +COLORS = { + 'blue': [78.0 / 255.0, 121.0 / 255.0, 167.0 / 255.0], + 'red': [255.0 / 255.0, 087.0 / 255.0, 089.0 / 255.0], + 'green': [089.0 / 255.0, 169.0 / 255.0, 078.0 / 255.0], + 'orange': [242.0 / 255.0, 142.0 / 255.0, 043.0 / 255.0], + 'yellow': [237.0 / 255.0, 201.0 / 255.0, 072.0 / 255.0], + 'purple': [176.0 / 255.0, 122.0 / 255.0, 161.0 / 255.0], + 'pink': [255.0 / 255.0, 157.0 / 255.0, 167.0 / 255.0], + 'cyan': [118.0 / 255.0, 183.0 / 255.0, 178.0 / 255.0], + 'brown': [156.0 / 255.0, 117.0 / 255.0, 095.0 / 255.0], + 'white': [255.0 / 255.0, 255.0 / 255.0, 255.0 / 255.0], + 'gray': [186.0 / 255.0, 176.0 / 255.0, 172.0 / 255.0], + 'indigo': [75.0 / 255.0, 0.0 / 255.0, 130.0 / 255.0], + 'violet': [143.0 / 255.0, 0.0 / 255.0, 255.0 / 255.0], + 'black': [0.0 / 255.0, 0.0 / 255.0, 0.0 / 255.0], + 'silver': [192.0 / 255.0, 192.0 / 255.0, 192.0 / 255.0], + 'gold': [255.0 / 255.0, 215.0 / 255.0, 0.0 / 255.0], + +} + +COLORS_NAMES = list(COLORS.keys()) +TRAIN_COLORS = ['blue', 'red', 'green', 'yellow', 'brown', 'gray', 'cyan'] +EVAL_COLORS = ['blue', 'red', 'green', 'orange', 'purple', 'pink', 'white'] + + +def get_colors(mode, n_colors=-1, **kwargs): + all_color_names = get_colors_names(mode) + + if n_colors == -1: + all_color_names = all_color_names + else: + all_color_names = random.sample(all_color_names, n_colors) + return [COLORS[cn] for cn in all_color_names], all_color_names + +def get_colors_names(mode): + if mode == 'train': + return TRAIN_COLORS + elif mode == 'full': + return TRAIN_COLORS + else: + return TRAIN_COLORS + +def get_random_color(): + return get_colors(mode='train', n_colors=1) + +def solve_hanoi_all(n_disks): + # Solve Hanoi sequence with dynamic programming. + hanoi_steps = [] # [[object index, from rod, to rod], ...] + + def solve_hanoi(n, t0, t1, t2): + if n == 0: + hanoi_steps.append([n, t0, t1]) + return + solve_hanoi(n - 1, t0, t2, t1) + hanoi_steps.append([n, t0, t1]) + solve_hanoi(n - 1, t2, t1, t0) + + solve_hanoi(n_disks - 1, 0, 2, 1) + return hanoi_steps + +def plot(fname, # pylint: disable=dangerous-default-value + title, + ylabel, + xlabel, + data, + xlim=[-np.inf, 0], + xticks=None, + ylim=[np.inf, -np.inf], + show_std=True): + """Plot frame data.""" + # Data is a dictionary that maps experiment names to tuples with 3 + # elements: x (size N array) and y (size N array) and y_std (size N array) + + # Get data limits. + for name, (x, y, _) in data.items(): + del name + y = np.array(y) + xlim[0] = max(xlim[0], np.min(x)) + xlim[1] = max(xlim[1], np.max(x)) + ylim[0] = min(ylim[0], np.min(y)) + ylim[1] = max(ylim[1], np.max(y)) + + # Draw background. + plt.title(title, fontsize=14) + plt.ylim(ylim) + plt.ylabel(ylabel, fontsize=14) + plt.yticks(fontsize=14) + plt.xlim(xlim) + plt.xlabel(xlabel, fontsize=14) + plt.grid(True, linestyle='-', color=[0.8, 0.8, 0.8]) + ax = plt.gca() + for axis in ['top', 'bottom', 'left', 'right']: + ax.spines[axis].set_color('#000000') + plt.rcParams.update({'font.size': 14}) + plt.rcParams['mathtext.default'] = 'regular' + matplotlib.rcParams['pdf.fonttype'] = 42 + matplotlib.rcParams['ps.fonttype'] = 42 + + # Draw data. + color_iter = 0 + for name, (x, y, std) in data.items(): + del name + x, y, std = np.float32(x), np.float32(y), np.float32(std) + upper = np.clip(y + std, ylim[0], ylim[1]) + lower = np.clip(y - std, ylim[0], ylim[1]) + color = COLORS[list(COLORS.keys())[color_iter]] + if show_std: + plt.fill_between(x, upper, lower, color=color, linewidth=0, alpha=0.3) + plt.plot(x, y, color=color, linewidth=2, marker='o', alpha=1.) + color_iter += 1 + + if xticks: + plt.xticks(ticks=range(len(xticks)), labels=xticks, fontsize=14) + else: + plt.xticks(fontsize=14) + plt.legend([name for name, _ in data.items()], + loc='lower right', fontsize=14) + plt.tight_layout() + plt.savefig(fname) + plt.clf() + + +# ----------------------------------------------------------------------------- +# MESHCAT UTILS +# ----------------------------------------------------------------------------- + +def create_visualizer(clear=True): + print('Waiting for meshcat server... have you started a server?') + vis = meshcat.Visualizer(zmq_url='tcp://127.0.0.1:6000') + if clear: + vis.delete() + return vis + + +def make_frame(vis, name, h, radius, o=1.0): + """Add a red-green-blue triad to the Meschat visualizer. + + Args: + vis (MeshCat Visualizer): the visualizer + name (string): name for this frame (should be unique) + h (float): height of frame visualization + radius (float): radius of frame visualization + o (float): opacity + """ + vis[name]['x'].set_object( + g.Cylinder(height=h, radius=radius), + g.MeshLambertMaterial(color=0xff0000, reflectivity=0.8, opacity=o)) + rotate_x = mtf.rotation_matrix(np.pi / 2.0, [0, 0, 1]) + rotate_x[0, 3] = h / 2 + vis[name]['x'].set_transform(rotate_x) + + vis[name]['y'].set_object( + g.Cylinder(height=h, radius=radius), + g.MeshLambertMaterial(color=0x00ff00, reflectivity=0.8, opacity=o)) + rotate_y = mtf.rotation_matrix(np.pi / 2.0, [0, 1, 0]) + rotate_y[1, 3] = h / 2 + vis[name]['y'].set_transform(rotate_y) + + vis[name]['z'].set_object( + g.Cylinder(height=h, radius=radius), + g.MeshLambertMaterial(color=0x0000ff, reflectivity=0.8, opacity=o)) + rotate_z = mtf.rotation_matrix(np.pi / 2.0, [1, 0, 0]) + rotate_z[2, 3] = h / 2 + vis[name]['z'].set_transform(rotate_z) + + +def meshcat_visualize(vis, obs, act, info): + """Visualize data using meshcat.""" + + for key in sorted(info.keys()): + pose = info[key] + pick_transform = np.eye(4) + pick_transform[0:3, 3] = pose[0] + quaternion_wxyz = np.asarray( + [pose[1][3], pose[1][0], pose[1][1], pose[1][2]]) + pick_transform[0:3, 0:3] = mtf.quaternion_matrix(quaternion_wxyz)[0:3, 0:3] + label = 'obj_' + str(key) + make_frame(vis, label, h=0.05, radius=0.0012, o=1.0) + vis[label].set_transform(pick_transform) + + for cam_index in range(len(act['camera_config'])): + verts = unproject_depth_vectorized( + obs['depth'][cam_index], np.array([0, 1]), + np.array(act['camera_config'][cam_index]['intrinsics']).reshape(3, 3), + np.zeros(5)) + + # switch from [N,3] to [3,N] + verts = verts.T + + cam_transform = np.eye(4) + cam_transform[0:3, 3] = act['camera_config'][cam_index]['position'] + quaternion_xyzw = act['camera_config'][cam_index]['rotation'] + quaternion_wxyz = np.asarray([ + quaternion_xyzw[3], quaternion_xyzw[0], quaternion_xyzw[1], + quaternion_xyzw[2] + ]) + cam_transform[0:3, 0:3] = mtf.quaternion_matrix(quaternion_wxyz)[0:3, 0:3] + verts = apply_transform(cam_transform, verts) + + colors = obs['color'][cam_index].reshape(-1, 3).T / 255.0 + + vis['pointclouds/' + str(cam_index)].set_object( + g.PointCloud(position=verts, color=colors)) + + +# ----------------------------------------------------------------------------- +# CONFIG UTILS +# ----------------------------------------------------------------------------- + +def set_seed(seed, torch=False): + random.seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) + np.random.seed(seed) + + if torch: + import torch + torch.manual_seed(seed) + + +def load_cfg(yaml_path): + with open(yaml_path, 'r') as f: + data = yaml.safe_load(f) + return data + + +def load_hydra_config(config_path): + return OmegaConf.load(config_path) diff --git a/gensim/__init__.py b/gensim/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gensim/agent.py b/gensim/agent.py new file mode 100644 index 0000000000000000000000000000000000000000..b96f56f89717c6d28d93468111178dd2f0af1d7f --- /dev/null +++ b/gensim/agent.py @@ -0,0 +1,162 @@ +import numpy as np +import os +import IPython +import random +import json +import traceback +import pybullet as p +from gensim.utils import ( + save_text, + add_to_txt, + extract_code, + extract_dict, + extract_list, + extract_assets, + format_dict_prompt, + sample_list_reference, + generate_feedback, +) + + +class Agent: + """ + class that design new tasks and codes for simulation environments + """ + def __init__(self, cfg, memory): + self.cfg = cfg + self.model_output_dir = cfg["model_output_dir"] + self.prompt_folder = f"prompts/{cfg['prompt_folder']}" + self.memory = memory + self.chat_log = memory.chat_log + self.use_template = cfg['use_template'] + + def propose_task(self, proposed_task_names): + """Language descriptions for the task""" + add_to_txt(self.chat_log, "================= Task and Asset Design!", with_print=True) + + if self.use_template: + task_prompt_text = open(f"{self.prompt_folder}/cliport_prompt_task.txt").read() + task_asset_replacement_str = format_dict_prompt(self.memory.online_asset_buffer, self.cfg['task_asset_candidate_num']) + task_prompt_text = task_prompt_text.replace("TASK_ASSET_PROMPT", task_asset_replacement_str) + + task_desc_replacement_str = format_dict_prompt(self.memory.online_task_buffer, self.cfg['task_description_candidate_num']) + print("prompt task description candidates:") + print(task_desc_replacement_str) + task_prompt_text = task_prompt_text.replace("TASK_DESCRIPTION_PROMPT", task_desc_replacement_str) + + if len(self.cfg['target_task_name']) > 0: + task_prompt_text = task_prompt_text.replace("TARGET_TASK_NAME", self.cfg['target_task_name']) + + # print("Template Task PROMPT: ", task_prompt_text) + else: + task_prompt_text = open(f"{self.prompt_folder}/cliport_prompt_task.txt").read() + + # maximum number + print("online_task_buffer size:", len(self.memory.online_task_buffer)) + total_tasks = self.memory.online_task_buffer + + MAX_NUM = 10 + if len(total_tasks) > MAX_NUM: + total_tasks = dict(random.sample(total_tasks.items(), MAX_NUM)) + + task_prompt_text = task_prompt_text.replace("PAST_TASKNAME_TEMPLATE", format_dict_prompt(total_tasks)) + + res = generate_feedback( + task_prompt_text, + temperature=self.cfg["gpt_temperature"], + interaction_txt=self.chat_log, + ) + + # Extract dictionary for task name, descriptions, and assets + task_def = extract_dict(res, prefix="new_task") + try: + exec(task_def, globals()) + self.new_task = new_task + return new_task + except: + self.new_task = {"task-name": "dummy", "assets-used": [], "task_descriptions": ""} + print(str(traceback.format_exc())) + return self.new_task + + def propose_assets(self): + """Asset Generation. Not used for now.""" + if os.path.exists(f"{self.prompt_folder}/cliport_prompt_asset_template.txt"): + add_to_txt(self.chat_log, "================= Asset Generation!", with_print=True) + asset_prompt_text = open(f"{self.prompt_folder}/cliport_prompt_asset_template.txt").read() + + if self.use_template: + asset_prompt_text = asset_prompt_text.replace("TASK_NAME_TEMPLATE", self.new_task["task-name"]) + asset_prompt_text = asset_prompt_text.replace("ASSET_STRING_TEMPLATE", str(self.new_task["assets-used"])) + print("Template Asset PROMPT: ", asset_prompt_text) + + res = generate_feedback(asset_prompt_text, temperature=0, interaction_txt=self.chat_log) + print("Save asset to:", self.model_output_dir, task_name + "_asset_output") + save_text(self.model_output_dir, f'{self.new_task["task-name"]}_asset_output', res) + asset_list = extract_assets(res) + # save_urdf(asset_list) + else: + asset_list = {} + return asset_list + + def api_review(self): + """review the task api""" + if os.path.exists(f"{self.prompt_folder}/cliport_prompt_api_template.txt"): + add_to_txt( + self.chat_log, "================= API Preview!", with_print=True) + api_prompt_text = open( + f"{self.prompt_folder}/cliport_prompt_api_template.txt").read() + if "task-name" in self.new_task: + api_prompt_text = api_prompt_text.replace("TASK_NAME_TEMPLATE", self.new_task["task-name"]) + api_prompt_text = api_prompt_text.replace("TASK_STRING_TEMPLATE", str(self.new_task)) + + res = generate_feedback( + api_prompt_text, temperature=0, interaction_txt=self.chat_log) + + def template_reference_prompt(self): + """ select which code reference to reference """ + if os.path.exists(f"{self.prompt_folder}/cliport_prompt_code_reference_selection_template.txt"): + self.chat_log = add_to_txt(self.chat_log, "================= Code Reference!", with_print=True) + code_reference_question = open(f'{self.prompt_folder}/cliport_prompt_code_reference_selection_template.txt').read() + code_reference_question = code_reference_question.replace("TASK_NAME_TEMPLATE", self.new_task["task-name"]) + code_reference_question = code_reference_question.replace("TASK_CODE_LIST_TEMPLATE", str(list(self.memory.online_code_buffer.keys()))) + + code_reference_question = code_reference_question.replace("TASK_STRING_TEMPLATE", str(self.new_task)) + res = generate_feedback(code_reference_question, temperature=0., interaction_txt=self.chat_log) + code_reference_cmd = extract_list(res, prefix='code_reference') + exec(code_reference_cmd, globals()) + task_code_reference_replace_prompt = '' + for key in code_reference: + if key in self.memory.online_code_buffer: + task_code_reference_replace_prompt += f'```\n{self.memory.online_code_buffer[key]}\n```\n\n' + else: + print("missing task reference code:", key) + else: + task_code_reference_replace_prompt = sample_list_reference(base_task_codes, sample_num=cfg['task_code_candidate_num']) + # print("Template Reference Code PROMPT: ", task_code_reference_replace_prompt) + + return task_code_reference_replace_prompt + + def implement_task(self): + """Generate Code for the task""" + code_prompt_text = open(f"{self.prompt_folder}/cliport_prompt_code_split_template.txt").read() + code_prompt_text = code_prompt_text.replace("TASK_NAME_TEMPLATE", self.new_task["task-name"]) + + if self.use_template or os.path.exists(f"{self.prompt_folder}/cliport_prompt_code_reference_selection_template.txt"): + task_code_reference_replace_prompt = self.template_reference_prompt() + code_prompt_text = code_prompt_text.replace("TASK_CODE_REFERENCE_TEMPLATE", task_code_reference_replace_prompt) + + elif os.path.exists(f"{self.prompt_folder}/cliport_prompt_code_split_template.txt"): + self.chat_log = add_to_txt(self.chat_log, "================= Code Generation!", with_print=True) + code_prompt_text = code_prompt_text.replace("TASK_STRING_TEMPLATE", str(self.new_task)) + + res = generate_feedback( + code_prompt_text, temperature=0, interaction_txt=self.chat_log) + code, task_name = extract_code(res) + print("Save code to:", self.model_output_dir, task_name + "_code_output") + save_text(self.model_output_dir, task_name + "_code_output", code) + + if len(task_name) == 0: + print("empty task name:", task_name) + return None + + return code, task_name diff --git a/gensim/critic.py b/gensim/critic.py new file mode 100644 index 0000000000000000000000000000000000000000..8cde0240552141276d6dc71f1e726db6a56948eb --- /dev/null +++ b/gensim/critic.py @@ -0,0 +1,85 @@ +import numpy as np +import os +import IPython + +import traceback +import json +from gensim.utils import ( + save_text, + add_to_txt, + extract_dict, + format_dict_prompt, + generate_feedback, +) +import copy +import random + +class Critic: + """ + class that reflects and criticizes new task for improvement + """ + def __init__(self, cfg, memory): + self.prompt_folder = f"prompts/{cfg['prompt_folder']}" + self.memory = memory + self.chat_log = self.memory.chat_log + self.cfg = cfg + self.model_output_dir = cfg["model_output_dir"] + + def error_review(self, new_task): + """ commonly made error review """ + if os.path.exists(f"{self.prompt_folder}/cliport_prompt_common_errors_template.txt") and "task-name" in new_task: + self.chat_log = add_to_txt(self.chat_log, "================= Error Book Preview!", with_print=True) + errorbook_prompt_text = open(f'{self.prompt_folder}/cliport_prompt_common_errors_template.txt').read() + errorbook_prompt_text = errorbook_prompt_text.replace("TASK_NAME_TEMPLATE", new_task["task-name"]) + res = generate_feedback(errorbook_prompt_text, temperature=0., interaction_txt=self.chat_log) # cfg['gpt_temperature'] + + def reflection(self, new_task, new_code, current_tasks=None): + """ reflect on if the new task needs to be added """ + all_add_to_the_task_list_flag = True + + if os.path.exists(f"{self.prompt_folder}/cliport_prompt_task_reflection.txt"): + # only consider successful task + self.chat_log = add_to_txt(self.chat_log, "================= Code Reflect!", with_print=True) + total_tasks = copy.deepcopy(self.memory.online_task_buffer) + if current_tasks is not None: + # adding all the tasks in the current run. at least should not overlap with those + for t in current_tasks: + total_tasks[t['task-name']] = t + + # need to load more + total_tasks = self.memory.online_task_buffer + MAX_NUM = 40 + if len(total_tasks) > MAX_NUM: + total_tasks = dict(random.sample(total_tasks.items(), MAX_NUM)) + + print("reflection history task num:", len(total_tasks)) + task_descriptions_replacement_str = format_dict_prompt(total_tasks, -1) + + # append current new task + code_reflection_prompt_text = open(f"{self.prompt_folder}/cliport_prompt_task_reflection.txt").read() + code_reflection_prompt_text = code_reflection_prompt_text.replace("CURRENT_TASK_NAME_TEMPLATE", str(task_descriptions_replacement_str)) + code_reflection_prompt_text = code_reflection_prompt_text.replace("TASK_STRING_TEMPLATE", str(new_task)) + code_reflection_prompt_text = code_reflection_prompt_text.replace("TASK_CODE_TEMPLATE", str(new_code)) + if len(self.cfg['target_task_name']) > 0: + code_reflection_prompt_text = code_reflection_prompt_text.replace("TARGET_TASK_NAME", self.cfg['target_task_name']) + + # no matter + total_tasks[new_task["task-name"].replace("-", "_")] = str(new_task) + res = generate_feedback(code_reflection_prompt_text, temperature=0.4, interaction_txt=self.chat_log, n=int(self.cfg['reflection_agreement_num'])) # cfg['gpt_temperature'] + all_add_to_the_task_list_flag = True + + for idx, r in enumerate(res): + # iterate through for agreement + reflection_def_cmd = extract_dict(r, prefix='task_reflection') + exec(reflection_def_cmd, globals()) + try: + print(f"critic {idx}:", task_reflection) + + if task_reflection["add_to_the_task_list"] == 'False': + all_add_to_the_task_list_flag = False + print(f"critic {idx} suggests not adding this task to the buffer! ") + except: + IPython.embed() + save_text(self.model_output_dir, new_task['task-name'] + "_reflection_output", str(task_reflection)) + + return all_add_to_the_task_list_flag \ No newline at end of file diff --git a/gensim/evaluate_finetune_model.py b/gensim/evaluate_finetune_model.py new file mode 100644 index 0000000000000000000000000000000000000000..0f301476443f740b9311a4d208b35ce0265b003d --- /dev/null +++ b/gensim/evaluate_finetune_model.py @@ -0,0 +1,78 @@ +import openai +import argparse +import os +from cliport import tasks +from cliport.dataset import RavensDataset +from cliport.environments.environment import Environment + +from pygments import highlight +from pygments.lexers import PythonLexer +from pygments.formatters import TerminalFormatter + +import time +import random +import json +import traceback +import pybullet as p +import IPython +from gensim.topdown_sim_runner import TopDownSimulationRunner +import hydra +from datetime import datetime + +from gensim.memory import Memory +from gensim.utils import set_gpt_model, clear_messages, format_finetune_prompt + +@hydra.main(config_path='../cliport/cfg', config_name='data', version_base="1.2") +def main(cfg): + # parser.add_argument("--task", type=str, default='build-car') + # parser.add_argument("--model", type=str, default='davinci:ft-wang-lab:gensim-2023-08-04-18-28-34') + + task = cfg.target_task + model = cfg.target_model + prompt = format_finetune_prompt(task) + + openai.api_key = cfg['openai_key'] + model_time = datetime.now().strftime("%d_%m_%Y_%H:%M:%S") + cfg['model_output_dir'] = os.path.join(cfg['output_folder'], cfg['prompt_folder'] + "_" + cfg.target_model) + if 'seed' in cfg: + cfg['model_output_dir'] = cfg['model_output_dir'] + f"_{cfg['seed']}" + + set_gpt_model(cfg['gpt_model']) + memory = Memory(cfg) + simulation_runner = TopDownSimulationRunner(cfg, memory) + + for trial_i in range(cfg['trials']): + if 'new_finetuned_model' in cfg or 'gpt-3.5-turbo' in cfg.target_model: + # the chat completion version + response = openai.ChatCompletion.create( + model=model, + messages=[{"role": "system", "content": "You are an AI in robot simulation code and task design."}, + {"role": "user", "content": prompt}], + temperature=0.01, + max_tokens=1000, + n=1, + stop=["\n```\n"]) + res = response["choices"][0]["message"]["content"] + else: + response = openai.Completion.create( + model=model, + prompt=prompt, + temperature=0, + max_tokens=1800, + stop=["\n```\n"]) + res = response["choices"][0]["text"] + + simulation_runner.task_creation(res) + simulation_runner.simulate_task() + simulation_runner.print_current_stats() + + simulation_runner.save_stats() + + + + +# load few shot prompts + + +if __name__ == "__main__": + main() diff --git a/gensim/memory.py b/gensim/memory.py new file mode 100644 index 0000000000000000000000000000000000000000..8b03a2b75330065acaa2d261ddb8f20c4b46ef7f --- /dev/null +++ b/gensim/memory.py @@ -0,0 +1,132 @@ +import numpy as np +import os +import IPython + +import random +import json +from gensim.utils import save_text + + +class Memory: + """ + class that maintains a buffer of generated tasks and codes + """ + def __init__(self, cfg): + self.prompt_folder = f"prompts/{cfg['prompt_folder']}" + self.data_path = cfg["prompt_data_path"] + self.cfg = cfg + + # a chat history is a list of strings + self.chat_log = [] + self.online_task_buffer = {} + self.online_code_buffer = {} + self.online_asset_buffer = {} + + # directly load current offline memory into online memory + base_tasks, base_assets, base_task_codes = self.load_offline_memory() + self.online_task_buffer.update(base_tasks) + self.online_asset_buffer.update(base_assets) + + # load each code file + for task_file in base_task_codes: + # the original cliport task path + if os.path.exists("cliport/tasks/" + task_file): + self.online_code_buffer[task_file] = open("cliport/tasks/" + task_file).read() + + # the generated cliport task path + elif os.path.exists("cliport/generated_tasks/" + task_file): + self.online_code_buffer[task_file] = open("cliport/generated_tasks/" + task_file).read() + + print(f"load {len(self.online_code_buffer)} tasks for memory from offline to online:") + cache_embedding_path = "outputs/task_cache_embedding.npz" + + if os.path.exists(cache_embedding_path): + print("task code embeding:", cache_embedding_path) + self.task_code_embedding = np.load(cache_embedding_path) + + def save_run(self, new_task): + """save chat history and potentially save base memory""" + print("save all interaction to :", f'{new_task["task-name"]}_full_output') + unroll_chatlog = '' + for chat in self.chat_log: + unroll_chatlog += chat + save_text( + self.cfg['model_output_dir'], f'{new_task["task-name"]}_full_output', unroll_chatlog + ) + + def save_task_to_online(self, new_task, code): + """(not dumping the task offline). save the task information for online bootstrapping.""" + self.online_task_buffer[new_task['task-name']] = new_task + code_file_name = new_task["task-name"].replace("-", "_") + ".py" + + # code file name: actual code in contrast to offline code files format. + self.online_code_buffer[code_file_name] = code + + def save_task_to_offline(self, new_task, code, generate_task_path='generated_tasks'): + """save the current task descriptions, assets, and code, if it passes reflection and environment test""" + generated_task_code_path = os.path.join( + self.cfg["prompt_data_path"], f"{generate_task_path}_codes.json" + ) + generated_task_codes = json.load(open(generated_task_code_path)) + new_file_path = new_task["task-name"].replace("-", "_") + ".py" + + if new_file_path not in generated_task_codes: + generated_task_codes.append(new_file_path) + + python_file_path = f"cliport/{generate_task_path}/{new_file_path}" + print(f"save {new_task['task-name']} to ", python_file_path) + + with open(python_file_path, "w") as fhandle: + fhandle.write(code) + + with open(generated_task_code_path, "w") as outfile: + json.dump(generated_task_codes, outfile, indent=4) + else: + print(f"{new_file_path}.py already exists.") + + # save task descriptions + generated_task_path = os.path.join( + self.cfg["prompt_data_path"], f"{generate_task_path}.json" + ) + generated_tasks = json.load(open(generated_task_path)) + generated_tasks[new_task["task-name"]] = new_task + + with open(generated_task_path, "w") as outfile: + json.dump(generated_tasks, outfile, indent=4) + + def save_task_to_offline_topdown(self, new_task, code, generate_task_path='topdown_generated_tasks'): + new_file_path = new_task["task-name"].replace("-", "_") + ".py" + generated_task_codes.append(new_file_path) + + python_file_path = f"cliport/{generate_task_path}/{new_file_path}" + print(f"save {new_task['task-name']} to ", python_file_path) + + with open(python_file_path, "w") as fhandle: + fhandle.write(code) + + + def load_offline_memory(self): + """get the current task descriptions, assets, and code""" + base_task_path = os.path.join(self.data_path, "base_tasks.json") + base_asset_path = os.path.join(self.data_path, "base_assets.json") + base_task_code_path = os.path.join(self.data_path, "base_task_codes.json") + + base_tasks = json.load(open(base_task_path)) + base_assets = json.load(open(base_asset_path)) + base_task_codes = json.load(open(base_task_code_path)) + + if self.cfg["load_memory"]: + generated_task_path = os.path.join(self.data_path, "generated_tasks.json") + generated_asset_path = os.path.join(self.data_path, "generated_assets.json") + generated_task_code_path = os.path.join(self.data_path, "generated_task_codes.json") + + print("original base task num:", len(base_tasks)) + base_tasks.update(json.load(open(generated_task_path))) + # base_assets.update(json.load(open(generated_asset_path))) + + for task in json.load(open(generated_task_code_path)): + if task not in base_task_codes: + base_task_codes.append(task) + + print("current base task num:", len(base_tasks)) + return base_tasks, base_assets, base_task_codes diff --git a/gensim/prepare_finetune_gpt.py b/gensim/prepare_finetune_gpt.py new file mode 100644 index 0000000000000000000000000000000000000000..13df49bf12e721c378a5159491cfc7be26f40ef1 --- /dev/null +++ b/gensim/prepare_finetune_gpt.py @@ -0,0 +1,110 @@ +import cv2 +import numpy as np +import IPython +import os + +import openai +import pandas as pd +import json +import subprocess +from gensim.utils import set_gpt_model, clear_messages, format_finetune_prompt + + + +def format_completion(task_name, descriptions, code): + completion_text = f" \n {task_name}: {descriptions}```\n\n###" + completion_text += "\n```python\n" + code + "\n```\n\n###" + return completion_text + +# test if using the finetuned model can generate better task coed than the base model +# https://platform.openai.com/docs/guides/fine-tuning +data_path = 'prompts/data' +def load_offline_memory(): + """get the current task descriptions, assets, and code""" + base_task_path = os.path.join(data_path, "base_tasks.json") + base_asset_path = os.path.join(data_path, "base_assets.json") + base_task_code_path = os.path.join(data_path, "base_task_codes.json") + + base_tasks = json.load(open(base_task_path)) + base_assets = json.load(open(base_asset_path)) + base_task_codes = json.load(open(base_task_code_path)) + + generated_task_path = os.path.join(data_path, "generated_tasks.json") + generated_asset_path = os.path.join(data_path, "generated_assets.json") + generated_task_code_path = os.path.join(data_path, "generated_task_codes.json") + + # print("original base task num:", len(base_tasks)) + base_tasks.update(json.load(open(generated_task_path))) + # base_assets.update(json.load(open(generated_asset_path))) + + for task in json.load(open(generated_task_code_path)): + if task not in base_task_codes: + base_task_codes.append(task) + + # print("current base task num:", len(base_tasks)) + return base_tasks, base_assets, base_task_codes + + +code_buffer = {} +base_tasks, base_assets, base_task_codes = load_offline_memory() +TOTAL_DATASET_TOKENS = 0 + +added_tasks = [] +df = pd.DataFrame() +for task_file in base_task_codes: + ## TODO(lirui): consider adding more structure here. + task_name = task_file[:-3].replace("_", "-") + if task_name in added_tasks: + continue + + if task_name not in base_tasks: + print(f"{task_name} missing") + continue + + added_tasks.append(task_name) + task_description = base_tasks[task_name] + + if os.path.exists("cliport/tasks/" + task_file): + task_code = open("cliport/tasks/" + task_file).read() + + # the generated cliport task path + elif os.path.exists("cliport/generated_tasks/" + task_file): + task_code = open("cliport/generated_tasks/" + task_file).read() + + prompt = format_finetune_prompt(task_name) + completion = format_completion(task_name, task_description, task_code) + + # rough estimates + TOTAL_DATASET_TOKENS += len(prompt) / 4 + TOTAL_DATASET_TOKENS += len(completion) / 4 + new_row = { 'prompt': prompt, + 'completion': completion} + new_row = pd.DataFrame([new_row]) + df = pd.concat([df, new_row], axis=0, ignore_index=True) + +df.to_csv("prompts/finetune_data.csv",index=False) +print("======================================") +print("estimate number of tokens:", TOTAL_DATASET_TOKENS) +print("estimate price for davinci:", TOTAL_DATASET_TOKENS / 1000 * 0.03) +print("total number of instructions:", len(df)) +print("======================================") +# actual finetuning + +## prepared_data.csv --> prepared_data_prepared.json +subprocess.run('openai tools fine_tunes.prepare_data --file prompts/finetune_data.csv'.split()) + +print("now you can run \n openai api fine_tunes.create --training_file prompts/finetune_data_prepared.jsonl --model davinci --suffix 'GenSim'") +# Model Training Usage +# Ada $0.0004 / 1K tokens $0.0016 / 1K tokens +# Curie $0.0030 / 1K tokens $0.0120 / 1K tokens +# Davinci $0.0300 / 1K tokens $0.1200 / 1K tokens + +# ## Start fine-tuning +# openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix "GenSim" +# subprocess.run('openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix "GenSim"'.split()) + + +# Tracking Finetune Status +# openai api fine_tunes.follow -i +# openai api fine_tunes.get -i +# openai wandb sync \ No newline at end of file diff --git a/gensim/prepare_finetune_gpt_new.py b/gensim/prepare_finetune_gpt_new.py new file mode 100644 index 0000000000000000000000000000000000000000..512c718ab461835b2cb9a945f363545d9d882e84 --- /dev/null +++ b/gensim/prepare_finetune_gpt_new.py @@ -0,0 +1,137 @@ +import cv2 +import numpy as np +import IPython +import os + +import openai +import pandas as pd +import json +import subprocess +from gensim.utils import set_gpt_model, clear_messages, format_finetune_prompt, format_finetune_prompt_codeonly + + +def format_completion_codeonly(task_name, descriptions, code): + completion_text = " \n```python\n" + code + "\n```\n\nSTOP" + return completion_text + +def format_completion(task_name, descriptions, code): + completion_text = f" \n {task_name}: {descriptions}```\n\n###" + completion_text += "\n```python\n" + code + "\n```\n\nSTOP" + return completion_text + +# test if using the finetuned model can generate better task coed than the base model +# https://platform.openai.com/docs/guides/fine-tuning +data_path = 'prompts/data' +def load_offline_memory(): + """get the current task descriptions, assets, and code""" + base_task_path = os.path.join(data_path, "base_tasks.json") + base_asset_path = os.path.join(data_path, "base_assets.json") + base_task_code_path = os.path.join(data_path, "base_task_codes.json") + + base_tasks = json.load(open(base_task_path)) + base_assets = json.load(open(base_asset_path)) + base_task_codes = json.load(open(base_task_code_path)) + + generated_task_path = os.path.join(data_path, "generated_tasks.json") + generated_asset_path = os.path.join(data_path, "generated_assets.json") + generated_task_code_path = os.path.join(data_path, "generated_task_codes.json") + + # print("original base task num:", len(base_tasks)) + base_tasks.update(json.load(open(generated_task_path))) + # base_assets.update(json.load(open(generated_asset_path))) + + for task in json.load(open(generated_task_code_path)): + if task not in base_task_codes: + base_task_codes.append(task) + + # print("current base task num:", len(base_tasks)) + return base_tasks, base_assets, base_task_codes + + +code_buffer = {} +base_tasks, base_assets, base_task_codes = load_offline_memory() +TOTAL_DATASET_TOKENS = 0 + +added_tasks = [] +df = pd.DataFrame() +file_name = 'prompts/finetune_data_new.jsonl' +file = open(file_name, 'w') + + +for task_file in base_task_codes: + ## TODO(lirui): consider adding more structure here. + task_name = task_file[:-3].replace("_", "-") + if task_name in added_tasks: + continue + + if task_name not in base_tasks: + print(f"{task_name} missing") + continue + + added_tasks.append(task_name) + task_description = base_tasks[task_name] + + if os.path.exists("cliport/tasks/" + task_file): + task_code = open("cliport/tasks/" + task_file).read() + + # the generated cliport task path + elif os.path.exists("cliport/generated_tasks/" + task_file): + task_code = open("cliport/generated_tasks/" + task_file).read() + + # prompt = format_finetune_prompt(task_name) + # completion = format_completion(task_name, task_description, task_code) + + prompt = format_finetune_prompt_codeonly(task_name) + completion = format_completion_codeonly(task_name, task_description, task_code) + + + # rough estimates + TOTAL_DATASET_TOKENS += len(prompt) / 4 + TOTAL_DATASET_TOKENS += len(completion) / 4 + # new_row = { 'prompt': prompt, + # 'completion': completion} + # new_row = {"role": "system", "content": "You are an AI in robot simulation code and task design."} + # new_row = pd.DataFrame([new_row]) + # df = pd.concat([df, new_row], axis=0, ignore_index=True) + + # new_row = {"role": "user", "content": prompt} + # new_row = pd.DataFrame([new_row]) + # df = pd.concat([df, new_row], axis=0, ignore_index=True) + + # new_row = {"role": "assistant", "content": completion} + # new_row = pd.DataFrame([new_row]) + # df = pd.concat([df, new_row], axis=0, ignore_index=True) + data = ({"messages": [{"role": "system", "content": "You are an AI in robot simulation code and task design."}, + {"role": "user", "content": prompt}, + {"role": "assistant", "content": completion}]}) + # print(data) + file.write(json.dumps(data)+"\n") # jsonl + +# df.to_csv("prompts/finetune_data.csv",index=False) +print("======================================") +print("estimate number of tokens:", TOTAL_DATASET_TOKENS) +print("estimate price for davinci:", TOTAL_DATASET_TOKENS / 1000 * 0.03) +print("total number of instructions:", len(df)) +print("======================================") +# actual finetuning + +## prepared_data.csv --> prepared_data_prepared.json +# subprocess.run('openai tools fine_tunes.prepare_data --file prompts/finetune_data.csv --quiet'.split()) + +print("now you can run \n python misc/job_create.py") +print("check file!:", file_name) + +# Model Training Usage +# Ada $0.0004 / 1K tokens $0.0016 / 1K tokens +# Curie $0.0030 / 1K tokens $0.0120 / 1K tokens +# Davinci $0.0300 / 1K tokens $0.1200 / 1K tokens + +# ## Start fine-tuning +# openai api fine_tunes.create --training_file prompts/finetune_data_new.jsonl --model gpt-3.5-turbo --suffix "GenSimNew" +# subprocess.run('openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix "GenSim"'.split()) + + +# Tracking Finetune Status +# openai api fine_tunes.follow -i +# openai api fine_tunes.get -i +# openai wandb sync \ No newline at end of file diff --git a/gensim/run_simulation.py b/gensim/run_simulation.py new file mode 100644 index 0000000000000000000000000000000000000000..29f7e6f9fadfc7b1d8a941e8ab8f85c791c0adf2 --- /dev/null +++ b/gensim/run_simulation.py @@ -0,0 +1,50 @@ +import numpy as np +import os +import hydra +import random + +import re +import openai +import IPython +import time +import pybullet as p +import traceback +from datetime import datetime +from pprint import pprint +import cv2 +import re +import random +import json + +from gensim.agent import Agent +from gensim.critic import Critic +from gensim.sim_runner import SimulationRunner +from gensim.memory import Memory +from gensim.utils import set_gpt_model, clear_messages + + +@hydra.main(config_path='../cliport/cfg', config_name='data', version_base="1.2") +def main(cfg): + openai.api_key = cfg['openai_key'] + + model_time = datetime.now().strftime("%d_%m_%Y_%H:%M:%S") + cfg['model_output_dir'] = os.path.join(cfg['output_folder'], cfg['prompt_folder'] + "_" + model_time) + if 'seed' in cfg: + cfg['model_output_dir'] = cfg['model_output_dir'] + f"_{cfg['seed']}" + + set_gpt_model(cfg['gpt_model']) + memory = Memory(cfg) + agent = Agent(cfg, memory) + critic = Critic(cfg, memory) + simulation_runner = SimulationRunner(cfg, agent, critic, memory) + + for trial_i in range(cfg['trials']): + simulation_runner.task_creation() + simulation_runner.simulate_task() + simulation_runner.print_current_stats() + # clear_messages() + + simulation_runner.save_stats() + +if __name__ == '__main__': + main() diff --git a/gensim/sim_runner.py b/gensim/sim_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..ecc373a3386b1266abbd5f842dce4234e1fcf2e0 --- /dev/null +++ b/gensim/sim_runner.py @@ -0,0 +1,397 @@ +import numpy as np +import os +import IPython +from cliport import tasks +from cliport.dataset import RavensDataset +from cliport.environments.environment import Environment + +from pygments import highlight +from pygments.lexers import PythonLexer +from pygments.formatters import TerminalFormatter + +import time +import random +import json +import traceback +from gensim.utils import ( + mkdir_if_missing, + save_text, + save_stat, + compute_diversity_score_from_assets, + add_to_txt +) +import pybullet as p + +class SimulationRunner: + """ the main class that runs simulation loop """ + def __init__(self, cfg, agent, critic, memory): + self.cfg = cfg + self.agent = agent + self.critic = critic + self.memory = memory + + # statistics + self.syntax_pass_rate = 0 + self.runtime_pass_rate = 0 + self.env_pass_rate = 0 + self.curr_trials = 0 + + self.prompt_folder = f"prompts/{cfg['prompt_folder']}" + self.chat_log = memory.chat_log + self.task_asset_logs = [] + + # All the generated tasks in this run. + # Different from the ones in online buffer that can load from offline. + self.generated_task_assets = [] + self.generated_task_programs = [] + self.generated_task_names = [] + self.generated_tasks = [] + self.passed_tasks = [] # accepted ones + + def print_current_stats(self): + """ print the current statistics of the simulation design """ + print("=========================================================") + print(f"{self.cfg['prompt_folder']} Trial {self.curr_trials} SYNTAX_PASS_RATE: {(self.syntax_pass_rate / (self.curr_trials)) * 100:.1f}% RUNTIME_PASS_RATE: {(self.runtime_pass_rate / (self.curr_trials)) * 100:.1f}% ENV_PASS_RATE: {(self.env_pass_rate / (self.curr_trials)) * 100:.1f}%") + print("=========================================================") + + def save_stats(self): + """ save the final simulation statistics """ + self.diversity_score = compute_diversity_score_from_assets(self.task_asset_logs, self.curr_trials) + save_stat(self.cfg, self.cfg['model_output_dir'], self.generated_tasks, self.syntax_pass_rate / (self.curr_trials), + self.runtime_pass_rate / (self.curr_trials), self.env_pass_rate / (self.curr_trials), self.diversity_score) + print("Model Folder: ", self.cfg['model_output_dir']) + print(f"Total {len(self.generated_tasks)} New Tasks:", [task['task-name'] for task in self.generated_tasks]) + try: + print(f"Added {len(self.passed_tasks)} Tasks:", self.passed_tasks) + except: + pass + + def example_task_creation(self): + """ create the task through interactions of agent and critic """ + self.task_creation_pass = True + mkdir_if_missing(self.cfg['model_output_dir']) + + try: + start_time = time.time() + + self.generated_task = {'task-name': 'TASK_NAME_TEMPLATE', 'task-description': 'TASK_STRING_TEMPLATE', 'assets-used': ['ASSET_1', 'ASSET_2', Ellipsis]} + print("generated_task\n", self.generated_task) + yield "Task Generated ==>", None, None + self.generated_asset = self.agent.propose_assets() + # self.generated_asset = {} + print("generated_asset\n", self.generated_asset) + yield "Task Generated ==> Asset Generated ==> ", None, None + yield "Task Generated ==> Asset Generated ==> API Reviewed ==> ", None, None + yield "Task Generated ==> Asset Generated ==> API Reviewed ==> Error Reviewed ==> ", None, None + + self.curr_task_name = self.generated_task_name = 'BuildWheel' + + self.generated_code = """ +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildWheel(Task): + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "Construct a wheel using blocks and a sphere. First, position eight blocks in a circular layout on the tabletop. Each block should be touching its two neighbors and colored in alternating red and blue. Then place a green sphere in the center of the circular layout, completing the wheel." + self.task_completed_desc = "done building wheel." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue']] + blocks = [] + for i in range(8): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i % 2]) + blocks.append(block_id) + + # Add sphere. + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + sphere_color = utils.COLORS['green'] + sphere_pose = ((0.5, 0.0, 0.0), (0,0,0,1)) # fixed pose + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=sphere_color) + + # Goal: blocks are arranged in a circle and sphere is in the center. + circle_radius = 0.1 + circle_center = (0, 0, block_size[2] / 2) + angles = np.linspace(0, 2 * np.pi, 8, endpoint=False) + block_poses = [(circle_center[0] + circle_radius * np.cos(angle), + circle_center[1] + circle_radius * np.sin(angle), + circle_center[2]) for angle in angles] + block_poses = [(utils.apply(sphere_pose, pos), sphere_pose[1]) for pos in block_poses] + self.add_goal(objs=blocks, matches=np.ones((8, 8)), targ_poses=block_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=8 / 9) + + # Goal: sphere is in the center of the blocks. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[sphere_pose], replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1 / 9) + + self.lang_goals.append(self.lang_template) + """ + print("generated_code\n", self.generated_code) + print("curr_task_name\n", self.curr_task_name) + yield "Task Generated ==> Asset Generated ==> API Reviewed ==> Error Reviewed ==> Code Generated ==> ", self.generated_code, None + + self.generated_tasks.append(self.generated_task) + self.generated_task_assets.append(self.generated_asset) + self.generated_task_programs.append(self.generated_code) + self.generated_task_names.append(self.generated_task_name) + except: + to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter()) + print("Task Creation Exception:", to_print) + self.task_creation_pass = False + + # self.curr_task_name = self.generated_task['task-name'] + print("task creation time {:.3f}".format(time.time() - start_time)) + + def task_creation(self): + """ create the task through interactions of agent and critic """ + self.task_creation_pass = True + mkdir_if_missing(self.cfg['model_output_dir']) + + try: + start_time = time.time() + self.generated_task = self.agent.propose_task(self.generated_task_names) + + # self.generated_task = {'task-name': 'TASK_NAME_TEMPLATE', 'task-description': 'TASK_STRING_TEMPLATE', 'assets-used': ['ASSET_1', 'ASSET_2', Ellipsis]} + print("generated_task\n", self.generated_task) + + yield "Task Generated ==>", None, None + + self.generated_asset = self.agent.propose_assets() + + # self.generated_asset = {} + print("generated_asset\n", self.generated_asset) + yield "Task Generated ==> Asset Generated ==> ", None, None + + self.agent.api_review() + + + yield "Task Generated ==> Asset Generated ==> API Reviewed ==> ", None, None + self.critic.error_review(self.generated_task) + + + yield "Task Generated ==> Asset Generated ==> API Reviewed ==> Error Reviewed ==> ", None, None + self.generated_code, self.curr_task_name = self.agent.implement_task() + self.task_asset_logs.append(self.generated_task["assets-used"]) + self.generated_task_name = self.generated_task["task-name"] + + # self.curr_task_name = self.generated_task_name = 'BuildWheel' +# +# self.generated_code = """ +# import numpy as np +# from cliport.tasks.task import Task +# from cliport.utils import utils +# +# class BuildWheel(Task): +# +# def __init__(self): +# super().__init__() +# self.max_steps = 10 +# self.lang_template = "Construct a wheel using blocks and a sphere. First, position eight blocks in a circular layout on the tabletop. Each block should be touching its two neighbors and colored in alternating red and blue. Then place a green sphere in the center of the circular layout, completing the wheel." +# self.task_completed_desc = "done building wheel." +# self.additional_reset() +# +# def reset(self, env): +# super().reset(env) +# +# # Add blocks. +# block_size = (0.04, 0.04, 0.04) +# block_urdf = 'block/block.urdf' +# block_colors = [utils.COLORS['red'], utils.COLORS['blue']] +# blocks = [] +# for i in range(8): +# block_pose = self.get_random_pose(env, block_size) +# block_id = env.add_object(block_urdf, block_pose, color=block_colors[i % 2]) +# blocks.append(block_id) +# +# # Add sphere. +# sphere_size = (0.04, 0.04, 0.04) +# sphere_urdf = 'sphere/sphere.urdf' +# sphere_color = utils.COLORS['green'] +# sphere_pose = ((0.5, 0.0, 0.0), (0,0,0,1)) # fixed pose +# sphere_id = env.add_object(sphere_urdf, sphere_pose, color=sphere_color) +# +# # Goal: blocks are arranged in a circle and sphere is in the center. +# circle_radius = 0.1 +# circle_center = (0, 0, block_size[2] / 2) +# angles = np.linspace(0, 2 * np.pi, 8, endpoint=False) +# block_poses = [(circle_center[0] + circle_radius * np.cos(angle), +# circle_center[1] + circle_radius * np.sin(angle), +# circle_center[2]) for angle in angles] +# block_poses = [(utils.apply(sphere_pose, pos), sphere_pose[1]) for pos in block_poses] +# self.add_goal(objs=blocks, matches=np.ones((8, 8)), targ_poses=block_poses, replace=False, +# rotations=True, metric='pose', params=None, step_max_reward=8 / 9) +# +# # Goal: sphere is in the center of the blocks. +# self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[sphere_pose], replace=False, +# rotations=False, metric='pose', params=None, step_max_reward=1 / 9) +# +# self.lang_goals.append(self.lang_template) +# """ + print("generated_code\n", self.generated_code) + print("curr_task_name\n", self.curr_task_name) + yield "Task Generated ==> Asset Generated ==> API Reviewed ==> Error Reviewed ==> Code Generated ==> ", self.generated_code, None + + self.generated_tasks.append(self.generated_task) + self.generated_task_assets.append(self.generated_asset) + self.generated_task_programs.append(self.generated_code) + self.generated_task_names.append(self.generated_task_name) + except: + to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter()) + print("Task Creation Exception:", to_print) + self.task_creation_pass = False + + # self.curr_task_name = self.generated_task['task-name'] + print("task creation time {:.3f}".format(time.time() - start_time)) + + + def setup_env(self): + """ build the new task""" + env = Environment( + self.cfg['assets_root'], + disp=self.cfg['disp'], + shared_memory=self.cfg['shared_memory'], + hz=480, + record_cfg=self.cfg['record'] + ) + + task = eval(self.curr_task_name)() + task.mode = self.cfg['mode'] + record = self.cfg['record']['save_video'] + save_data = self.cfg['save_data'] + + # Initialize scripted oracle agent and dataset. + expert = task.oracle(env) + self.cfg['task'] = self.generated_task["task-name"] + data_path = os.path.join(self.cfg['data_dir'], "{}-{}".format(self.generated_task["task-name"], task.mode)) + dataset = RavensDataset(data_path, self.cfg, n_demos=0, augment=False) + print(f"Saving to: {data_path}") + print(f"Mode: {task.mode}") + + # Start video recording + if record: + env.start_rec(f'{dataset.n_episodes+1:06d}') + + return task, dataset, env, expert + + def run_one_episode(self, dataset, expert, env, task, episode, seed): + """ run the new task for one episode """ + add_to_txt( + self.chat_log, f"================= TRIAL: {self.curr_trials}", with_print=True) + record = self.cfg['record']['save_video'] + np.random.seed(seed) + random.seed(seed) + print('Oracle demo: {}/{} | Seed: {}'.format(dataset.n_episodes + 1, self.cfg['n'], seed)) + env.set_task(task) + obs = env.reset() + + info = env.info + reward = 0 + total_reward = 0 + + # Rollout expert policy + for _ in range(task.max_steps): + act = expert.act(obs, info) + episode.append((obs, act, reward, info)) + lang_goal = info['lang_goal'] + obs, reward, done, info = env.step(act) + total_reward += reward + print(f'Total Reward: {total_reward:.3f} | Done: {done} | Goal: {lang_goal}') + if done: + break + + episode.append((obs, None, reward, info)) + return total_reward + + def simulate_task(self): + """ simulate the created task and save demonstrations """ + total_cnt = 0. + reset_success_cnt = 0. + env_success_cnt = 0. + seed = 123 + self.curr_trials += 1 + + if p.isConnected(): + p.disconnect() + + if not self.task_creation_pass: + print("task creation failure => count as syntax exceptions.") + return + + # Check syntax and compilation-time error + try: + exec(self.generated_code, globals()) + task, dataset, env, expert = self.setup_env() + self.syntax_pass_rate += 1 + + except: + to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter()) + save_text(self.cfg['model_output_dir'], self.generated_task_name + '_error', str(traceback.format_exc())) + print("========================================================") + print("Syntax Exception:", to_print) + return + + try: + # Collect environment and collect data from oracle demonstrations. + env.generated_code = self.generated_code + # Set seeds. + episode = [] + + + """ run the new task for one episode """ + add_to_txt( + self.chat_log, f"================= TRIAL: {self.curr_trials}", with_print=True) + np.random.seed(seed) + random.seed(seed) + print('Oracle demo: {}/{} | Seed: {}'.format(dataset.n_episodes + 1, self.cfg['n'], seed)) + env.set_task(task) + obs = env.reset() + + info = env.info + reward = 0 + total_reward = 0 + # Rollout expert policy + + start_time = time.time() + print("start sim") + for i in range(task.max_steps): + act = expert.act(obs, info) + episode.append((obs, act, reward, info)) + lang_goal = info['lang_goal'] + env.generated_code = self.generated_code + yield from env.step(act) + + obs, reward, done, info = env.cur_obs, env.cur_reward, env.cur_done, env.cur_info + total_reward += reward + print(f'Total Reward: {total_reward:.3f} | Done: {done} | Goal: {lang_goal}') + + if done: + break + + end_time = time.time() + print("end sim, time used = ", end_time - start_time) + yield "Task Generated ==> Asset Generated ==> API Reviewed ==> Error Reviewed ==> Code Generated ==> Simulation Running completed", self.generated_code, env.video_path + episode.append((obs, None, reward, info)) + + + # reset_success_cnt += 1 + # env_success_cnt += total_reward > 0.99 + # + # self.runtime_pass_rate += 1 + print("Runtime Test Pass!") + except: + to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter()) + save_text(self.cfg['model_output_dir'], self.generated_task_name + '_error', str(traceback.format_exc())) + print("========================================================") + print("Runtime Exception:", to_print) + self.memory.save_run(self.generated_task) diff --git a/gensim/topdown_sim_runner.py b/gensim/topdown_sim_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..45a2b200a12ae28987b4101ca38264f521702f88 --- /dev/null +++ b/gensim/topdown_sim_runner.py @@ -0,0 +1,221 @@ +import numpy as np +import os +import IPython +from cliport import tasks +from cliport.dataset import RavensDataset +from cliport.environments.environment import Environment + +from pygments import highlight +from pygments.lexers import PythonLexer +from pygments.formatters import TerminalFormatter + +import time +import random +import json +import traceback +from gensim.utils import ( + mkdir_if_missing, + save_text, + save_stat, + compute_diversity_score_from_assets, + add_to_txt, + extract_dict, + extract_code, + extract_code_topdown +) +import pybullet as p +import copy +dummy_task = {"task_name": "dummy", "task_descriptions": "dummy", "assets-used": "dummy"} + +class TopDownSimulationRunner: + """ the main class that runs simulation loop """ + def __init__(self, cfg, memory): + self.cfg = cfg + self.memory = memory + + # statistics + self.syntax_pass_rate = 0 + self.runtime_pass_rate = 0 + self.env_pass_rate = 0 + self.curr_trials = 0 + + self.prompt_folder = f"prompts/{cfg['prompt_folder']}" + self.chat_log = memory.chat_log + self.task_asset_logs = [] + + # All the generated tasks in this run. + # Different from the ones in online buffer that can load from offline. + self.generated_task_assets = [] + self.generated_task_programs = [] + self.generated_task_names = [] + self.generated_tasks = [] + self.passed_tasks = [] # accepted ones + + def print_current_stats(self): + """ print the current statistics of the simulation design """ + print("=========================================================") + print(f"{self.cfg['prompt_folder']} Trial {self.curr_trials} SYNTAX_PASS_RATE: {(self.syntax_pass_rate / (self.curr_trials)) * 100:.1f}% RUNTIME_PASS_RATE: {(self.runtime_pass_rate / (self.curr_trials)) * 100:.1f}% ENV_PASS_RATE: {(self.env_pass_rate / (self.curr_trials)) * 100:.1f}%") + print("=========================================================") + + def save_stats(self): + """ save the final simulation statistics """ + self.diversity_score = compute_diversity_score_from_assets(self.task_asset_logs, self.curr_trials) + save_stat(self.cfg, self.cfg['model_output_dir'], self.generated_tasks, self.syntax_pass_rate / (self.curr_trials), + self.runtime_pass_rate / (self.curr_trials), self.env_pass_rate / (self.curr_trials), self.diversity_score) + print("Model Folder: ", self.cfg['model_output_dir']) + print(f"Total {len(self.generated_tasks)} New Tasks:", [task['task-name'] for task in self.generated_tasks]) + try: + print(f"Added {len(self.passed_tasks)} Tasks:", self.passed_tasks) + except: + pass + + + def task_creation(self, res): + """ create the task through interactions of agent and critic """ + self.task_creation_pass = True + mkdir_if_missing(self.cfg['model_output_dir']) + + try: + start_time = time.time() + task_def = extract_dict(res, prefix="new_task") + exec(task_def, globals()) + self.generated_task = new_task + except: + to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter()) + print("Task Creation Exception:", to_print) + self.generated_task = copy.deepcopy(dummy_task) + try: + self.generated_asset = {} + self.generated_code, self.curr_task_name = extract_code_topdown(res) + + self.task_asset_logs.append(self.generated_task["assets-used"]) + self.generated_task_name = self.generated_task["task-name"] + self.generated_tasks.append(self.generated_task) + self.generated_task_assets.append(self.generated_asset) + self.generated_task_programs.append(self.generated_code) + self.generated_task_names.append(self.generated_task_name) + except: + to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter()) + print("Task Code Creation Exception:", to_print) + self.task_creation_pass = False + + # self.curr_task_name = self.generated_task['task-name'] + print("task creation time {:.3f}".format(time.time() - start_time)) + + + def setup_env(self): + """ build the new task""" + env = Environment( + self.cfg['assets_root'], + disp=self.cfg['disp'], + shared_memory=self.cfg['shared_memory'], + hz=480, + record_cfg=self.cfg['record'] + ) + + task = eval(self.curr_task_name)() + task.mode = self.cfg['mode'] + record = self.cfg['record']['save_video'] + save_data = self.cfg['save_data'] + + # Initialize scripted oracle agent and dataset. + expert = task.oracle(env) + self.cfg['task'] = self.generated_task["task-name"] + data_path = os.path.join(self.cfg['data_dir'], "{}-{}".format(self.generated_task["task-name"], task.mode)) + dataset = RavensDataset(data_path, self.cfg, n_demos=0, augment=False) + print(f"Saving to: {data_path}") + print(f"Mode: {task.mode}") + + # Start video recording + if record: + env.start_rec(f'{dataset.n_episodes+1:06d}') + + return task, dataset, env, expert + + def run_one_episode(self, dataset, expert, env, task, episode, seed): + """ run the new task for one episode """ + add_to_txt( + self.chat_log, f"================= TRIAL: {self.curr_trials}", with_print=True) + record = self.cfg['record']['save_video'] + np.random.seed(seed) + random.seed(seed) + print('Oracle demo: {}/{} | Seed: {}'.format(dataset.n_episodes + 1, self.cfg['n'], seed)) + env.set_task(task) + obs = env.reset() + + info = env.info + reward = 0 + total_reward = 0 + + # Rollout expert policy + for _ in range(task.max_steps): + act = expert.act(obs, info) + episode.append((obs, act, reward, info)) + lang_goal = info['lang_goal'] + obs, reward, done, info = env.step(act) + total_reward += reward + print(f'Total Reward: {total_reward:.3f} | Done: {done} | Goal: {lang_goal}') + if done: + break + + episode.append((obs, None, reward, info)) + return total_reward + + def simulate_task(self): + """ simulate the created task and save demonstrations """ + total_cnt = 0. + reset_success_cnt = 0. + env_success_cnt = 0. + seed = 123 + self.curr_trials += 1 + + if p.isConnected(): + p.disconnect() + + if not self.task_creation_pass: + print("task creation failure => count as syntax exceptions.") + return + + # Check syntax and compilation-time error + try: + exec(self.generated_code, globals()) + task, dataset, env, expert = self.setup_env() + self.syntax_pass_rate += 1 + + except: + to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter()) + save_text(self.cfg['model_output_dir'], self.generated_task_name + '_error', str(traceback.format_exc())) + print("========================================================") + print("Syntax Exception:", to_print) + return + + try: + # Collect environment and collect data from oracle demonstrations. + while total_cnt <= self.cfg['max_env_run_cnt']: + total_cnt += 1 + episode = [] + total_reward = self.run_one_episode(dataset, expert, env, task, episode, seed) + + reset_success_cnt += 1 + env_success_cnt += total_reward > 0.99 + + self.runtime_pass_rate += 1 + print("Runtime Test Pass!") + + # the task can actually be completed with oracle. 50% success rates are high enough. + if env_success_cnt >= total_cnt / 2: + self.env_pass_rate += 1 + print("Environment Test Pass!") + + if self.cfg['save_memory']: + self.memory.save_task_to_offline_topdown(self.generated_task, self.generated_code) + print(f"added new task to offline: {self.generated_task['task-name']}") + + except: + to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter()) + save_text(self.cfg['model_output_dir'], self.generated_task_name + '_error', str(traceback.format_exc())) + print("========================================================") + print("Runtime Exception:", to_print) + self.memory.save_run(self.generated_task) + + diff --git a/gensim/use_finetune_model.py b/gensim/use_finetune_model.py new file mode 100644 index 0000000000000000000000000000000000000000..dd7f2032a4a1fbf9ae7014a03911df6c65fbbb8a --- /dev/null +++ b/gensim/use_finetune_model.py @@ -0,0 +1,54 @@ +import openai +import argparse +import os +from cliport import tasks +from cliport.dataset import RavensDataset +from cliport.environments.environment import Environment + +from pygments import highlight +from pygments.lexers import PythonLexer +from pygments.formatters import TerminalFormatter + +import time +import random +import json + +from gensim.utils import set_gpt_model, clear_messages, format_finetune_prompt + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--task", type=str, default='build-car') + parser.add_argument("--model", type=str, default='davinci:ft-wang-lab:gensim-2023-08-05-16-54-05') + # davinci:ft-mit-cal:gensim-2023-08-06-16-00-56 + args = parser.parse_args() + task = args.task + prompt = format_finetune_prompt(task) + + if True: + response = openai.Completion.create( + model=args.model, + prompt=prompt, + temperature=0, + max_tokens=1024) + res = response["choices"][0]["text"] + else: + params = { + "model": args.model, + "max_tokens": 500, + "temperature": 0.1, + "messages": [prompt] + } + call_res = openai.ChatCompletion.create(**params) + res = call_res["choices"][0]["message"]["content"] + + print("code!:", res) + python_file_path = f"cliport/generated_tasks/finetune_{task.replace('-','_')}.py" + print(f"saving task {args.task} to {python_file_path}") + + # evaluate and then save + # with open(python_file_path, "w", + # ) as fhandle: + # fhandle.write(res) + diff --git a/gensim/utils.py b/gensim/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0e83e6a2a2d8c0109807f4068ba4ad59a9b221ae --- /dev/null +++ b/gensim/utils.py @@ -0,0 +1,398 @@ +import os + +import numpy as np +import os +import hydra +import numpy as np +import random + +from cliport import tasks +from cliport.dataset import RavensDataset +from cliport.environments.environment import Environment + +from pygments import highlight +from pygments.lexers import PythonLexer +from pygments.formatters import TerminalFormatter +import re + +import openai +import IPython +import time +import pybullet as p +import traceback +from datetime import datetime +from pprint import pprint +import cv2 +import re +import random +import json +import operator +import csv +import itertools + +model = "gpt-4" +# model = "gpt-3.5-turbo-16k" +# model = "gpt-4-0613" + +def set_gpt_model(gpt_model_name): + """ globally set gpt-model""" + global model + model = gpt_model_name + print("use gpt model:", model) + +def mkdir_if_missing(dst_dir): + if not os.path.exists(dst_dir): + os.makedirs(dst_dir) + + +def save_text(folder, name, out): + mkdir_if_missing(folder) + with open(os.path.join(folder, name + ".txt"), "w") as fhandle: + fhandle.write(out) + + +def add_to_txt(full_interaction, message, with_print=False): + """ Add the message string to the full interaction """ + full_interaction.append("\n\n"+message) + if with_print: + print("\n\n"+message) + return full_interaction + +def get_task_import_str(): + return "import numpy as np\n" + \ + "import os\n" + \ + "import pybullet as p\n" + \ + "import random\n" + \ + "from cliport.tasks import primitives\n" + \ + "from cliport.tasks.grippers import Spatula\n" + \ + "from cliport.tasks.task import Task\n" + \ + "from cliport.utils import utils\n" + +def extract_code(res): + """ parse code block """ + # Pattern to find string between ``` + pattern = r'```(.*?)```' + + # Use re.findall to get all substrings within ``` + code_string = re.findall(pattern, res, re.DOTALL) + if len(code_string) == 0: + print("\n".join(res.split("\n"))) + print("empty code string") + return '', '' + + code_string = code_string[0] + code_string = code_string.replace('python', '') + code_lines = code_string.split("\n") + + if 'python' in code_string: + code_lines = code_lines[1:] # skip the first line + + class_def = [line for line in code_lines if line.startswith('class')] + task_name = class_def[0] + task_name = task_name[task_name.find("class "): task_name.rfind("(Task)")][6:] + + print("task_name:", task_name) + return get_task_import_str() + '\n'.join(code_lines).strip(), task_name + +def extract_code_topdown(res): + """ parse code block """ + # Pattern to find string between ``` + pattern = r'```python\n(.*?)```' + # pattern = r'```python\n(.*?)' + # Use re.findall to get all substrings within ``` + # code_string = re.findall(pattern, res, re.DOTALL) + print(res) + code_string = res[res.index("```python\n"):].strip() + if len(code_string) == 0: + print("\n".join(res.split("\n"))) + print("empty code string") + return '', '' + + # code_string = code_string[0] + code_string = code_string.replace('python', '') + code_lines = code_string.split("\n")[1:] + if code_lines[-1].strip().endswith(","): + code_lines[-1] = code_lines[-1][:-1] + "))" + if 'python' in code_string: + code_lines = code_lines[1:] # skip the first line + + class_def = [line for line in code_lines if line.startswith('class')] + task_name = class_def[0] + task_name = task_name[task_name.find("class "): task_name.rfind("(Task)")][6:] + # IPython.embed() + print("task_name:", task_name) + return '\n'.join(code_lines).strip(), task_name + + +def extract_dict(res, prefix="new_task"): + """ parse task dictionary """ + pattern = r'{(.*?)}' + code_string = re.findall(pattern, res, re.DOTALL) + if len(code_string) == 0: + return '' + + code_string = code_string[0] + code_string = code_string.replace('python', '') + + return prefix + '={'+ code_string.replace("\n","").strip() + '}' + + + +def extract_list(res, prefix="code_reference"): + """ parse task dictionary """ + pattern = r'\[(.*?)\]' + code_string = re.findall(pattern, res, re.DOTALL) + if len(code_string) == 0: + return '' + + code_string = code_string[0] + return prefix + '=[' + code_string.strip() + ']' + +def extract_assets(res): + """ parse generated assets """ + pattern = r'' + code_string = re.findall(pattern, res, re.DOTALL) + + assets_pattern = r'robot name="(.*?)">' + assets_string = re.findall(assets_pattern, res, re.DOTALL) + if len(code_string) == 0: + return {} + + try: + new_urdf = {} + for asset_path, code in zip(assets_string, code_string): + new_urdf[asset_path] = " 0: + sample_idx = np.random.choice(sample_idx, sample_num, replace=False) + + for idx, (task_name, task_desc) in enumerate(task_name_dict.items()): + if idx in sample_idx: + prompt_replacement += f'- {task_name}: {task_desc}\n' + + return prompt_replacement + "\n\n" + +def format_list_prompt(task_list, sample_num=-1, sort_items=False): + """ format a saved dictionary into prompt """ + + # if sort_items: + # task_list = sorted(task_list, key=operator.itemgetter(0)) + prompt_replacement = '' + sample_idx = list(range(len(task_list))) + + if sample_num > 0: + sample_idx = np.random.choice(len(task_list), sample_num, replace=False) + + for idx, task in enumerate(task_list): + if idx in sample_idx: + prompt_replacement += f"- {task['task-name']}: {task['task-descriptions']}\n" + + return prompt_replacement + "\n\n" + +def sample_list_reference(item_list, sample_num=-1): + """ sample reference code from a list of python files """ + sample_idx = list(range(len(item_list))) + prompt_replacement = '' + + if sample_num > 0: + sample_idx = np.random.choice(len(item_list), sample_num, replace=False) + + print("reference files: ", [item_list[idx] for idx in sample_idx]) + for idx, item in enumerate(item_list): + try: + item_content = open(f"cliport/tasks/{item}").read() + except: + # one or the other + item_content = open(f"cliport/generated_tasks/{item}").read() + + if idx in sample_idx: + prompt_replacement += f'```\n{item_content}\n```\n\n' + + return prompt_replacement + "\n\n" + + +def compute_diversity_score_from_assets_old(task_assets): + """ compute how many new asset combos are covered by previous by a proxy""" + if len(task_assets) < 2: + return 0 + + existing_assets = [] + for asset in task_assets: + new_asset_flag = True + for existing_asset in existing_assets: + # it's covered by any previous assets + if set(asset).issubset(existing_asset): + new_asset_flag = False + break + + if new_asset_flag: + existing_assets.append(asset) + + return len(existing_assets) / len(task_assets) + +def iou_assets(asset1, asset2): + asset1 = set(asset1) + asset2 = set(asset2) + return len(asset1 & asset2) / len(asset1 | asset2) + +def compute_diversity_score_from_assets(task_assets, total_trials): + """ compute the pairwise IOU for assets""" + if len(task_assets) == 0: + return 0 + + score = 0 + pairs = list(itertools.combinations(range(len(task_assets)), 2)) + for j, k in pairs: + score += 1. - iou_assets(task_assets[j], task_assets[k]) + + if len(pairs) == 0: + return 0 + + return score / len(pairs) + +def truncate_message_for_token_limit(message_history, max_tokens=6000): + truncated_messages = [] + tokens = 0 + + # reverse + for idx in range(len(message_history)-1, -1, -1) : + message = message_history[idx] + message_tokens = len(message['content']) / 4 # rough estimate. + # print("message_tokens:", message['content']) + if tokens + message_tokens > max_tokens: + break # This message would put us over the limit + + truncated_messages.append(message) + tokens += message_tokens + + truncated_messages.reverse() + # print("truncated messages:", len(truncated_messages)) + return truncated_messages + +def insert_system_message(message_history): + system_message_prompt = 'You are a helpful and expert assistant in robot simulation code writing and task design.' + 'You design tasks that are creative and do-able by table-top manipulation. ' + 'You write code without syntax errors and always think through and document your code carefully. ' + message_history.insert(0, {"role": "system", "content": system_message_prompt}) + +# globally always feed the previous reply as the assistant message back into the model +existing_messages = [] +def generate_feedback(prompt, max_tokens=2048, temperature=0.0, interaction_txt=None, retry_max=5, n=1): + """ use GPT-4 API """ + global existing_messages + global model + if model == "text-davinci-003": + return generate_feedback_completion_only(prompt, max_tokens, temperature) + existing_messages.append({"role": "user", "content": prompt}) + truncated_messages = truncate_message_for_token_limit(existing_messages) + insert_system_message(truncated_messages) + + params = { + "model": model, + "max_tokens": max_tokens, + "temperature": temperature, + "messages": truncated_messages, + "n": n + } + + for retry in range(retry_max): + try: + if interaction_txt is not None: + add_to_txt(interaction_txt, ">>> Prompt: \n" + prompt, with_print=False) + call_res = openai.ChatCompletion.create(**params) + res = call_res["choices"][0]["message"]["content"] + existing_messages.append({"role": "assistant", "content": res}) + + to_print = highlight(f"{res}", PythonLexer(), TerminalFormatter()) + print(to_print) + if interaction_txt is not None: + add_to_txt(interaction_txt, ">>> Answer: \n" + res, with_print=False) + + if n > 1: + return [r["message"]["content"] for r in call_res["choices"]] + return res + + except Exception as e: + print("failed chat completion", e) + raise Exception("Failed to generate") + +def clear_messages(): + global existing_messages + existing_messages = [] + + +def format_finetune_prompt(task_name): + instruction_text = open('prompts/finetune_instructions_prompt.txt').read() + instruction_text = instruction_text.replace("TASK_NAME_TEMPLATE", task_name) + prompt_text = instruction_text + return prompt_text + +def format_finetune_prompt_codeonly(task_name): + instruction_text = open('prompts/finetune_instructions_prompt_codeonly.txt').read() + instruction_text = instruction_text.replace("TASK_NAME_TEMPLATE", task_name) + prompt_text = instruction_text + return prompt_text + +existing_messages = [] +def generate_feedback_completion_only(prompt, max_tokens=800, temperature=0.0, interaction_txt=None, retry_max=5, n=1): + """ use GPT-4 API """ + print("prompt size:", len(prompt)) + params = { + "model": model, + "max_tokens": 1200, + "temperature": temperature, + "prompt": prompt[-6000:], # in total 2048 + "n": n + } + + for retry in range(retry_max): + try: + if interaction_txt is not None: + add_to_txt(interaction_txt, ">>> Prompt: \n" + prompt, with_print=False) + call_res = openai.Completion.create(**params) + res = call_res["choices"][0]["text"] + + to_print = highlight(f"{res}", PythonLexer(), TerminalFormatter()) + print(to_print) + if interaction_txt is not None: + add_to_txt(interaction_txt, ">>> Answer: \n" + res, with_print=False) + + if n > 1: + return [r["text"] for r in call_res["choices"]] + return res + + except Exception as e: + print("failed chat completion", e) + # IPython.embed() + raise Exception("Failed to generate") diff --git a/misc/__init__.py b/misc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/misc/add_task_from_code.py b/misc/add_task_from_code.py new file mode 100644 index 0000000000000000000000000000000000000000..65202a3101532460767c2bdec4b1fb0915401ee7 --- /dev/null +++ b/misc/add_task_from_code.py @@ -0,0 +1,72 @@ +import re +import os + +import os +import json +import argparse + +import IPython +# "place-blue-on-line-ends": { +# "task-name": "place-blue-on-line-ends", +# "task-description": "Pick up each blue box and accurately place it at the end of a green line.", +# "assets-used": [ +# "line/line-template.urdf", +# "box/box-template.urdf" +# ] +# } +def extract_dict(res, task_name, prefix="new_task"): + """ parse task dictionary from the code itself """ + task_dict = {"task-name": task_name, + "assets-used": []} + pattern = r'\'(.*?).urdf' + asset_string = re.findall(pattern, res) + + pattern = r'"""(.*?)"""' + description_string = re.findall(pattern, res, re.DOTALL) + task_dict["assets-used"] = [file + ".urdf" for file in asset_string] + task_dict["task-description"] = description_string[0] + print(description_string[0]) + print(asset_string) + return task_dict + + +# remove some tasks from the list +parser = argparse.ArgumentParser() + +parser.add_argument( + "--files", "-f", type=str, default="exps" +) +args = parser.parse_args() + + +data_path = "prompts/data" +generated_task_path = os.path.join(data_path, "generated_tasks.json") +generated_task_code_path = os.path.join(data_path, "generated_task_codes.json") + +generated_tasks = json.load(open(generated_task_path)) +generated_task_codes = json.load(open(generated_task_code_path)) + + +task_names = args.files.split(",") +print("Task names:", task_names) + +for task_name in task_names: + + task_name = task_name.replace("_", "-") + task_name_py = task_name.replace("-", "_") + ".py" + file_path = "cliport/generated_tasks/" + task_name_py + if os.path.exists(file_path) and task_name not in generated_tasks: + print("add task:", task_name) + + code = open(file_path).read() + generated_tasks[task_name] = extract_dict(code, task_name) + + if task_name_py not in generated_task_codes: + generated_task_codes.append(task_name_py) + +with open(generated_task_code_path, "w") as outfile: + json.dump(generated_task_codes, outfile, indent=4) + +with open(generated_task_path, "w") as outfile: + json.dump(generated_tasks, outfile, indent=4) + diff --git a/misc/analyze_stats.py b/misc/analyze_stats.py new file mode 100644 index 0000000000000000000000000000000000000000..465c508f6ab2069ec3db4366045dc9569c6f2611 --- /dev/null +++ b/misc/analyze_stats.py @@ -0,0 +1,76 @@ +import matplotlib as mpl + +mpl.use("Agg") +import argparse +import os +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +import matplotlib +import IPython + +font = { + "size": 22, +} +matplotlib.rc("font", **font) +sns.set_context("paper", font_scale=2.0) + + +def mkdir_if_missing(dst_dir): + if not os.path.exists(dst_dir): + os.makedirs(dst_dir) + + +def save_figure(name, title=""): + if len(title) > 0: + plt.title(title) + plt.tight_layout() + print(f"output/output_figures/{name[:30]}") + mkdir_if_missing(f"output/output_figures/{name[:30]}") + plt.savefig(f"output/output_figures/{name[:30]}/output.png") + plt.clf() + + +def main(multirun_out, title): + dfs = [] + suffix = "" + run_num = 0 + + for rundir in (sorted(multirun_out.split(","))): + runpath = os.path.join('output/output_stats', rundir) + statspath = os.path.join(runpath, "eval_results.csv") + if os.path.exists(statspath): + run_num += 1 + df = pd.read_csv(statspath) + # print(df) + # df.drop(df.iloc[-1], axis=0, inplace=True) + # df.drop('diversity', axis=1) + dfs.append(df) + else: + print("skip:", statspath) + + # merge dfs, which have shared column names + df = pd.concat(dfs) + print(df.iloc) + title += f" run: {run_num} " + + # rewards + fig, ax = plt.subplots(figsize=(16, 8)) + sns_plot = sns.barplot( + data=df, x="metric", y="success", hue='model', errorbar=("sd", 1), palette="deep" + ) + + # label texts + for container in ax.containers: + ax.bar_label(container, label_type="center", fontsize="x-large", fmt="%.2f") + + # save plot + save_figure(f"{multirun_out}_{title}{suffix}", title) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--multirun_out", type=str) + parser.add_argument("--title", type=str, default="") + + args = parser.parse_args() + main(args.multirun_out, args.title) diff --git a/misc/analyze_stats_order.py b/misc/analyze_stats_order.py new file mode 100644 index 0000000000000000000000000000000000000000..66e0cc8204a49e54563305bedb980c9be4d629fd --- /dev/null +++ b/misc/analyze_stats_order.py @@ -0,0 +1,76 @@ +import matplotlib as mpl + +mpl.use("Agg") +import argparse +import os +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +import matplotlib +import IPython + +font = { + "size": 22, +} +matplotlib.rc("font", **font) +sns.set_context("paper", font_scale=2.0) + + +def mkdir_if_missing(dst_dir): + if not os.path.exists(dst_dir): + os.makedirs(dst_dir) + + +def save_figure(name, title=""): + if len(title) > 0: + plt.title(title) + plt.tight_layout() + print(f"output/output_figures/{name[:30]}") + mkdir_if_missing(f"output/output_figures/{name[:30]}") + plt.savefig(f"output/output_figures/{name[:30]}/output.png") + plt.clf() + + +def main(multirun_out, title): + dfs = [] + suffix = "" + run_num = 0 + + for rundir in (sorted(multirun_out.split(","))): + runpath = os.path.join('output/output_stats', rundir) + statspath = os.path.join(runpath, "eval_results.csv") + if os.path.exists(statspath): + run_num += 1 + df = pd.read_csv(statspath) + # print(df) + # df.drop(df.iloc[-1], axis=0, inplace=True) + # df.drop('diversity', axis=1) + dfs.append(df) + else: + print("skip:", statspath) + + # merge dfs, which have shared column names + df = pd.concat(dfs) + print(df.iloc) + title += f" run: {run_num} " + + # rewards + fig, ax = plt.subplots(figsize=(16, 8)) + sns_plot = sns.barplot( + data=df, x="metric", y="success", hue='model', errorbar=("sd", 1), palette="deep", hue_order=["gpt3", "gpt3-finetuned", "gpt3.5", "gpt3.5-finetuned", "gpt4"] + ) + + # label texts + for container in ax.containers: + ax.bar_label(container, label_type="center", fontsize="x-large", fmt="%.2f") + + # save plot + save_figure(f"{multirun_out}_{title}{suffix}", title) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--multirun_out", type=str) + parser.add_argument("--title", type=str, default="") + + args = parser.parse_args() + main(args.multirun_out, args.title) diff --git a/misc/command.sh b/misc/command.sh new file mode 100644 index 0000000000000000000000000000000000000000..6189718286fc32665587e0d39127111f1f646270 --- /dev/null +++ b/misc/command.sh @@ -0,0 +1,2 @@ +python misc/analyze_stats.py --multirun_out vanilla_task_generation_prompt_simple_zeroshot_03_08_2023_10:18:23,vanilla_task_generation_prompt_simple_03_08_2023_10:18:21,vanilla_task_generation_prompt_simple_singleprompt_03_08_2023_11:57:59 +python misc/analyze_stats_order.py --multirun_out topdown_finetune_exp_gpt3.5,topdown_finetune_exp_gpt4,topdown_finetune_exp_gpt3_ours,topdown_finetune_exp_gpt3.5_ours,topdown_finetune_exp_gpt3 \ No newline at end of file diff --git a/misc/compare_stats.py b/misc/compare_stats.py new file mode 100644 index 0000000000000000000000000000000000000000..c2a1e3272ee1fbe98fbe1280aa85822fce29255b --- /dev/null +++ b/misc/compare_stats.py @@ -0,0 +1,71 @@ +import matplotlib as mpl + +mpl.use("Agg") +import argparse +import os +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +import matplotlib +import IPython + +font = { + "size": 22, +} +matplotlib.rc("font", **font) +sns.set_context("paper", font_scale=2.0) + + +def mkdir_if_missing(dst_dir): + if not os.path.exists(dst_dir): + os.makedirs(dst_dir) + + +def save_figure(name, title=""): + print(f"output/output_figures/{name}.png") + if len(title) > 0: + plt.title(title) + plt.tight_layout() + mkdir_if_missing(f"output/output_figures/{name[:30]}") + plt.savefig(f"output/output_figures/{name[:30]}/output.png") + plt.clf() + + +def main(multirun_out, title): + dfs = [] + suffix = "" + run_num = 0 + + for rundir in (sorted(multirun_out.split(","))): + runpath = os.path.join('output/output_stats', rundir) + statspath = os.path.join(runpath, "eval_results.csv") + if os.path.exists(statspath): + run_num += 1 + df = pd.read_csv(statspath) + dfs.append(df) + + # merge dfs, which have shared column names + df = pd.concat(dfs) + title += f" run: {run_num} " + + # rewards + fig, ax = plt.subplots(figsize=(16, 8)) + sns_plot = sns.barplot( + data=df, x="task", y="success", hue='method', errorbar=("sd", 1), palette="deep" + ) + + # label texts + for container in ax.containers: + ax.bar_label(container, label_type="center", fontsize="x-large", fmt="%.2f") + ax.set_xticklabels(['\n'.join(str(xlabel.get_text()).split("-")) for xlabel in ax.get_xticklabels()]) + + # save plot + save_figure(f"{multirun_out}_{title}{suffix}", title) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--multirun_out", type=str) + parser.add_argument("--title", type=str, default="") + + args = parser.parse_args() + main(args.multirun_out, args.title) diff --git a/misc/compute_embedding_neighbor_tasks.py b/misc/compute_embedding_neighbor_tasks.py new file mode 100644 index 0000000000000000000000000000000000000000..96546131f18e78adba96fae954fa1e4fbc8e6759 --- /dev/null +++ b/misc/compute_embedding_neighbor_tasks.py @@ -0,0 +1,189 @@ +import torch +import torch.nn +import torchvision.models as models +from copy import deepcopy +import cv2 + +import cv2 +import numpy as np +import sys +import itertools +import os +import IPython +import matplotlib +matplotlib.use("Agg") + +import matplotlib.pyplot as plt +import pandas as pd + +import openai +from sklearn.manifold import TSNE +from sklearn.decomposition import PCA, KernelPCA +import seaborn as sns + +import time +from matplotlib.offsetbox import OffsetImage, AnnotationBbox +import colorsys +from torchvision import datasets +import argparse +import matplotlib.patheffects as PathEffects +from scipy.spatial import cKDTree + +sns.set_style("white") +sns.set_palette("muted") + +font = { + "size": 22, +} + +matplotlib.rc("font", **font) +sns.set_context("paper", font_scale=3.0) + + +plt_param = {'legend.fontsize': 60, + 'axes.labelsize': 80, + 'axes.titlesize':80, + 'font.size' : 80 , + 'xtick.labelsize':80, + 'ytick.labelsize':80, + 'lines.linewidth': 10, + 'lines.color': (0,0,0)} + +plt.rcParams.update(plt_param) + +openai.api_key ="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" +GPT_MODEL = "gpt4" +EMBEDDING_MODEL = "text-embedding-ada-002" +ORIGINAL_NAMES = [ + # demo conditioned + 'align-box-corner', + 'assembling-kits', + 'assembling-kits-easy', + 'block-insertion', + 'block-insertion-easy', + 'block-insertion-nofixture', + 'block-insertion-sixdof', + 'block-insertion-translation', + 'manipulating-rope', + 'packing-boxes', + 'palletizing-boxes', + 'place-red-in-green', + 'stack-block-pyramid', + 'sweeping-piles', + 'towers-of-hanoi', + 'gen-task', + # goal conditioned + 'align-rope', + 'assembling-kits-seq', + 'assembling-kits-seq-seen-colors', + 'assembling-kits-seq-unseen-colors', + 'assembling-kits-seq-full', + 'packing-shapes', + 'packing-boxes-pairs', + 'packing-boxes-pairs-seen-colors', + 'packing-boxes-pairs-unseen-colors', + 'packing-boxes-pairs-full', + 'packing-seen-google-objects-seq', + 'packing-unseen-google-objects-seq', + 'packing-seen-google-objects-group', + 'packing-unseen-google-objects-group', + 'put-block-in-bowl', + 'put-block-in-bowl-seen-colors', + 'put-block-in-bowl-unseen-colors', + 'put-block-in-bowl-full', + 'stack-block-pyramid-seq', + 'stack-block-pyramid-seq-seen-colors', + 'stack-block-pyramid-seq-unseen-colors', + 'stack-block-pyramid-seq-full', + 'separating-piles', + 'separating-piles-seen-colors', + 'separating-piles-unseen-colors', + 'separating-piles-full', + 'towers-of-hanoi-seq', + 'towers-of-hanoi-seq-seen-colors', + 'towers-of-hanoi-seq-unseen-colors', + 'towers-of-hanoi-seq-full', + ] + + +def normalize_numpy_array(arr): + return arr / (arr.max(axis=-1, keepdims=True) - arr.min(axis=-1, keepdims=True)) + + +def compute_embedding(response): + for _ in range(3): + try: + response_embedding = openai.Embedding.create( + model=EMBEDDING_MODEL, + input=response, + ) + + response_embedding = np.array(response_embedding["data"][0]['embedding']) + return response_embedding + except Exception as e: + print(e) + +def find_cliport_neighbor(kdtree, latents, label_sets): + closest_embeddings, closest_idx = kdtree.query(latents, k=78) + for i, idx in enumerate(closest_idx[0][1:]): + s_replaced = label_sets[idx].replace("_", "-") + if s_replaced in ORIGINAL_NAMES: + print(label_sets[idx], i) + + +def compute_neighbors(args): + fig_name=f'output/output_embedding/{args.file}' + # query: (response, embeddings) + latents = [] + class_labels = [] + label_sets = [] + + # chatgpt embedding + total_tasks = [os.path.join("cliport/tasks", x) for x in os.listdir("cliport/tasks")] + [os.path.join("cliport/generated_tasks", x) for x in os.listdir("cliport/generated_tasks")] + total_tasks = [t for t in total_tasks if 'pycache' not in t and 'init' not in t \ + and 'README' not in t and 'extended' not in t and 'gripper' not in t and 'primitive' not in t\ + and 'task.py' not in t and 'camera' not in t and 'seq' not in t and 'seen' not in t] + cache_embedding_path = "output/output_embedding/task_cache_embedding.npz" + cache_embedding = {} + + if os.path.exists(cache_embedding_path): + cache_embedding = dict(np.load(cache_embedding_path)) + + # print(total_tasks) + + for idx, task_name in enumerate(total_tasks): + if task_name in cache_embedding: + code_embedding = cache_embedding[task_name] + else: + code = open(task_name).read() + code_embedding = compute_embedding(code) + + latents.append(code_embedding) + label_sets.append(task_name.split("/")[-1][:-3]) + cache_embedding[task_name] = code_embedding + class_labels.append(idx) + + latents = np.array(latents) + # print("latents shape:", latents.shape) + # np.savez(cache_embedding_path, **cache_embedding) + + target_task_idx = label_sets.index(args.target_task) + kdtree = cKDTree(latents) + closest_embeddings, closest_idx = kdtree.query(latents[[target_task_idx]], k=args.num+1) + # print(latents.shape, args.num, target_task_idx, closest_idx,label_sets) + + print(f"closest tasks to {args.target_task}: {[label_sets[task] for task in closest_idx[0][1:]]}") + + # print(f"closest tasks in cliport original tasks: {find_cliport_neighbor(kdtree, latents[[target_task_idx]], label_sets)}") + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Generate chat-gpt embeddings") + """ + load task descriptions from the tasks folder and embed + """ + parser.add_argument("--file", type=str, default="task_embedding") + parser.add_argument("--target_task", type=str, default="align_box_corner") + parser.add_argument("--num", type=int, default=3) + + args = parser.parse_args() + compute_neighbors(args) \ No newline at end of file diff --git a/misc/concat_video.py b/misc/concat_video.py new file mode 100644 index 0000000000000000000000000000000000000000..12a73668aceb14cfe305470c0b115f13ab930117 --- /dev/null +++ b/misc/concat_video.py @@ -0,0 +1,45 @@ +import cv2 +import os +import imageio + +# Path to the directory containing the images +images_folder = 'output/blender_reder' + +# Get a list of image filenames in the directory +image_filenames = os.listdir(images_folder) +image_filenames.sort() # Sort filenames to ensure the correct order + +# Set the frame size (you can change these values as needed) +frame_width = 1920 +frame_height = 1080 + +# Create the video writer object +video_output_filename = 'output/blender_reder/output_video.mp4' +fps = 3.0 # Frames per second +fourcc = cv2.VideoWriter_fourcc(*'mp4v') +video_writer = cv2.VideoWriter(video_output_filename, fourcc, fps, (frame_width, frame_height)) + +# Iterate through the images and write each image as a frame into the video +IMG_NUM = 20 +frames = [] + +for image_filename in image_filenames[:IMG_NUM]: + image_path = os.path.join(images_folder, image_filename) + image = cv2.imread(image_path) + + # Resize the image to fit the video frame size + resized_image = cv2.resize(image, (frame_width, frame_height)) + + # Write the image as a frame to the video + video_writer.write(resized_image) + frames.append(resized_image) + +# Release the video writer +video_writer.release() + +print("Video concatenation complete.") + +with imageio.get_writer("output/blender_reder/output_video.gif", mode="I") as writer: + for idx, frame in enumerate(frames): + print("Adding frame to GIF file: ", idx + 1) + writer.append_data(frame[...,[2,1,0]]) \ No newline at end of file diff --git a/misc/copy_all_videos.py b/misc/copy_all_videos.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/misc/fewshot_instructions_prompt.txt b/misc/fewshot_instructions_prompt.txt new file mode 100644 index 0000000000000000000000000000000000000000..0e0a71e3e8b8660fcb2fb4ae772b025323a5bbc0 --- /dev/null +++ b/misc/fewshot_instructions_prompt.txt @@ -0,0 +1,114 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task. My goal is to design diverse and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Use only these assets in the task and code design. +""" +insertion/: +ell.urdf fixture.urdf + +bowl/: +bowl.urdf + +box/: +box-template.urdf + +stacking/: +block.urdf stand.urdf + +zone/: +zone.obj zone.urdf + +pallet/: +pallet.obj pallet.urdf + +ball/: +ball-template.urdf + +cylinder/: +cylinder-template.urdf + +bowl/: +bowl.urdf + +# assets not for picking +corner/: +corner-template.urdf + +line/: +single-green-line-template.urdf + +container/: +container-template.urdf +""" + +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1) + self.lang_goals.append(self.lang_template) + + # Colors of distractor objects. + # IMPORTANT: RETRIEVE THE ACTUAL COLOR VALUES + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" + +========= +Please describe the task "TASK_NAME_TEMPLATE" in natural languages and format the answer in a python dictionary with keys "task-name" and value type string, "task-description" (one specific sentence) and value type string, and "assets-used" and value type list of strings. + +========= +Now write the pybullet simulation code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. diff --git a/misc/finetune_data.csv b/misc/finetune_data.csv new file mode 100644 index 0000000000000000000000000000000000000000..ca30cfad0f21c3239681ef42476b295c4248f3cb --- /dev/null +++ b/misc/finetune_data.csv @@ -0,0 +1,8351 @@ +prompt,completion +" + Instructions: ========= +Please describe the task ""align-box-corner"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""align-box-corner"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```align-box-corner: {'assets-used': ['box/box-template.urdf', 'corner/corner-template.urdf'], 'task-description': 'pick up the randomly sized box and align one of its corners to the L-shaped marker on the tabletop..', 'task-name': 'align-box-corner'} + +### + +Implementation: + ```python +import os +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class AlignBoxCorner(Task): + """"""Pick up the randomly sized box and align one of its corners to the L-shaped marker on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 3 + self.lang_template = ""align the brown box with the green corner"" + self.task_completed_desc = ""done with alignment"" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Generate randomly shaped box. + box_size = self.get_random_size(0.05, 0.15, 0.05, 0.15, 0.01, 0.06) + + # Add corner. + dimx = (box_size[0] / 2 - 0.025 + 0.0025, box_size[0] / 2 + 0.0025) + dimy = (box_size[1] / 2 + 0.0025, box_size[1] / 2 - 0.025 + 0.0025) + corner_template = 'corner/corner-template.urdf' + replace = {'DIMX': dimx, 'DIMY': dimy} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + corner_urdf = self.fill_template(corner_template, replace) + corner_size = (box_size[0], box_size[1], 0) + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, 'fixed') + + # Add possible placing poses. + theta = utils.quatXYZW_to_eulerXYZ(corner_pose[1])[2] + fip_rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta + np.pi)) + pose1 = (corner_pose[0], fip_rot) + alt_x = (box_size[0] / 2) - (box_size[1] / 2) + alt_y = (box_size[1] / 2) - (box_size[0] / 2) + alt_pos = (alt_x, alt_y, 0) + alt_rot0 = utils.eulerXYZ_to_quatXYZW((0, 0, np.pi / 2)) + alt_rot1 = utils.eulerXYZ_to_quatXYZW((0, 0, 3 * np.pi / 2)) + pose2 = utils.multiply(corner_pose, (alt_pos, alt_rot0)) + pose3 = utils.multiply(corner_pose, (alt_pos, alt_rot1)) + + # Add box. + box_template = 'box/box-template.urdf' + + # IMPORTANT: REPLACE THE TEMPLATE URDF + box_urdf = self.fill_template(box_template, {'DIM': np.float32(box_size)}) + box_pose = self.get_random_pose(env, box_size) + box_id = env.add_object(box_urdf, box_pose) + self.color_random_brown(box_id) + + # Goal: box is aligned with corner (1 of 4 possible poses). + self.add_goal(objs=[box_id], matches=np.int32([[1, 1, 1, 1]]), targ_poses=[corner_pose, pose1, pose2, pose3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, symmetries=[2 * np.pi], + language_goal=self.lang_template) +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""align-rope"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""align-rope"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```align-rope: {'assets-used': ['square/square-template.urdf'], 'task-description': 'manipulate a deformable rope to connect its end-points between two corners of a 3-sided square.', 'task-name': 'align-rope'} + +### + +Implementation: + ```python +import os + +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.task import Task +from cliport.utils import utils + +import random +import pybullet as p + + +class AlignRope(Task): + """"""Manipulate a deformable rope to connect its end-points between two + corners of a 3-sided square."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""align the rope from {direction}"" + self.task_completed_desc = ""done aligning the rope."" + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + n_parts = 20 + radius = 0.005 + length = 2 * radius * n_parts * np.sqrt(2) + + # Add 3-sided square. + square_size = (length, length, 0) + square_pose = self.get_random_pose(env, square_size) + square_template = 'square/square-template.urdf' + replace = {'DIM': (length,), 'HALF': (np.float32(length) / 2 - 0.005,)} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(square_template, replace) + env.add_object(urdf, square_pose, 'fixed') + + # Get four corner points of square. + corner0 = ( length / 2, length / 2, 0.001) + corner1 = (-length / 2, length / 2, 0.001) + corner2 = ( length / 2, -length / 2, 0.001) + corner3 = (-length / 2, -length / 2, 0.001) + + corner0 = utils.apply(square_pose, corner0) + corner1 = utils.apply(square_pose, corner1) + corner2 = utils.apply(square_pose, corner2) + corner3 = utils.apply(square_pose, corner3) + + # Four possible alignment tasks. + task_descs = [ + ((corner0, corner1), ""front left tip to front right tip""), + ((corner0, corner2), ""front right tip to back right corner""), + ((corner1, corner3), ""front left tip to back left corner""), + ((corner3, corner2), ""back right corner to back left corner"") + ] + chosen_task = np.random.choice(len(task_descs), 1)[0] + (corner_0, corner_1), direction = task_descs[chosen_task] + + # IMPORTANT: use `make_ropes` to add cable (series of articulated small blocks). + objects, targets, matches = self.make_ropes(env, corners=(corner_0, corner_1)) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1., + language_goal=[self.lang_template.format(direction=direction)] * len(objects)) + + # wait for the scene to settle down + for i in range(480): + p.stepSimulation()<|endoftext|>" +" + Instructions: ========= +Please describe the task ""assembling-kits"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""assembling-kits"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```assembling-kits: {'assets-used': ['kitting/kit.urdf', 'kitting/object-template.urdf'], 'task-description': 'pick up different objects and arrange them on a board marked with corresponding silhouettes.', 'task-name': 'assembling-kits'} + +### + +Implementation: + ```python +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class AssemblingKits(Task): + """"""pick up different objects and arrange them on a board marked with corresponding silhouettes."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.homogeneous = False + + self.lang_template = ""put all the blocks inside the holes they fit in"" + self.task_completed_desc = ""done assembling blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add kit. + kit_size = (0.28, 0.2, 0.005) + kit_urdf = 'kitting/kit.urdf' + kit_pose = self.get_random_pose(env, kit_size) + env.add_object(kit_urdf, kit_pose, 'fixed') + + n_objects = 5 + obj_shapes = self.get_kitting_shapes(n_objects) + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['red'] + ] + + # Build kit. + targets = [] + targ_pos = [[-0.09, 0.045, 0.0014], [0, 0.045, 0.0014], + [0.09, 0.045, 0.0014], [-0.045, -0.045, 0.0014], + [0.045, -0.045, 0.0014]] + template = 'kitting/object-template.urdf' + + for i in range(n_objects): + shape = os.path.join(self.assets_root, 'kitting', + f'{obj_shapes[i]:02d}.obj') + scale = [0.003, 0.003, 0.0001] # .0005 + pos = utils.apply(kit_pose, targ_pos[i]) + theta = np.random.rand() * 2 * np.pi + rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': [0.2, 0.2, 0.2]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + env.add_object(urdf, (pos, rot), 'fixed') + targets.append((pos, rot)) + + # Add objects. + objects, matches = self.make_kitting_objects(env, targets=targets, obj_shapes=obj_shapes, n_objects=n_objects, colors=colors) + matches = np.int32(matches) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + <|endoftext|>" +" + Instructions: ========= +Please describe the task ""manipulating-rope"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""manipulating-rope"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```manipulating-rope: {'assets-used': ['square/square-template.urdf'], 'task-description': 'rearrange a deformable rope such that it connects the two endpoints of a 3-sided square.', 'task-name': 'manipulating-rope'} + +### + +Implementation: + ```python +import os + +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.task import Task +from cliport.utils import utils + +import pybullet as p + + +class ManipulatingRope(Task): + """"""rearrange a deformable rope such that it connects the two endpoints of a 3-sided square."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""manipulate the rope to complete the square"" + self.task_completed_desc = ""done manipulating the rope."" + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + n_parts = 20 + radius = 0.005 + length = 2 * radius * n_parts * np.sqrt(2) + + # Add 3-sided square. + square_size = (length, length, 0) + square_pose = self.get_random_pose(env, square_size) + square_template = 'square/square-template.urdf' + + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + replace = {'DIM': (length,), 'HALF': (np.float32(length) / 2 - 0.005,)} + urdf = self.fill_template(square_template, replace) + env.add_object(urdf, square_pose, 'fixed') + + # compute corners + corner0 = (length / 2, length / 2, 0.001) + corner1 = (-length / 2, length / 2, 0.001) + corner_0 = utils.apply(square_pose, corner0) + corner_1 = utils.apply(square_pose, corner1) + + # IMPORTANT: use `make_ropes` to add cable (series of articulated small blocks). + objects, targets, matches = self.make_ropes(env, corners=(corner_0, corner_1)) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1., lang_goal=self.lang_template) + + for i in range(480): + p.stepSimulation() +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""packing-boxes"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""packing-boxes"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```packing-boxes: {'assets-used': ['container/container-template.urdf', 'box/box-template.urdf'], 'task-description': 'pick up randomly sized boxes and place them tightly into a container.', 'task-name': 'packing-boxes'} + +### + +Implementation: + ```python +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +import pybullet as p + + +class PackingBoxes(Task): + """"""pick up randomly sized boxes and place them tightly into a container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""pack all the boxes inside the brown box"" + self.task_completed_desc = ""done packing boxes."" + + self.zone_bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.08]]) + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container box. + zone_size = self.get_random_size(0.05, 0.3, 0.05, 0.3, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + margin = 0.01 + min_object_dim = 0.05 + bboxes = [] + + # Split container space with KD trees. + stack_size = np.array(zone_size) + stack_size[0] -= 0.01 + stack_size[1] -= 0.01 + root_size = (0.01, 0.01, 0) + tuple(stack_size) + root = utils.TreeNode(None, [], bbox=np.array(root_size)) + utils.KDTree(root, min_object_dim, margin, bboxes) + + colors = [utils.COLORS[c] for c in utils.COLORS if c != 'brown'] + + # Add objects in container. + object_ids = [] + bboxes = np.array(bboxes) + object_template = 'box/box-template.urdf' + + # Compute object points that are needed for zone + for bbox in bboxes: + size = bbox[3:] - bbox[:3] + position = size / 2. + bbox[:3] + position[0] += -zone_size[0] / 2 + position[1] += -zone_size[1] / 2 + pose = (position, (0, 0, 0, 1)) + pose = utils.multiply(zone_pose, pose) + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(object_template, {'DIM': size}) + icolor = np.random.choice(range(len(colors)), 1).squeeze() + box_id = env.add_object(urdf, pose, color=colors[icolor]) + object_ids.append(box_id) + + # Randomly select object in box and save ground truth pose. + object_volumes = [] + true_poses = [] + + for object_id in object_ids: + true_pose = p.getBasePositionAndOrientation(object_id) + object_size = p.getVisualShapeData(object_id)[0][3] + object_volumes.append(np.prod(np.array(object_size) * 100)) + pose = self.get_random_pose(env, object_size) + p.resetBasePositionAndOrientation(object_id, pose[0], pose[1]) + true_poses.append(true_pose) + + self.add_goal(objs=object_ids, matches=np.eye(len(object_ids)), targ_poses=true_poses, replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""palletizing-boxes"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""palletizing-boxes"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```palletizing-boxes: {'assets-used': ['pallet/pallet.urdf', 'box/box-template.urdf'], 'task-description': 'pick up homogeneous fixed-sized boxes and stack them in transposed layers on the pallet.', 'task-name': 'palletizing-boxes'} + +### + +Implementation: + ```python +import os +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PalletizingBoxes(Task): + """"""Pick up homogeneous fixed-sized boxes and stack them in transposed layers on the pallet."""""" + + def __init__(self): + super().__init__() + self.max_steps = 30 + self.lang_template = ""stack all the boxes on the pallet"" + self.task_completed_desc = ""done stacking boxes."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + zone_size = (0.3, 0.25, 0.25) + zone_urdf = 'pallet/pallet.urdf' + rotation = utils.eulerXYZ_to_quatXYZW((0, 0, 0)) + zone_pose = ((0.5, 0.25, 0.02), rotation) + env.add_object(zone_urdf, zone_pose, 'fixed') + + # Add stack of boxes on pallet. + margin = 0.01 + object_ids = [] + + # x, y, z dimensions for the asset size + stack_size = (0.19, 0.19, 0.19) + box_template = 'box/box-template.urdf' + stack_dim = np.int32([2, 3, 3]) + + box_size = (stack_size - (stack_dim - 1) * margin) / stack_dim + for z in range(stack_dim[2]): + + # Transpose every layer. + stack_dim[0], stack_dim[1] = stack_dim[1], stack_dim[0] + box_size[0], box_size[1] = box_size[1], box_size[0] + + # IMPORTANT: Compute object points and store as a dictionary for the `goal` + for y in range(stack_dim[1]): + for x in range(stack_dim[0]): + position = list((x + 0.5, y + 0.5, z + 0.5) * box_size) + position[0] += x * margin - stack_size[0] / 2 + position[1] += y * margin - stack_size[1] / 2 + position[2] += z * margin + 0.03 + pose = (position, (0, 0, 0, 1)) + pose = utils.multiply(zone_pose, pose) + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(box_template, {'DIM': box_size}) + box_id = env.add_object(urdf, pose) + object_ids.append(box_id) + self.color_random_brown(box_id) + + # Randomly select top box on pallet and save ground truth pose. + targets = [] + self.steps = [] + boxes = object_ids[:] # make copy + while boxes: + _, height, object_mask = self.get_true_image(env) + top = np.argwhere(height > (np.max(height) - 0.03)) + rpixel = top[int(np.floor(np.random.random() * len(top)))] # y, x + box_id = int(object_mask[rpixel[0], rpixel[1]]) + if box_id in boxes: + position, rotation = p.getBasePositionAndOrientation(box_id) + rposition = np.float32(position) + np.float32([0, -10, 0]) + p.resetBasePositionAndOrientation(box_id, rposition, rotation) + self.steps.append(box_id) + targets.append((position, rotation)) + boxes.remove(box_id) + + self.steps.reverse() # Time-reversed depalletizing. + self.add_goal(objs=object_ids, matches=np.eye(len(object_ids)), targ_poses=targets, replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) + self.spawn_box() + + def reward(self): + reward, info = super().reward() + self.spawn_box() + return reward, info<|endoftext|>" +" + Instructions: ========= +Please describe the task ""place-red-in-green"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""place-red-in-green"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```place-red-in-green: {'assets-used': ['bowl/bowl.urdf', 'stacking/block.urdf'], 'task-description': 'pick up the red blocks and place them into the green bowls amidst other objects.', 'task-name': 'place-red-in-green'} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """"""pick up the red blocks and place them into the green bowls amidst other objects."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""put the red blocks in a green bowl"" + self.task_completed_desc = ""done placing blocks in bowls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""stack-block-pyramid"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""stack-block-pyramid"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```stack-block-pyramid: {'assets-used': ['stacking/stand.urdf', 'stacking/block.urdf'], 'task-description': 'sequentially stack 6 blocks into a pyramid of 3-2-1 with rainbow colored ordering.', 'task-name': 'stack-block-pyramid'} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """"""Build a pyramid of colored blocks in a color sequence"""""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = ""make the {row} row with {blocks}"" + self.task_completed_desc = ""done stacking block pyramid."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks=""the green, blue and purple blocks"", row=""bottom"") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks=""the yellow and orange blocks"", row=""middle"") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks=""the red block"", row=""top"") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""sweeping-piles"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""sweeping-piles"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```sweeping-piles: {'assets-used': ['zone/zone.urdf', 'block/small.urdf'], 'task-description': 'push piles of small objects into a target goal zone marked on the tabletop.', 'task-name': 'sweeping-piles'} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """"""Push piles of small objects into a target goal zone marked on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""push the pile of blocks into the green square"" + self.task_completed_desc = ""done sweeping."" + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""build-wheel"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""build-wheel"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```build-wheel: {'task-name': 'build-wheel', 'task-description': 'Construct a wheel using blocks and a sphere. First, position eight blocks in a circular layout on the tabletop. Each block should be touching its two neighbors and colored in alternating red and blue. Then place a green sphere in the center of the circular layout, completing the wheel.', 'assets-used': ['block/block.urdf', 'sphere/sphere.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildWheel(Task): + """"""Construct a wheel using blocks and a sphere."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""Construct a wheel using blocks and a sphere. First, position eight blocks in a circular layout on the tabletop. Each block should be touching its two neighbors and colored in alternating red and blue. Then place a green sphere in the center of the circular layout, completing the wheel."" + self.task_completed_desc = ""done building wheel."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue']] + blocks = [] + for i in range(8): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i % 2]) + blocks.append(block_id) + + # Add sphere. + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + sphere_color = utils.COLORS['green'] + sphere_pose = ((0.5, 0.0, 0.0), (0,0,0,1)) # fixed pose + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=sphere_color) + + # Goal: blocks are arranged in a circle and sphere is in the center. + circle_radius = 0.1 + circle_center = (0, 0, block_size[2] / 2) + angles = np.linspace(0, 2 * np.pi, 8, endpoint=False) + block_poses = [(circle_center[0] + circle_radius * np.cos(angle), + circle_center[1] + circle_radius * np.sin(angle), + circle_center[2]) for angle in angles] + block_poses = [(utils.apply(sphere_pose, pos), sphere_pose[1]) for pos in block_poses] + self.add_goal(objs=blocks, matches=np.ones((8, 8)), targ_poses=block_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=8 / 9, language_goal=self.lang_template) + + # Goal: sphere is in the center of the blocks. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[sphere_pose], replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1 / 9, language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""push-piles-into-letter"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""push-piles-into-letter"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```push-piles-into-letter: {'task-name': 'push-piles-into-letter', 'task-description': 'Push piles of small objects into a target goal zone shaped in some letters.', 'assets-used': ['kitting/kit.urdf', 'kitting/object-template-nocollision.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildWheel(Task): + """"""Construct a wheel using blocks and a sphere."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""Construct a wheel using blocks and a sphere. First, position eight blocks in a circular layout on the tabletop. Each block should be touching its two neighbors and colored in alternating red and blue. Then place a green sphere in the center of the circular layout, completing the wheel."" + self.task_completed_desc = ""done building wheel."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue']] + blocks = [] + for i in range(8): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i % 2]) + blocks.append(block_id) + + # Add sphere. + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + sphere_color = utils.COLORS['green'] + sphere_pose = ((0.5, 0.0, 0.0), (0,0,0,1)) # fixed pose + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=sphere_color) + + # Goal: blocks are arranged in a circle and sphere is in the center. + circle_radius = 0.1 + circle_center = (0, 0, block_size[2] / 2) + angles = np.linspace(0, 2 * np.pi, 8, endpoint=False) + block_poses = [(circle_center[0] + circle_radius * np.cos(angle), + circle_center[1] + circle_radius * np.sin(angle), + circle_center[2]) for angle in angles] + block_poses = [(utils.apply(sphere_pose, pos), sphere_pose[1]) for pos in block_poses] + self.add_goal(objs=blocks, matches=np.ones((8, 8)), targ_poses=block_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=8 / 9, language_goal=self.lang_template) + + # Goal: sphere is in the center of the blocks. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[sphere_pose], replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1 / 9, language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""connect-boxes-with-rope"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""connect-boxes-with-rope"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```connect-boxes-with-rope: {'task-name': 'connect-boxes-with-rope', 'task-description': 'Connect two colored blocks with ropes.', 'assets-used': ['block/block.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import IPython + +class ConnectBoxesWithRope(Task): + """"""Connect two colored blocks with ropes."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""connect the {color1} and {color2} blocks with the rope."" + self.task_completed_desc = ""done connecting."" + self.additional_reset() + self.pos_eps = 0.04 # higher tolerance + + def reset(self, env): + super().reset(env) + colors = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'] + blocks = [] + target_colors = np.random.choice(colors, 2, replace=False) + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + corner_poses = [] + + for color in colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + if color in target_colors: + corner_poses.append(block_pose) + + dist = np.linalg.norm(np.array(corner_poses[0][0])-np.array(corner_poses[1][0])) + n_parts = int(20 * dist / 0.4) + + # IMPORTANT: use `make_ropes` to add cable (series of articulated small blocks). + objects, targets, matches = self.make_ropes(env, corners=(corner_poses[0][0], corner_poses[1][0]), n_parts=n_parts) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1., + language_goal=self.lang_template.format(color1=target_colors[0], color2=target_colors[1])) + + # wait for the scene to settle down + for i in range(600): + p.stepSimulation()<|endoftext|>" +" + Instructions: ========= +Please describe the task ""build-car"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""build-car"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```build-car: {'task-name': 'build-car', 'task-description': 'Construct a simple car structure using blocks and cylinders.', 'assets-used': ['block/block.urdf', 'ball/ball-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildCar(Task): + """"""Construct a simple car structure using blocks and cylinders."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""Construct a simple car structure using blocks and cylinders. "" \ + ""Firstly, create the base of the car by positioning two red blocks side by side. "" \ + ""Then, add the car body by stacking a blue block on top of the base. "" \ + ""For the wheels, place a black cylinder on each side of the base blocks."" + self.task_completed_desc = ""done building car."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + car_pose = ((0.5, 0.0, 0.0), (0,0,0,1)) # fixed pose + base_length = 0.04 + self.add_corner_anchor_for_pose(env, car_pose) + + # Add base blocks. Use box template so that we can change its size. + base_size = (0.02, 0.04, 0.02) + base_block_urdf = ""box/box-template.urdf"" + base_block_urdf = self.fill_template(base_block_urdf, {'DIM': base_size}) + anchor_base_poses = [(utils.apply(car_pose, (base_length / 2, base_length / 2, 0.001)), car_pose[1]), + (utils.apply(car_pose, (-base_length / 2, base_length / 2, 0.001)), car_pose[1])] + base_blocks = [] + + for idx in range(2): + base_block_pose = self.get_random_pose(env, base_size) + base_block_id = env.add_object(base_block_urdf, base_block_pose, color=utils.COLORS['red']) + base_blocks.append(base_block_id) + + # Add car body block. + body_size = (0.04, 0.02, 0.02) # x, y, z dimensions for the asset size + body_block_urdf = ""box/box-template.urdf"" + body_block_urdf = self.fill_template(body_block_urdf, {'DIM': body_size}) + body_block_pose = self.get_random_pose(env, body_size) + body_block_id = env.add_object(body_block_urdf, body_block_pose, color=utils.COLORS['blue']) + anchor_body_poses = [car_pose] + + wheel_length = 0.12 + anchor_wheel_poses = [(utils.apply(car_pose, ( wheel_length / 2, wheel_length / 2, 0.001)), car_pose[1]), + (utils.apply(car_pose, (-wheel_length / 2, wheel_length / 2, 0.001)), car_pose[1]), + (utils.apply(car_pose, ( wheel_length / 2, -wheel_length / 2, 0.001)), car_pose[1]), + (utils.apply(car_pose, (-wheel_length / 2, -wheel_length / 2, 0.001)), car_pose[1])] + + # Add wheels. + wheel_size = (0.02, 0.02, 0.02) # x, y, z dimensions for the asset size + wheel_urdf = 'cylinder/cylinder-template.urdf' + wheel_urdf = self.fill_template(wheel_urdf, {'DIM': wheel_size}) + + wheels = [] + for idx in range(4): + wheel_pose = self.get_random_pose(env, wheel_size) + wheel_id = env.add_object(wheel_urdf, wheel_pose, color=utils.COLORS['black']) + wheels.append(wheel_id) + + # Goal: Firstly, create the base of the car by positioning two red blocks side by side. + self.add_goal(objs=base_blocks, + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./3, + language_goal=""Firstly, create the base of the car by positioning two red blocks side by side."") + + # Then, add the car body by stacking a blue block on top of the base. + self.add_goal(objs=[body_block_id], + matches=np.ones((1, 1)), + targ_poses=anchor_body_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./3, + language_goal=""Then, add the car body by stacking a blue block on top of the base."") + + # For the wheels, place a black cylinder on each side of the base blocks. + self.add_goal(objs=wheels, + matches=np.ones((4, 4)), + targ_poses=anchor_wheel_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./3, + language_goal=""For the wheels, place a black cylinder on each side of the base blocks."") + +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""manipulating-two-ropes"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""manipulating-two-ropes"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```manipulating-two-ropes: {'task-name': 'manipulating-two-ropes', 'task-description': 'rearrange the red and blue deformable ropes such that it connects the two endpoints of a 3-sided square of corresponding color.', 'assets-used': ['square/square-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula + +class ManipulatingTwoRopes(Task): + """"""rearrange the red and blue deformable ropes such that it connects the two endpoints of a 3-sided square of corresponding color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""rearrange the {color_name} rope such that it connects the two endpoints of a 3-sided square of corresponding color."" + self.task_completed_desc = ""done manipulating two ropes."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + n_parts = 20 + radius = 0.005 + length = 2 * radius * n_parts * np.sqrt(2) + + # Add 3-sided square for the red rope. + color_list = ['red', 'blue'] + for color_name in color_list: + square_size = (length, length, 0) + square_pose = self.get_random_pose(env, square_size) + square_template = 'square/square-template.urdf' + + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + replace = {'DIM': (length,), 'HALF': (np.float32(length) / 2 - 0.005,)} + urdf = self.fill_template(square_template, replace) + env.add_object(urdf, square_pose, 'fixed', color=utils.COLORS[color_name]) + + # compute corners + corner0 = (length / 2, length / 2, 0.001) + corner1 = (-length / 2, length / 2, 0.001) + corner_0 = utils.apply(square_pose, corner0) + corner_1 = utils.apply(square_pose, corner1) + + # IMPORTANT: use `make_ropes` to add cable (series of articulated small blocks). + objects, targets, matches = self.make_ropes(env, corners=(corner_0, corner_1), color_name=color_name) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1. / len(color_list), + language_goal=self.lang_template.format(color_name=color_name)) + + print(f""len of languages: {len(self.lang_goals)} obj:{len(objects)}"") + for i in range(480): + p.stepSimulation() +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""insert-sphere-into-container"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""insert-sphere-into-container"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```insert-sphere-into-container: {'task-name': 'insert-sphere-into-container', 'task-description': 'Pick up a blue sphere and place it into an open container.', 'assets-used': ['sphere/sphere.urdf', 'container/container-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertSphereIntoContainer(Task): + """"""Pick up a blue sphere and place it into an open container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""pick up a blue sphere and place it into an open container"" + self.task_completed_desc = ""done inserting sphere into container."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + # x, y, z dimensions for the asset size + container_size = (0.1, 0.1, 0.1) + container_pose = self.get_random_pose(env, container_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + container_id = env.add_object(container_urdf, container_pose, 'fixed') + + # Add sphere. + # x, y, z dimensions for the asset size + sphere_size = (0.04, 0.04, 0.04) + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_urdf = 'sphere/sphere.urdf' + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS['blue']) + + # Goal: the blue sphere is in the container. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""insert-cylinder-in-container"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""insert-cylinder-in-container"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```insert-cylinder-in-container: {'task-name': 'insert-cylinder-in-container', 'task-description': 'Pick up a blue cylindrical block and place it into an empty container.', 'assets-used': ['cylinder/cylinder-template.urdf', 'container/container-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertSphereIntoContainer(Task): + """"""Pick up a blue sphere and place it into an open container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""pick up a blue sphere and place it into an open container"" + self.task_completed_desc = ""done inserting sphere into container."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + # x, y, z dimensions for the asset size + container_size = (0.1, 0.1, 0.1) + container_pose = self.get_random_pose(env, container_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + container_id = env.add_object(container_urdf, container_pose, 'fixed') + + # Add sphere. + # x, y, z dimensions for the asset size + sphere_size = (0.04, 0.04, 0.04) + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_urdf = 'sphere/sphere.urdf' + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS['blue']) + + # Goal: the blue sphere is in the container. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""build-bridge"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""build-bridge"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```build-bridge: {'task-name': 'build-bridge', 'task-description': 'Construct a bridge using two yellow blocks and three blue blocks. Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between. Then, place the blue block horizontally on top of the yellow blocks.', 'assets-used': ['block/block.urdf', 'ball/ball-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildBridge(Task): + """"""Construct a bridge using two yellow blocks and three blue blocks. + Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between. + Then, place the blue block horizontally on top of the yellow blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""build a bridge using four yellow blocks and one long blue block"" + self.task_completed_desc = ""done building bridge."" + + def reset(self, env): + super().reset(env) + + # Add yellow blocks. + base_length = 0.04 + base_size = (base_length, base_length, base_length) + base_block_urdf = ""box/box-template.urdf"" + bridge_pose = ((0.5, 0.0, 0.0), (0, 0, 0, 1)) # fixed pose + self.add_corner_anchor_for_pose(env, bridge_pose) + + base_block_urdf = self.fill_template(base_block_urdf, {'DIM': base_size}) + anchor_base_poses = [(utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.041)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.041)), bridge_pose[1])] + base_blocks = [] + + for idx in range(4): + base_block_pose = self.get_random_pose(env, base_size) + base_block_id = env.add_object(base_block_urdf, base_block_pose, color=utils.COLORS['yellow']) + base_blocks.append(base_block_id) + + # Add car body block. + body_size = (0.12, 0.04, 0.02) # x, y, z dimensions for the asset size + body_block_urdf = ""box/box-template.urdf"" + body_block_urdf = self.fill_template(body_block_urdf, {'DIM': body_size}) + body_block_pose = self.get_random_pose(env, body_size) + body_block_id = env.add_object(body_block_urdf, body_block_pose, color=utils.COLORS['blue']) + anchor_body_poses = [bridge_pose] + + # Goal: Firstly, create the base of the car by positioning two red blocks side by side. + self.add_goal(objs=base_blocks[:2], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + self.add_goal(objs=base_blocks[2:], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./2, + language_goal=""Place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + # Then, add the car body by stacking a blue block on top of the base. + self.add_goal(objs=[body_block_id], + matches=np.ones((1, 1)), + targ_poses=anchor_body_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Then, place the blue block horizontally on top of the yellow blocks."")<|endoftext|>" +" + Instructions: ========= +Please describe the task ""insert-ell-in-fixture"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""insert-ell-in-fixture"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```insert-ell-in-fixture: {'task-name': 'insert-ell-in-fixture', 'task-description': 'Pick up an Ell shaped block and insert it into a fixture on the tabletop.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildBridge(Task): + """"""Construct a bridge using two yellow blocks and three blue blocks. + Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between. + Then, place the blue block horizontally on top of the yellow blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""build a bridge using four yellow blocks and one long blue block"" + self.task_completed_desc = ""done building bridge."" + + def reset(self, env): + super().reset(env) + + # Add yellow blocks. + base_length = 0.04 + base_size = (base_length, base_length, base_length) + base_block_urdf = ""box/box-template.urdf"" + bridge_pose = ((0.5, 0.0, 0.0), (0, 0, 0, 1)) # fixed pose + self.add_corner_anchor_for_pose(env, bridge_pose) + + base_block_urdf = self.fill_template(base_block_urdf, {'DIM': base_size}) + anchor_base_poses = [(utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.041)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.041)), bridge_pose[1])] + base_blocks = [] + + for idx in range(4): + base_block_pose = self.get_random_pose(env, base_size) + base_block_id = env.add_object(base_block_urdf, base_block_pose, color=utils.COLORS['yellow']) + base_blocks.append(base_block_id) + + # Add car body block. + body_size = (0.12, 0.04, 0.02) # x, y, z dimensions for the asset size + body_block_urdf = ""box/box-template.urdf"" + body_block_urdf = self.fill_template(body_block_urdf, {'DIM': body_size}) + body_block_pose = self.get_random_pose(env, body_size) + body_block_id = env.add_object(body_block_urdf, body_block_pose, color=utils.COLORS['blue']) + anchor_body_poses = [bridge_pose] + + # Goal: Firstly, create the base of the car by positioning two red blocks side by side. + self.add_goal(objs=base_blocks[:2], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + self.add_goal(objs=base_blocks[2:], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./2, + language_goal=""Place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + # Then, add the car body by stacking a blue block on top of the base. + self.add_goal(objs=[body_block_id], + matches=np.ones((1, 1)), + targ_poses=anchor_body_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Then, place the blue block horizontally on top of the yellow blocks."")<|endoftext|>" +" + Instructions: ========= +Please describe the task ""build-block-tower-on-pallet"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""build-block-tower-on-pallet"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```build-block-tower-on-pallet: {'task-name': 'build-block-tower-on-pallet', 'task-description': 'Pick up six blocks in two colors (three red and three blue) and stack them in alternating colors on the pallet. The tower should start with a red block at the bottom.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildBridge(Task): + """"""Construct a bridge using two yellow blocks and three blue blocks. + Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between. + Then, place the blue block horizontally on top of the yellow blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""build a bridge using four yellow blocks and one long blue block"" + self.task_completed_desc = ""done building bridge."" + + def reset(self, env): + super().reset(env) + + # Add yellow blocks. + base_length = 0.04 + base_size = (base_length, base_length, base_length) + base_block_urdf = ""box/box-template.urdf"" + bridge_pose = ((0.5, 0.0, 0.0), (0, 0, 0, 1)) # fixed pose + self.add_corner_anchor_for_pose(env, bridge_pose) + + base_block_urdf = self.fill_template(base_block_urdf, {'DIM': base_size}) + anchor_base_poses = [(utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.041)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.041)), bridge_pose[1])] + base_blocks = [] + + for idx in range(4): + base_block_pose = self.get_random_pose(env, base_size) + base_block_id = env.add_object(base_block_urdf, base_block_pose, color=utils.COLORS['yellow']) + base_blocks.append(base_block_id) + + # Add car body block. + body_size = (0.12, 0.04, 0.02) # x, y, z dimensions for the asset size + body_block_urdf = ""box/box-template.urdf"" + body_block_urdf = self.fill_template(body_block_urdf, {'DIM': body_size}) + body_block_pose = self.get_random_pose(env, body_size) + body_block_id = env.add_object(body_block_urdf, body_block_pose, color=utils.COLORS['blue']) + anchor_body_poses = [bridge_pose] + + # Goal: Firstly, create the base of the car by positioning two red blocks side by side. + self.add_goal(objs=base_blocks[:2], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + self.add_goal(objs=base_blocks[2:], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./2, + language_goal=""Place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + # Then, add the car body by stacking a blue block on top of the base. + self.add_goal(objs=[body_block_id], + matches=np.ones((1, 1)), + targ_poses=anchor_body_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Then, place the blue block horizontally on top of the yellow blocks."")<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-tower"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-tower"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-tower: {'task-name': 'color-coordinated-tower', 'task-description': 'Pick up blocks of five different colors (red, blue, green, yellow, and orange) and stack them on a pallet in the specific sequence. The bottom of the tower should start with a red block followed by a blue, green, yellow and finally an orange block at the top.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildBridge(Task): + """"""Construct a bridge using two yellow blocks and three blue blocks. + Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between. + Then, place the blue block horizontally on top of the yellow blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""build a bridge using four yellow blocks and one long blue block"" + self.task_completed_desc = ""done building bridge."" + + def reset(self, env): + super().reset(env) + + # Add yellow blocks. + base_length = 0.04 + base_size = (base_length, base_length, base_length) + base_block_urdf = ""box/box-template.urdf"" + bridge_pose = ((0.5, 0.0, 0.0), (0, 0, 0, 1)) # fixed pose + self.add_corner_anchor_for_pose(env, bridge_pose) + + base_block_urdf = self.fill_template(base_block_urdf, {'DIM': base_size}) + anchor_base_poses = [(utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.041)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.041)), bridge_pose[1])] + base_blocks = [] + + for idx in range(4): + base_block_pose = self.get_random_pose(env, base_size) + base_block_id = env.add_object(base_block_urdf, base_block_pose, color=utils.COLORS['yellow']) + base_blocks.append(base_block_id) + + # Add car body block. + body_size = (0.12, 0.04, 0.02) # x, y, z dimensions for the asset size + body_block_urdf = ""box/box-template.urdf"" + body_block_urdf = self.fill_template(body_block_urdf, {'DIM': body_size}) + body_block_pose = self.get_random_pose(env, body_size) + body_block_id = env.add_object(body_block_urdf, body_block_pose, color=utils.COLORS['blue']) + anchor_body_poses = [bridge_pose] + + # Goal: Firstly, create the base of the car by positioning two red blocks side by side. + self.add_goal(objs=base_blocks[:2], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + self.add_goal(objs=base_blocks[2:], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./2, + language_goal=""Place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + # Then, add the car body by stacking a blue block on top of the base. + self.add_goal(objs=[body_block_id], + matches=np.ones((1, 1)), + targ_poses=anchor_body_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Then, place the blue block horizontally on top of the yellow blocks."")<|endoftext|>" +" + Instructions: ========= +Please describe the task ""stack-blocks-in-container"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""stack-blocks-in-container"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```stack-blocks-in-container: {'task-name': 'stack-blocks-in-container', 'task-description': 'Pick up five blocks of different colors (red, blue, green, yellow, and orange) and stack them in a container in a specific sequence. The bottom of the stack should start with a red block followed by a blue, green, yellow and finally an orange block at the top.', 'assets-used': ['block/block.urdf', 'container/container-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class StackBlocksInContainer(Task): + """"""Pick up five blocks of different colors (red, blue, green, yellow, and orange) + and stack them in a container in a specific sequence. + The bottom of the stack should start with a red block followed by a blue, + green, yellow and finally an orange block at the top."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""stack the blocks in the container in the following order: {order}"" + self.task_completed_desc = ""done stacking blocks in container."" + self.order = ['red', 'blue', 'green', 'yellow', 'orange'] + self.colors = [utils.COLORS[color] for color in self.order] + + def reset(self, env): + super().reset(env) + + # Add container. + container_size = (0.15, 0.15, 0.15) # x, y, z dimensions for the container size + container_pose = self.get_random_pose(env, container_size) + container_urdf = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_urdf, replace) + env.add_object(container_urdf, container_pose, 'fixed') + + # Add blocks. + block_size = (0.04, 0.04, 0.04) # x, y, z dimensions for the block size + block_urdf = 'block/block.urdf' + blocks = [] + for color in self.colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Goal: each block is stacked in the container in the specified order. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(blocks), + language_goal=self.lang_template.format(order=', '.join(self.order)))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-block-tower"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-block-tower"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-block-tower: {'task-name': 'color-coordinated-block-tower', 'task-description': 'On a tabletop, there are fifteen blocks of three different colors (five red, five blue, and five green). The task is to pick up these blocks and stack them onto three different stands on the table, creating three different towers. Each stand should have a tower of the same color blocks with five blocks each. The blocks in each tower should be stacked in a way that the block on top is slightly displaced in relation to the block underneath, creating a spiral-like effect. The challenge lies in the color-based sorting, precise placement for achieving the spiral effect and careful stacking of the blocks to avoid toppling.', 'assets-used': ['block/block.urdf', 'stacking/stand.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedBlockTower(Task): + """"""Stack four blocks on a pallet in the following order from bottom to top: + two blue blocks side by side, one red block centered on the blue blocks, + and one green block on top of the red block."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""stack four blocks on a pallet in the following order from bottom to top: two blue blocks side by side, one red block centered on the blue blocks, and one green block on top of the red block."" + self.task_completed_desc = ""done stacking blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.015) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['blue'], utils.COLORS['blue'], utils.COLORS['red'], utils.COLORS['green']] + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.02, 0.02), (0, 0.02, 0.02), (0, 0, 0.06), (0, 0, 0.10)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: two blue blocks are placed side by side on the pallet. + # Break the language prompt step-by-step + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, + language_goal=""place two blue blocks side by side on the pallet"") + + # Goal: one red block is placed centered on the blue blocks. + self.add_goal(objs=blocks[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=""place one red block centered on the blue blocks"") + + # Goal: one green block is placed on top of the red block. + self.add_goal(objs=blocks[3:], matches=np.ones((1, 1)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=""place one green block on top of the red block"")<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-structured-block-tower"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-structured-block-tower"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-structured-block-tower: {'task-name': 'color-structured-block-tower', 'task-description': 'Construct a tower using six blocks: two red, two blue, and two green. The tower should be built in the order of a red block at the base, followed by a blue, then green, then red, blue and green at the top.', 'assets-used': ['block/block.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorStructuredBlockTower(Task): + """"""Construct a tower using six blocks: two red, two blue, and two green. + The tower should be built in the order of a red block at the base, + followed by a blue, then green, then red, blue and green at the top."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""construct a tower using six blocks: two red, two blue, and two green. "" \ + ""The tower should be built in the order of a red block at the base, "" \ + ""followed by a blue, then green, then red, blue and green at the top."" + self.task_completed_desc = ""done building color-structured block tower."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define block colors and sizes + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green']] * 2 + block_size = (0.04, 0.04, 0.04) + + # Add blocks + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Define target poses for the blocks in the tower + base_pose = self.get_random_pose(env, block_size) + targ_poses = [base_pose] + for i in range(1, 6): + targ_poses.append((np.array(base_pose[0]) + np.array([0, 0, i * block_size[2]]), base_pose[1])) + + # Add goals + for i in range(6): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targ_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/6, symmetries=[np.pi/2], + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""stack-color-coordinated-blocks"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""stack-color-coordinated-blocks"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```stack-color-coordinated-blocks: {'task-name': 'stack-color-coordinated-blocks', 'task-description': 'Pick up six blocks of different colors (red, blue, green, yellow, orange, and purple) and stack them on a pallet in two separate stacks. The first stack should be red at the bottom, blue in the middle, and green at top. The second stack should be yellow at the bottom, orange in the middle, and purple at the top.', 'assets-used': ['box/box-template.urdf', 'pallet/pallet.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class StackColorCoordinatedBlocks(Task): + """"""Pick up six blocks of different colors (red, blue, green, yellow, orange, and purple) + and stack them on a pallet in two separate stacks. The first stack should be red at the bottom, + blue in the middle, and green at top. The second stack should be yellow at the bottom, + orange in the middle, and purple at the top."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""stack the blocks on the pallet in two separate stacks. "" \ + ""The first stack should be red at the bottom, blue in the middle, "" \ + ""and green at top. The second stack should be yellow at the bottom, "" \ + ""orange in the middle, and purple at the top."" + self.task_completed_desc = ""done stacking color-coordinated blocks."" + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['purple'] + ] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'box/box-template.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0, 0.02), (0, 0.05, 0.02), + (0, -0.05, 0.06), (0, 0, 0.06), (0, 0.05, 0.06)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked on the pallet in two separate stacks. + # First stack: red at the bottom, blue in the middle, and green at top. + self.add_goal(objs=blocks[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) + + # Second stack: yellow at the bottom, orange in the middle, and purple at the top. + self.add_goal(objs=blocks[3:], matches=np.ones((3, 3)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""assemble-single-car"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""assemble-single-car"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```assemble-single-car: {'task-name': 'assemble-single-car', 'task-description': 'Build a mini car using a large blue box as the body, a smaller red box on top as the roof, and two tiny green boxes on the sides as wheels.', 'assets-used': ['box/box-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class AssembleSingleCar(Task): + """"""Assemble a mini car using a large blue box as the body, a smaller red box on top as the roof, and two tiny green boxes on the sides as wheels."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""build a mini car using a large blue box as the body, a smaller red box on top as the roof, and two tiny green boxes on the sides as wheels"" + self.task_completed_desc = ""done assembling the car."" + + def reset(self, env): + super().reset(env) + + # Add car body (large blue box). + body_size = (0.1, 0.05, 0.02) # x, y, z dimensions + body_pose = self.get_random_pose(env, body_size) + body_urdf = 'box/box-template.urdf' + body_color = utils.COLORS['blue'] + body_id = env.add_object(body_urdf, body_pose, color=body_color) + + # Add car roof (smaller red box). + roof_size = (0.08, 0.04, 0.02) # x, y, z dimensions + roof_pose = self.get_random_pose(env, roof_size) + roof_urdf = 'box/box-template.urdf' + roof_color = utils.COLORS['red'] + roof_id = env.add_object(roof_urdf, roof_pose, color=roof_color) + + # Add car wheels (two tiny green boxes). + wheel_size = (0.02, 0.02, 0.01) # x, y, z dimensions + wheel_urdf = 'box/box-template.urdf' + wheel_color = utils.COLORS['green'] + wheel_ids = [] + + for _ in range(2): + wheel_pose = self.get_random_pose(env, wheel_size) + wheel_id = env.add_object(wheel_urdf, wheel_pose, color=wheel_color) + wheel_ids.append(wheel_id) + + # Goal: assemble the car by placing the roof on the body and the wheels on the sides. + # The target poses are calculated based on the body pose. + roof_targ_pose = (body_pose[0] + np.array([0, 0, body_size[2] + roof_size[2]/2]), body_pose[1]) + wheel_targ_poses = [(body_pose[0] + np.array([0, body_size[1]/2 + wheel_size[1]/2, -body_size[2]/2]), body_pose[1]), + (body_pose[0] + np.array([0, -body_size[1]/2 - wheel_size[1]/2, -body_size[2]/2]), body_pose[1])] + + # Add the goals. + self.add_goal(objs=[roof_id], matches=np.ones((1, 1)), targ_poses=[roof_targ_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/3, language_goal=self.lang_template) + + self.add_goal(objs=wheel_ids, matches=np.ones((2, 2)), targ_poses=wheel_targ_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=2/3, language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""sort-and-stack-clr-blocks"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""sort-and-stack-clr-blocks"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```sort-and-stack-clr-blocks: {'task-name': 'sort-and-stack-clr-blocks', 'task-description': 'Pick up four blocks of different colors (red, blue, green, yellow) and place them into separate corners of a pallet. After sorting, stack them in a specific sequence on top of the pallet. The bottom of the stack should start with a green block followed by a blue, then red, and finally a yellow block at the top.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SortAndStackClrBlocks(Task): + """"""Pick up four blocks of different colors (red, blue, green, yellow) and place them into separate corners of a pallet. After sorting, stack them in a specific sequence on top of the pallet. The bottom of the stack should start with a green block followed by a blue, then red, and finally a yellow block at the top."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""sort and stack the blocks in the order of green, blue, red, and yellow"" + self.task_completed_desc = ""done sorting and stacking blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0.05, 0.05, 0.02), (-0.05, 0.05, 0.02), (-0.05, -0.05, 0.02), (0.05, -0.05, 0.02)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are sorted into separate corners of the pallet. + self.add_goal(objs=blocks, matches=np.eye(4), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=0.5, symmetries=[np.pi/2]*4, + language_goal=self.lang_template) + + # Associate stacking locations for goals. + stack_pos = [(0, 0, 0.02), (0, 0, 0.06), (0, 0, 0.10), (0, 0, 0.14)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in stack_pos] + + # Goal: blocks are stacked on top of the pallet in the order of green, blue, red, and yellow. + self.add_goal(objs=blocks, matches=np.eye(4), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=0.5, symmetries=[np.pi/2]*4, + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""create-pyramid-blocks-and-container"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""create-pyramid-blocks-and-container"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```create-pyramid-blocks-and-container: {'task-name': 'create-pyramid-blocks-and-container', 'task-description': 'Create a pyramid structure using six blocks of three different colors (two red, two green, and two blue) inside a container. The first layer of the pyramid should consist of the two green and one blue block. The second layer should be the two red blocks, and the last block which is the top of the pyramid should be the remaining blue block.', 'assets-used': ['container/container-template.urdf', 'block/block.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CreatePyramidBlocksAndContainer(Task): + """"""Create a pyramid structure using six blocks of three different colors (two red, two green, and two blue) inside a container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Create a pyramid structure using six blocks of three different colors (two red, two green, and two blue) inside a container."" + self.task_completed_desc = ""done creating pyramid."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + # x, y, z dimensions for the asset size + container_size = (0.3, 0.3, 0.1) + container_pose = self.get_random_pose(env, container_size) + container_urdf = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_urdf, replace) + env.add_object(container_urdf, container_pose, 'fixed') + self.add_corner_anchor_for_pose(env, container_pose) + + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['blue']] + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), (0, 0.05, 0.03), (0, -0.025, 0.08), (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(container_pose, i), container_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, green, blue). + self.add_goal(objs=blocks[2:5], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(blocks=""the green and blue blocks"", + row=""bottom"")) + + # Goal: blocks are stacked in a pyramid (middle row: red, red). + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, + language_goal=self.lang_template.format(blocks=""the red blocks"", + row=""middle"")) + + # Goal: blocks are stacked in a pyramid (top row: blue). + self.add_goal(objs=blocks[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=self.lang_template.format(blocks=""the blue block"", + row=""top""))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""Four-corner-pyramid-challenge"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""Four-corner-pyramid-challenge"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```Four-corner-pyramid-challenge: {'task-name': 'Four-corner-pyramid-challenge', 'task-description': ""A tabletop is partitioned into four different zones using the 'zone/zone.urdf' asset. In each zone, there are four blocks of different colors (red, blue, green, and yellow). The task is to construct a pyramid of blocks in each zone using the 'block/block.urdf' asset such that the sequence of blocks from bottom to top is red, blue, green, and yellow. The task is challenging because it requires precise stack control and color recognition."", 'assets-used': ['block/block.urdf', 'zone/zone.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class FourCornerPyramidChallenge(Task): + """"""Construct a pyramid of blocks in each zone with a specific color sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""build a pyramid of blocks in each zone with the sequence red, blue, green, and yellow from bottom to top"" + self.task_completed_desc = ""done building pyramids."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for _ in range(4): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + for _ in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(zone_pose, i), zone_pose[1]) for i in place_pos for zone_pose in zone_poses] + + # Goal: blocks are stacked in a pyramid in each zone. + for i in range(4): + self.add_goal(objs=blocks[i*4:(i+1)*4], matches=np.ones((4, 4)), targ_poses=targs[i*4:(i+1)*4], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*4, + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""colorful-block-tower-on-cylinder-base"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""colorful-block-tower-on-cylinder-base"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```colorful-block-tower-on-cylinder-base: {'task-name': 'colorful-block-tower-on-cylinder-base', 'task-description': 'Construct a tower using four blocks of different colors (red, blue, green, and yellow) on a placed cylindrical base at the corner of the tabletop. The sequence from bottom to top should be red, blue, green, and yellow.', 'assets-used': ['block/block.urdf', 'corner/corner-template.urdf', 'cylinder/cylinder-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorfulBlockTowerOnCylinderBase(Task): + """"""Construct a tower using four blocks of different colors (red, blue, green, and yellow) on a placed cylindrical base at the corner of the tabletop. The sequence from bottom to top should be red, blue, green, and yellow."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""construct a tower using four blocks of different colors (red, blue, green, and yellow) on a placed cylindrical base at the corner of the tabletop. The sequence from bottom to top should be red, blue, green, and yellow."" + self.task_completed_desc = ""done building the tower."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add cylindrical base. + # x, y, z dimensions for the asset size + base_size = (0.05, 0.05, 0.05) + base_urdf = 'cylinder/cylinder-template.urdf' + base_pose = self.get_random_pose(env, base_size) + base_id = env.add_object(base_urdf, base_pose, 'fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + + objs = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, 0, 0.05), (0, 0, 0.09), (0, 0, 0.13), (0, 0, 0.17)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked on the cylindrical base in the order red, blue, green, yellow from bottom to top. + for i in range(4): + self.add_goal(objs=[objs[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""construct-corner-blocks"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""construct-corner-blocks"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```construct-corner-blocks: {'task-name': 'construct-corner-blocks', 'task-description': 'Create a corner structure using four blocks. Two red blocks form the base, one on each side of the corner, followed by a green block that is positioned on the red blocks at the corner junction, and finally a blue block on top of the green one. The overall structure forms a 3-D corner.', 'assets-used': ['block/block.urdf', 'corner/corner-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ConstructCornerBlocks(Task): + """"""Create a corner structure using four blocks. Two red blocks form the base, one on each side of the corner, followed by a green block that is positioned on the red blocks at the corner junction, and finally a blue block on top of the green one. The overall structure forms a 3-D corner."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Create a corner structure using four blocks. Two red blocks form the base, one on each side of the corner, followed by a green block that is positioned on the red blocks at the corner junction, and finally a blue block on top of the green one. The overall structure forms a 3-D corner."" + self.task_completed_desc = ""done constructing corner blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add corner. + corner_size = (0.15, 0.15, 0.05) + corner_urdf = 'corner/corner-template.urdf' + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, 'fixed') + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue']] + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0.05, 0.02), (0, 0, 0.06), (0, 0, 0.10)] + targs = [(utils.apply(corner_pose, i), corner_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a corner (bottom row: two red blocks). + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a corner (middle row: one green block). + self.add_goal(objs=blocks[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*1, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a corner (top row: one blue block). + self.add_goal(objs=blocks[3:], matches=np.ones((1, 1)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*1, + language_goal=self.lang_template) +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""corner-sort-cylinders"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""corner-sort-cylinders"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```corner-sort-cylinders: {'task-name': 'corner-sort-cylinders', 'task-description': 'Pick up cylinders of four different colors (red, blue, green, yellow) and place them into four corners accordingly marked on the tabletop. The corner is designed as a 2-block-size square where only one cylinder can fit. The task is challenging due to precise placement and the need for identifying the corners accurately.', 'assets-used': ['corner/corner-template.urdf', 'cylinder/cylinder-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CornerSortCylinders(Task): + """"""Pick up cylinders of four different colors (red, blue, green, yellow) and place them into four corners accordingly marked on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} cylinder in the {color} corner"" + self.task_completed_desc = ""done sorting cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors + colors = ['red', 'blue', 'green', 'yellow'] + + # Add corners + corner_size = (0.04, 0.04, 0.04) # x, y, z dimensions for the asset size + corner_template = 'corner/corner-template.urdf' + corner_poses = [] + for color in colors: + replace = {'DIM': corner_size, 'HALF': (corner_size[0] / 2, corner_size[1] / 2, corner_size[2] / 2), 'COLOR': utils.COLORS[color]} + corner_urdf = self.fill_template(corner_template, replace) + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, 'fixed') + corner_poses.append(corner_pose) + + # Add cylinders + cylinder_size = (0.02, 0.02, 0.06) # x, y, z dimensions for the asset size + cylinder_template = 'cylinder/cylinder-template.urdf' + cylinders = [] + for color in colors: + replace = {'DIM': cylinder_size, 'HALF': (cylinder_size[0] / 2, cylinder_size[1] / 2, cylinder_size[2] / 2), 'COLOR': utils.COLORS[color]} + cylinder_urdf = self.fill_template(cylinder_template, replace) + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose) + cylinders.append(cylinder_id) + + # Add goals + for i in range(len(cylinders)): + self.add_goal(objs=[cylinders[i]], matches=np.int32([[1]]), targ_poses=[corner_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(cylinders), + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""sorting-blocks-into-pallets"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""sorting-blocks-into-pallets"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```sorting-blocks-into-pallets: {'task-name': 'sorting-blocks-into-pallets', 'task-description': 'Pick up blocks of four different colors (red, blue, green, yellow) and place them into four separate pallets of matching color. The pallets are placed in a row and the blocks are scattered randomly on the table.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SortingBlocksIntoPallets(Task): + """"""Pick up blocks of four different colors (red, blue, green, yellow) and place them into four separate pallets of matching color. The pallets are placed in a row and the blocks are scattered randomly on the table."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""put the {color} block into the {color} pallet"" + self.task_completed_desc = ""done sorting blocks into pallets."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallets. + # x, y, z dimensions for the asset size + pallet_size = (0.12, 0.12, 0.02) + pallet_urdf = 'pallet/pallet.urdf' + pallet_poses = [] + pallet_colors = ['red', 'blue', 'green', 'yellow'] + for color in pallet_colors: + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed', color=utils.COLORS[color]) + pallet_poses.append(pallet_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + for color in pallet_colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in a different pallet of matching color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[pallet_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), + language_goal=self.lang_template.format(color=pallet_colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""sort-and-assemble-block-castle"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""sort-and-assemble-block-castle"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```sort-and-assemble-block-castle: {'task-name': 'sort-and-assemble-block-castle', 'task-description': 'On a tabletop, there are twelve blocks of three different colors (four red, four green, and four blue). The task involves sorting the blocks according to the color in three marked zones on the tabletop and subsequently constructing a castle in each zone. In each castle, the first layer should consist of the two blocks of the same color, followed by the second layer of one block and finally the last block on the top forming a castle-like structure. The challenge lies in the color-based sorting and careful assembly of the blocks to avoid toppling.', 'assets-used': ['block/block.urdf', 'zone/zone.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SortAndAssembleBlockCastle(Task): + """"""Sort blocks by color and assemble them into a castle-like structure."""""" + + def __init__(self): + super().__init__() + self.max_steps = 50 + self.lang_template = ""sort the blocks by color and assemble them into a castle"" + self.task_completed_desc = ""done sorting and assembling."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for _ in range(3): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue']] + blocks = [] + for color in block_colors: + for _ in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Goal: each block is in a different zone based on color. + for i in range(12): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 3)), targ_poses=zone_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/12) + + # Goal: blocks are stacked in a pyramid in each zone. + for i in range(3): + zone_blocks = blocks[i*4:(i+1)*4] + place_pos = [(0, -0.02, 0.02), (0, 0.02, 0.02), + (0, 0, 0.06), (0, 0, 0.10)] + targs = [(utils.apply(zone_poses[i], pos), zone_poses[i][1]) for pos in place_pos] + for j in range(4): + self.add_goal(objs=[zone_blocks[j]], matches=np.ones((1, 1)), targ_poses=[targs[j]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/12, language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""vertical-insertion-blocks"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""vertical-insertion-blocks"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```vertical-insertion-blocks: {'task-name': 'vertical-insertion-blocks', 'task-description': 'The task involves picking up four color specific blocks (red, blue, green, and yellow) and inserting each block into four differently colored stands set upright on the tabletop. The block-colored with red needs to be inserted into the red-colored stand, and the same sequence is maintained for each colored blocks and stands. This task is challenging due to the requirement for precise insertion and the manipulation of vertical objects.', 'assets-used': ['stacking/block.urdf', 'stacking/stand.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class VerticalInsertionBlocks(Task): + """"""Pick up four color specific blocks and insert each block into four differently colored stands set upright on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert the {color} block into the {color} stand"" + self.task_completed_desc = ""done inserting blocks into stands."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for blocks and stands + colors = ['red', 'blue', 'green', 'yellow'] + + # Add stands. + # x, y, z dimensions for the asset size + stand_size = (0.04, 0.04, 0.1) + stand_urdf = 'stacking/stand.urdf' + stands = [] + for color in colors: + stand_pose = self.get_random_pose(env, stand_size) + stand_id = env.add_object(stand_urdf, stand_pose, color=utils.COLORS[color], category='fixed') + stands.append(stand_id) + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + blocks = [] + for color in colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is inserted into the stand of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(stands[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-sphere-insertion"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-sphere-insertion"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-sphere-insertion: {'task-name': 'color-coordinated-sphere-insertion', 'task-description': 'There are four spheres and four boxes of different colors (red, blue, green, and yellow). Each sphere is inside a box, but not corresponding to the color of the box. The task is to pick up each sphere and place it in the box of the same color. The challenge lies in the precise placement and color matching.', 'assets-used': ['sphere/sphere.urdf', 'box/box-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedSphereInsertion(Task): + """"""Insert each sphere into the bowl of the same color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert each sphere into the bowl of the same color"" + self.task_completed_desc = ""done inserting spheres into bowls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and corresponding names + colors = ['red', 'blue', 'green', 'yellow'] + color_values = [utils.COLORS[color] for color in colors] + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0.02) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for i in range(4): + bowl_pose = self.get_random_pose(env, bowl_size) + env.add_object(bowl_urdf, bowl_pose, 'fixed', color=color_values[i]) + bowl_poses.append(bowl_pose) + + # Add spheres. + # x, y, z dimensions for the asset size + sphere_size = (0.04, 0.04, 0.04) + sphere_template = 'sphere/sphere-template.urdf' + spheres = [] + for i in range(4): + sphere_pose = self.get_random_pose(env, sphere_size) + replace = {'DIM': sphere_size, 'HALF': (sphere_size[0] / 2, sphere_size[1] / 2, sphere_size[2] / 2)} + sphere_urdf = self.fill_template(sphere_template, replace) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=color_values[i]) + spheres.append(sphere_id) + + # Goal: each sphere is in a bowl of the same color. + for i in range(4): + self.add_goal(objs=[spheres[i]], matches=np.ones((1, 1)), targ_poses=[bowl_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=f""insert the {colors[i]} sphere into the {colors[i]} bowl"")<|endoftext|>" +" + Instructions: ========= +Please describe the task ""block-pyramid-with-limited-space"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""block-pyramid-with-limited-space"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```block-pyramid-with-limited-space: {'task-name': 'block-pyramid-with-limited-space', 'task-description': 'On a tabletop, there are twelve blocks of four different colors (three red, three green, three blue, three yellow). Three zones are defined, each with a triangle-shaped border that is marked. The task involves sorting the blocks according to the color into three zones on the tabletop and constructing a pyramid in each zone. In each pyramid, the base should contain two blocks of the same color, followed by the second layer of one block, thus forming a pyramid-like structure. However, the third yellow block should be placed in the center of the zones forming a smaller pyramid. The challenge lies in the color-based sorting, careful assembly of the blocks to avoid topple, and limited space in the zones which adds to the complexity.', 'assets-used': ['block/block.urdf', 'zone/zone.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BlockPyramidWithLimitedSpace(Task): + """"""Sort blocks according to color into three zones on the tabletop and construct a pyramid in each zone."""""" + + def __init__(self): + super().__init__() + self.max_steps = 50 + self.lang_template = ""sort the blocks according to color into three zones and construct a pyramid in each zone"" + self.task_completed_desc = ""done sorting and constructing pyramids."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for _ in range(3): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['yellow'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for color in colors: + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(zone_pose, i), zone_pose[1]) for zone_pose in zone_poses for i in place_pos] + + # Goal: blocks are sorted and stacked in a pyramid in each zone. + for i in range(3): + self.add_goal(objs=blocks[i*3:(i+1)*3], matches=np.ones((3, 3)), targ_poses=targs[i*3:(i+1)*3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""build-cylinder-structure"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""build-cylinder-structure"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```build-cylinder-structure: {'task-name': 'build-cylinder-structure', 'task-description': ""Using four colored cylinders (red, blue, green, yellow), construct a structure atop a square base. The red and blue cylinders should be sealed by the square base side by side, while the green cylinder should be on top of the blue one, and the yellow one on top of the red. The final look should resemble the letter 'H'."", 'assets-used': ['cylinder/cylinder-template.urdf', 'square/square-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildCylinderStructure(Task): + """"""Construct a structure using four colored cylinders (red, blue, green, yellow) on a square base."""""" + + def __init__(self): + super().__init__() + self.max_steps = 5 + self.lang_template = ""construct a structure using four colored cylinders on a square base"" + self.task_completed_desc = ""done building the cylinder structure."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add square base. + # x, y, z dimensions for the asset size + base_size = (0.15, 0.15, 0.005) + base_urdf = 'square/square-template.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Cylinder colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow'] + ] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.08) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + + objs = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=colors[i]) + objs.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.04), (0, 0.05, 0.04), + (0, 0.05, 0.12), (0, -0.05, 0.12)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: red and blue cylinders are placed side by side on the base. + self.add_goal(objs=objs[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, + language_goal=""place the red and blue cylinders side by side on the base"") + + # Goal: green cylinder is placed on top of the blue cylinder. + self.add_goal(objs=[objs[2]], matches=np.ones((1, 1)), targ_poses=[targs[2]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2], + language_goal=""place the green cylinder on top of the blue cylinder"") + + # Goal: yellow cylinder is placed on top of the red cylinder. + self.add_goal(objs=[objs[3]], matches=np.ones((1, 1)), targ_poses=[targs[3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2], + language_goal=""place the yellow cylinder on top of the red cylinder"")<|endoftext|>" +" + Instructions: ========= +Please describe the task ""insert-blocks-lineup"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""insert-blocks-lineup"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```insert-blocks-lineup: {'task-name': 'insert-blocks-lineup', 'task-description': 'On the tabletop, there are four different color blocks (red, blue, green, and yellow), and four fixtures in corresponding colors. The task is to pick up each block and insert it into the corresponding color fixture. However, the fixtures are arranged in a straight line, with a line of small blocks serving as barrier between the fixtures and the colored blocks initially scattered on the table, providing a challenge in precise navigation and placement.', 'assets-used': ['block/block.urdf', 'insertion/fixture.urdf', 'block/small.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertBlocksLineup(Task): + """"""Pick up four different color blocks and insert them into the corresponding color fixtures."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert the {color} block into the {color} fixture"" + self.task_completed_desc = ""done inserting blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for blocks and fixtures + colors = ['red', 'blue', 'green', 'yellow'] + + # Add fixtures. + fixture_size = (0.04, 0.04, 0.04) + fixture_urdf = 'insertion/fixture.urdf' + fixture_poses = [] + for i in range(4): + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[colors[i]], category='fixed') + fixture_poses.append((fixture_pose, fixture_id)) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[colors[i]]) + blocks.append(block_id) + + # Add small blocks as barriers. + small_block_size = (0.02, 0.02, 0.02) + small_block_urdf = 'block/small.urdf' + for _ in range(10): + small_block_pose = self.get_random_pose(env, small_block_size) + env.add_object(small_block_urdf, small_block_pose) + + # Goal: each block is in the corresponding color fixture. + for i in range(4): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[fixture_poses[i][0]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-specific-container-fill"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-specific-container-fill"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-specific-container-fill: {'task-name': 'color-specific-container-fill', 'task-description': 'Arrange four colored blocks (red, blue, green, and yellow) around a pallet. Then, pick up these blocks and place them inside a container marked in the same color. The task requires precise placement, color matching, and an understanding of spatial structures.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'container/container-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorSpecificContainerFill(Task): + """"""Arrange four colored blocks (red, blue, green, and yellow) around a pallet. + Then, pick up these blocks and place them inside a container marked in the same color. + The task requires precise placement, color matching, and an understanding of spatial structures."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""put the {color} block in the {color} container"" + self.task_completed_desc = ""done arranging blocks in containers."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Define block and container colors. + colors = ['red', 'blue', 'green', 'yellow'] + + # Add blocks and containers. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + container_size = (0.12, 0.12, 0.05) + container_template = 'container/container-template.urdf' + blocks = [] + containers = [] + for color in colors: + # Add block. + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add container. + container_pose = self.get_random_pose(env, container_size) + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_template, replace) + container_id = env.add_object(container_urdf, container_pose, 'fixed', color=utils.COLORS[color]) + containers.append(container_id) + + # Goal: each block is in a container of the same color. + for i in range(len(colors)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(containers[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(colors), + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""multicolor-block-bridge"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""multicolor-block-bridge"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```multicolor-block-bridge: {'task-name': 'multicolor-block-bridge', 'task-description': 'Build a bridge by stacking three red, three blue, and three green blocks on a pallet. Arrange in a sequence from left to right: red, blue, and green. Then, place three cylinders of corresponding colors on top of the stacked blocks, forming a bridge. The cylinders should roll from the top block to the pallet, creating a challenge of precision and control.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'cylinder/cylinder-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class MulticolorBlockBridge(Task): + """"""Build a bridge by stacking three red, three blue, and three green blocks on a pallet. + Arrange in a sequence from left to right: red, blue, and green. + Then, place three cylinders of corresponding colors on top of the stacked blocks, forming a bridge. + The cylinders should roll from the top block to the pallet, creating a challenge of precision and control."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Build a bridge by stacking three red, three blue, and three green blocks on a pallet. Arrange in a sequence from left to right: red, blue, and green. Then, place three cylinders of corresponding colors on top of the stacked blocks, forming a bridge."" + self.task_completed_desc = ""done building the bridge."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green']] + blocks = [] + for i in range(9): # 3 blocks of each color + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i // 3]) + blocks.append(block_id) + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.04) + cylinder_template = 'cylinder/cylinder-template.urdf' + cylinders = [] + for i in range(3): # 1 cylinder of each color + cylinder_pose = self.get_random_pose(env, cylinder_size) + replace = {'DIM': cylinder_size, 'HALF': (cylinder_size[0] / 2, cylinder_size[1] / 2, cylinder_size[2] / 2)} + cylinder_urdf = self.fill_template(cylinder_template, replace) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=block_colors[i]) + cylinders.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), (0, 0.05, 0.03)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked on the pallet in the order red, blue, green. + for i in range(9): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targs[i // 3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 9, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: cylinders are placed on top of the stacked blocks. + for i in range(3): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""pyramid-blocks-assemble"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""pyramid-blocks-assemble"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```pyramid-blocks-assemble: {'task-name': 'pyramid-blocks-assemble', 'task-description': 'Construct a pyramid using nine blocks in a specific color order on a pallet. The bottom layer should contain five blocks: red, blue, green, yellow, and orange (in that order from left to right). The middle layer should consist of three blocks: yellow, red, and blue (from left to right). The top layer should contain a single green block. The pyramid requires careful placement and color matching.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class PyramidBlocksAssemble(Task): + """"""Construct a pyramid using nine blocks in a specific color order on a pallet."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""construct a pyramid using nine blocks in a specific color order on a pallet"" + self.task_completed_desc = ""done constructing pyramid."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.35, 0.35, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'] + ] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for _ in range(9): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [ + (-0.1, -0.1, 0.02), (0, -0.1, 0.02), (0.1, -0.1, 0.02), (-0.1, 0, 0.02), (0.1, 0, 0.02), + (-0.05, 0.05, 0.06), (0.05, 0.05, 0.06), (0, 0.1, 0.06), + (0, 0.05, 0.1) + ] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid in a specific color order. + for i in range(9): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 9, + language_goal=self.lang_template.format(blocks=""the blocks"", + row=""row""))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""place-ball-in-elevated-bowl"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""place-ball-in-elevated-bowl"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```place-ball-in-elevated-bowl: {'task-name': 'place-ball-in-elevated-bowl', 'task-description': 'The primary objective of the task is to pick up a red ball and carefully place it into a bowl, which is positioned on a raised platform that is surrounded by small blocks. The challenge lies in precise navigation, maintaining a hold of the ball, and avoiding the surrounding blocks.', 'assets-used': ['ball/ball-template.urdf', 'bowl/bowl.urdf', 'block/small.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class PlaceBallInElevatedBowl(Task): + """"""Pick up a red ball and carefully place it into a bowl, which is positioned on a raised platform that is surrounded by small blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""place the red ball in the elevated bowl"" + self.task_completed_desc = ""done placing ball in bowl."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add elevated platform. + platform_size = (0.3, 0.3, 0.05) + + # Add bowl on the platform. + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_pose[0][2] += platform_size[2] # place the bowl on top of the platform + bowl_id = env.add_object(bowl_urdf, bowl_pose, 'fixed') + + # Add red ball. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS['red']) + + # Add small blocks around the platform. + block_size = (0.02, 0.02, 0.02) + block_urdf = 'block/small.urdf' + for _ in range(5): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose) + + # Goal: the red ball is in the bowl. + self.add_goal(objs=[ball_id], matches=np.ones((1, 1)), targ_poses=[bowl_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""align-balls-in-colored-zones"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""align-balls-in-colored-zones"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```align-balls-in-colored-zones: {'task-name': 'align-balls-in-colored-zones', 'task-description': 'There are six balls of different colors (red, blue, green, yellow, orange, and purple) and six zones correspondingly colored. The task is to pick up each ball and place it in the zone of the same color, arranging the balls in a straight line. The challenge lies in the precise placement and color matching.', 'assets-used': ['ball/ball-template.urdf', 'zone/zone.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class AlignBallsInColoredZones(Task): + """"""Align balls of different colors in correspondingly colored zones."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} ball in the {color} zone"" + self.task_completed_desc = ""done aligning balls in colored zones."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for balls and zones + colors = ['red', 'blue', 'green', 'yellow', 'orange', 'purple'] + color_names = ['Red', 'Blue', 'Green', 'Yellow', 'Orange', 'Purple'] + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for i in range(6): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[colors[i]]) + zone_poses.append(zone_pose) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for i in range(6): + ball_pose = self.get_random_pose(env, ball_size) + replace = {'DIM': ball_size, 'HALF': (ball_size[0] / 2, ball_size[1] / 2, ball_size[2] / 2), 'COLOR': colors[i]} + ball_urdf = self.fill_template(ball_urdf, replace) + ball_id = env.add_object(ball_urdf, ball_pose) + balls.append(ball_id) + + # Goal: each ball is in a different colored zone. + for i in range(6): + self.add_goal(objs=[balls[i]], matches=np.int32([[1]]), targ_poses=[zone_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, + language_goal=self.lang_template.format(color=color_names[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-cylinder-tower"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-cylinder-tower"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-cylinder-tower: {'task-name': 'color-coordinated-cylinder-tower', 'task-description': 'Stack cylinders of four different colors (red, blue, green, yellow) on top of each other on a square stand in a specific sequence. The bottom of the stack should start with a blue cylinder, follow by a green cylinder, then a red one, and finally a yellow cylinder at the top. Each cylinder has to be aligned correctly to avoid falling.', 'assets-used': ['cylinder/cylinder-template.urdf', 'stacking/stand.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedCylinderTower(Task): + """"""Stack cylinders of four different colors (red, blue, green, yellow) on top of each other on a square stand in a specific sequence. The bottom of the stack should start with a blue cylinder, follow by a green cylinder, then a red one, and finally a yellow cylinder at the top. Each cylinder has to be aligned correctly to avoid falling."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""Stack cylinders of four different colors (red, blue, green, yellow) on top of each other on a square stand in a specific sequence. The bottom of the stack should start with a blue cylinder, follow by a green cylinder, then a red one, and finally a yellow cylinder at the top."" + self.task_completed_desc = ""done stacking cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Cylinder colors. + colors = [utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['red'], utils.COLORS['yellow']] + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + + objs = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=colors[i]) + objs.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0, 0, 0.03), (0, 0, 0.08), (0, 0, 0.13), (0, 0, 0.18)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: cylinders are stacked in a tower (bottom to top: blue, green, red, yellow). + for i in range(4): + self.add_goal(objs=[objs[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""symmetric-block-bridge-construction"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""symmetric-block-bridge-construction"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```symmetric-block-bridge-construction: {'task-name': 'symmetric-block-bridge-construction', 'task-description': 'Create a symmetrical bridge-shaped structure on a stand using eight blocks of two different colors (four red and four blue). Start by placing two red blocks side by side at the center of the stand to form the base of the bridge. Then, take a blue block and place it on top of the red blocks, followed by another red block on top of the blue one, and this pattern continues till you exhaust all the blocks. The final structure should be a bridge with alternating colors (red, blue, red, blue). The challenge lies in ensuring symmetry and balancing the blocks without making them fall.', 'assets-used': ['stacking/stand.urdf', 'block/block-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class SymmetricBlockBridgeConstruction(Task): + """"""Create a symmetrical bridge-shaped structure on a stand using eight blocks of two different colors (four red and four blue)."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""create a symmetrical bridge-shaped structure on a stand using eight blocks of two different colors (four red and four blue)"" + self.task_completed_desc = ""done building the bridge."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['blue']] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(8): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i%2]) + objs.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13), + (0, -0.025, 0.18), (0, 0.025, 0.18)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a bridge (bottom row: red, red). + self.add_goal(objs=objs[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*2, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (second row: blue). + self.add_goal(objs=objs[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (third row: red). + self.add_goal(objs=objs[3:4], matches=np.ones((1, 1)), targ_poses=targs[3:4], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (fourth row: blue). + self.add_goal(objs=objs[4:5], matches=np.ones((1, 1)), targ_poses=targs[4:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (fifth row: red). + self.add_goal(objs=objs[5:6], matches=np.ones((1, 1)), targ_poses=targs[5:6], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (sixth row: blue). + self.add_goal(objs=objs[6:7], matches=np.ones((1, 1)), targ_poses=targs[6:7], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (top row: red, red). + self.add_goal(objs=objs[7:], matches=np.ones((1, 1)), targ_poses=targs[7:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""sphere-align-stand"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""sphere-align-stand"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```sphere-align-stand: {'task-name': 'sphere-align-stand', 'task-description': 'On a table there are five differently colored stands and five spheres. The task involves picking up each sphere and placing it on the stand of the matching color. The task is challenging due to the precision required in picking up and placing the spheres, and the color coordination.', 'assets-used': ['stacking/stand.urdf', 'sphere/sphere.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SphereAlignStand(Task): + """"""Pick up each sphere and place it on the stand of the matching color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 5 + self.lang_template = ""place the {color} sphere on the {color} stand"" + self.task_completed_desc = ""done aligning spheres with stands."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for the spheres and stands + colors = ['red', 'green', 'blue', 'yellow', 'purple'] + color_names = ['red', 'green', 'blue', 'yellow', 'purple'] + + # Add stands. + # x, y, z dimensions for the asset size + stand_size = (0.05, 0.05, 0.05) + stand_urdf = 'stacking/stand.urdf' + stand_poses = [] + for i in range(5): + stand_pose = self.get_random_pose(env, stand_size) + env.add_object(stand_urdf, stand_pose, 'fixed', color=utils.COLORS[colors[i]]) + stand_poses.append(stand_pose) + + # Add spheres. + # x, y, z dimensions for the asset size + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + spheres = [] + for i in range(5): + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS[colors[i]]) + spheres.append(sphere_id) + + # Goal: each sphere is on the stand of the matching color. + for i in range(5): + self.add_goal(objs=[spheres[i]], matches=np.ones((1, 1)), targ_poses=[stand_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/5, + language_goal=self.lang_template.format(color=color_names[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""construct-colorful-arch"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""construct-colorful-arch"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```construct-colorful-arch: {'task-name': 'construct-colorful-arch', 'task-description': 'Construct an arch using six blocks: three red, and three blue. Place two red blocks on the tabletop parallel to each other, then place a blue block on top of the red blocks to form a basic arch. Repeat the process by placing a red block on each side of the base arch and bridge them with the last blue block. Finally, place the last two red blocks on top of the second layer of the red blocks, on either side. The overall structure forms a colorful 3D arch.', 'assets-used': ['block/block.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ConstructColorfulArch(Task): + """"""Construct an arch using six blocks: three red, and three blue."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Construct an arch using six blocks: three red, and three blue."" + self.task_completed_desc = ""done constructing colorful arch."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + colors = [utils.COLORS['red'], utils.COLORS['blue']] + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + color = colors[i // 3] # First three blocks are red, last three are blue + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0.05, 0.02), # Base layer + (0, 0, 0.06), # Second layer + (0, -0.05, 0.10), (0, 0.05, 0.10), # Third layer + (0, 0, 0.14)] # Top layer + targs = [(utils.apply(block_pose, i), block_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in an arch (bottom layer: red, red). + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, + language_goal=""Place two red blocks on the tabletop parallel to each other"") + + # Goal: blocks are stacked in an arch (second layer: blue). + self.add_goal(objs=blocks[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=""Place a blue block on top of the red blocks to form a basic arch"") + + # Goal: blocks are stacked in an arch (third layer: red, red). + self.add_goal(objs=blocks[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, + language_goal=""Place a red block on each side of the base arch"") + + # Goal: blocks are stacked in an arch (top layer: blue). + self.add_goal(objs=blocks[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=""Bridge them with the last blue block"")<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-sorted-container-stack"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-sorted-container-stack"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-sorted-container-stack: {'task-name': 'color-sorted-container-stack', 'task-description': 'The challenge is to stack four differently colored blocks (red, blue, green, yellow) inside a container. These blocks are initially placed inside two separate bowls. The red and blue blocks are in one bowl, the yellow and green blocks are in another. The robot has to pick up the blocks from the two bowls and stack them inside the container in the specific order: red, blue, green, then yellow.', 'assets-used': ['bowl/bowl.urdf', 'container/container-template.urdf', 'block/block.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorSortedContainerStack(Task): + """"""Stack four differently colored blocks (red, blue, green, yellow) inside a container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""stack the blocks in the container in the order: red, blue, green, then yellow"" + self.task_completed_desc = ""done stacking blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + # x, y, z dimensions for the asset size + container_size = (0.15, 0.15, 0.15) + container_pose = self.get_random_pose(env, container_size) + container_urdf = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_urdf, replace) + env.add_object(container_urdf, container_pose, 'fixed') + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + for i in range(2): + bowl_pose = self.get_random_pose(env, bowl_size) + env.add_object(bowl_urdf, bowl_pose, 'fixed') + + # Goal: each block is stacked in the container in the order: red, blue, green, yellow. + for i in range(4): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""align-spheres-in-colored-zones"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""align-spheres-in-colored-zones"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```align-spheres-in-colored-zones: {'task-name': 'align-spheres-in-colored-zones', 'task-description': 'There are four spheres of different colors (red, blue, green, yellow) positioned randomly on the table along with four zones marked with matching colors. The task is to pick up each sphere and place it into the matching colored zone with precise placement.', 'assets-used': ['sphere/sphere-template.urdf', 'zone/zone.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class AlignSpheresInColoredZones(Task): + """"""Align spheres of different colors in the matching colored zones."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} sphere in the {color} zone"" + self.task_completed_desc = ""done aligning spheres in colored zones."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors + colors = ['red', 'blue', 'green', 'yellow'] + color_names = ['red', 'blue', 'green', 'yellow'] + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for color in colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add spheres. + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere-template.urdf' + spheres = [] + for i, color in enumerate(colors): + sphere_pose = self.get_random_pose(env, sphere_size) + replace = {'DIM': sphere_size, 'HALF': (sphere_size[0] / 2, sphere_size[1] / 2, sphere_size[2] / 2)} + sphere_urdf = self.fill_template(sphere_urdf, replace) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS[color]) + spheres.append(sphere_id) + + # Add goal + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[zone_poses[i]], replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1, + language_goal=self.lang_template.format(color=color_names[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""sort-insert-color-coordinated-blocks"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""sort-insert-color-coordinated-blocks"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```sort-insert-color-coordinated-blocks: {'task-name': 'sort-insert-color-coordinated-blocks', 'task-description': 'There are six blocks of three different colors (two red, two blue, and two green), two red containers, two blue containers, and two green containers scattered on the table. The task is to sort the blocks by their colors and place (insert) them into the containers of the matching color. Two blocks of the same color should go into two different containers of the same color. The challenge lies in the color-based sorting and insertion of the blocks into the containers.', 'assets-used': ['block/block.urdf', 'container/container-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SortInsertColorCoordinatedBlocks(Task): + """"""Sort blocks by their colors and place them into the containers of the matching color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""sort the blocks by their colors and place them into the containers of the matching color"" + self.task_completed_desc = ""done sorting and inserting blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add containers. + container_size = (0.12, 0.12, 0.12) + container_size = (0.1, 0.1, 0.1) + container_pose = self.get_random_pose(env, container_size) + container_urdf = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_urdf, replace) + container_colors = ['red', 'blue', 'green'] + container_poses = [] + for color in container_colors: + container_pose = self.get_random_pose(env, container_size) + env.add_object(container_urdf, container_pose, color=utils.COLORS[color]) + container_poses.append(container_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = ['red', 'red', 'blue', 'blue', 'green', 'green'] + blocks = [] + for color in block_colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in a container of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[container_poses[i//2]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-ordered-insertion"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-ordered-insertion"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-ordered-insertion: {'task-name': 'color-ordered-insertion', 'task-description': 'There are four differently-colored ell objects (red, blue, green, yellow) and a corresponding set of color-coded fixtures. The task involves picking up each ell object and inserting it into the matching color fixture in a specific order: from left to right, insert red, blue, green, and finally yellow. The challenge lies in the precise manipulation of the ell objects and the color-coordination required. The fixtures are arranged in a straight line, and can only be approached from one direction, demanding careful navigation.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorOrderedInsertion(Task): + """"""Insert differently-colored ell objects into the matching color fixture in a specific order."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert the {color} ell into the matching fixture"" + self.task_completed_desc = ""done inserting ells."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their order + colors = ['red', 'blue', 'green', 'yellow'] + color_order = {color: i for i, color in enumerate(colors)} + + # Add fixtures. + fixture_size = (0.12, 0.12, 0.02) + fixture_urdf = 'insertion/fixture.urdf' + fixtures = [] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color], category='fixed') + fixtures.append(fixture_id) + + # Add ell objects. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + ells = [] + for color in colors: + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[color]) + ells.append(ell_id) + + # Goal: each ell is inserted into the matching color fixture in the correct order. + for i, ell in enumerate(ells): + self.add_goal(objs=[ell], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(ells), + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-insertion"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-insertion"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-insertion: {'task-name': 'color-coordinated-insertion', 'task-description': 'There are three insertion fixtures and three ell shaped blocks of different colors (red, blue, green) on the table top. The task is to pick up the ell shaped blocks and insert each one of them into the fixture of the same color. However, the ell blocks should be inserted in a specific sequence - red first, then blue, and finally green. This task is challenging due to the precision required for insertion and the need for color coordination and sequencing.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedInsertion(Task): + """"""Insert each block into the fixture of the same color"""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert each block into the fixture of the same color"" + self.task_completed_desc = ""done with color-coordinated-insertion."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.35, 0.35, 0.01) + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_urdf = 'pallet/pallet.urdf' + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add fixtures and blocks. + colors = ['red', 'blue', 'green', 'yellow'] + fixtures = [] + blocks = [] + fixture_size = (0.05, 0.05, 0.05) + block_size = (0.04, 0.04, 0.04) + fixture_urdf = 'insertion/fixture.urdf' + block_urdf = 'block/block.urdf' + for color in colors: + # Add fixture. + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color]) + fixtures.append(fixture_id) + + # Add block. + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in the fixture of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(blocks), + language_goal=self.lang_template) + + # Goal: each fixture is on the pallet. + for i in range(len(fixtures)): + self.add_goal(objs=[fixtures[i]], matches=np.ones((1, 1)), targ_poses=[pallet_pose], replace=False, + rotations=True, metric='zone', params=[(pallet_pose, pallet_size)], step_max_reward=1 / len(fixtures), + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""cylinder-stand-alignment"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""cylinder-stand-alignment"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```cylinder-stand-alignment: {'task-name': 'cylinder-stand-alignment', 'task-description': 'Arrange four colored cylinders (red, blue, green, yellow) in order of their colors on four stands of matching color. However, the stands are placed in a random order on the table, which increases the complexity of the task as it requires careful planning and color matching.', 'assets-used': ['cylinder/cylinder-template.urdf', 'stacking/stand.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CylinderStandAlignment(Task): + """"""Arrange four colored cylinders (red, blue, green, yellow) in order of their colors on four stands of matching color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Arrange the {color} cylinder on the {color} stand"" + self.task_completed_desc = ""done arranging cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and corresponding names + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + color_names = ['red', 'blue', 'green', 'yellow'] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + replace = {'DIM': cylinder_size, 'HALF': (cylinder_size[0] / 2, cylinder_size[1] / 2, cylinder_size[2] / 2), + 'COLOR': colors[i]} + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(cylinder_urdf, replace) + cylinder_id = env.add_object(urdf, cylinder_pose) + cylinders.append(cylinder_id) + + # Add stands. + # x, y, z dimensions for the asset size + stand_size = (0.05, 0.05, 0.005) + stand_urdf = 'stacking/stand.urdf' + stands = [] + for i in range(4): + stand_pose = self.get_random_pose(env, stand_size) + env.add_object(stand_urdf, stand_pose, color=colors[i], category='fixed') + stands.append(stand_pose) + + # Goal: each cylinder is on a stand of the same color. + for i in range(4): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[stands[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, + language_goal=self.lang_template.format(color=color_names[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-sorted-block-race"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-sorted-block-race"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-sorted-block-race: {'task-name': 'color-sorted-block-race', 'task-description': 'On one end of a tabletop, there are six blocks in two colors (three red and three blue). On the other end of the tabletop, two sets of three small marked zones are arranged in a straight line, one set for blue and one set for red. The task involves picking up one block at a time and placing it in the corresponding colored zone in a sequence from the bottom end zone to the top end zone. The blocks must be placed following the rule: the three colored blocks must be transported consecutively, e.g., first place all three blue blocks and then place all three red blocks. The challenge lies in careful transportation and placement of the blocks and follows the specific rule.', 'assets-used': ['block/block.urdf', 'zone/zone.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorSortedBlockRace(Task): + """"""Pick up blocks of two colors and place them in corresponding colored zones in a sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the blocks in the corresponding colored zones in sequence"" + self.task_completed_desc = ""done placing blocks in zones."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_colors = ['blue', 'red'] + zone_poses = [] + for color in zone_colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = ['blue', 'red'] + blocks = [] + for color in block_colors: + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in the corresponding colored zone. + for i, block in enumerate(blocks): + self.add_goal(objs=[block], matches=np.ones((1, 1)), targ_poses=[zone_poses[i//3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""multi-level-block-construction"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""multi-level-block-construction"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```multi-level-block-construction: {'task-name': 'multi-level-block-construction', 'task-description': 'Construct a two-level structure on a pallet using four blocks: two red and two blue. The lower level should be a rectangle created by placing the red blocks side by side. The upper level is made up by placing the blue blocks placed on top of the red blocks creating a line aligned perpendicular to the red blocks. The challenge lies in the precise placement of blocks, maintaining balance of the structure, and correct color arrangement.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class MultiLevelBlockConstruction(Task): + """"""Construct a two-level structure on a pallet using four blocks: two red and two blue. + The lower level should be a rectangle created by placing the red blocks side by side. + The upper level is made up by placing the blue blocks placed on top of the red blocks + creating a line aligned perpendicular to the red blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""construct a two-level structure on a pallet using four blocks: two red and two blue"" + self.task_completed_desc = ""done constructing multi-level block structure."" + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.015) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['blue']] + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.02, 0.02), (0, 0.02, 0.02), + (0, -0.02, 0.06), (0, 0.02, 0.06)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: red blocks are placed side by side on the pallet. + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, language_goal=self.lang_template) + + # Goal: blue blocks are stacked on top of the red blocks. + self.add_goal(objs=blocks[2:], matches=np.ones((2, 2)), targ_poses=targs[2:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-blocks-in-cylinder-maze"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-blocks-in-cylinder-maze"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-blocks-in-cylinder-maze: {'task-name': 'color-blocks-in-cylinder-maze', 'task-description': 'Pick up five differently colored blocks (red, blue, yellow, green, and orange) that are scattered randomly on the table top. Arrange three cylindrical containers in a row to create a maze-like structure. Place the red, yellow, and blue block into the first, second, and third cylinder from left respectively. Then, stack the green and orange block on top of any container, followed by placing the same color palette on the respective block.', 'assets-used': ['block/block.urdf', 'cylinder/cylinder-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorBlocksInCylinderMaze(Task): + """"""Pick up five differently colored blocks (red, blue, yellow, green, and orange) that are scattered randomly on the table top. Arrange three cylindrical containers in a row to create a maze-like structure. Place the red, yellow, and blue block into the first, second, and third cylinder from left respectively. Then, stack the green and orange block on top of any container, followed by placing the same color palette on the respective block."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""arrange the blocks in the cylinders and stack the green and orange blocks"" + self.task_completed_desc = ""done arranging blocks in cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add cylinders. + cylinder_size = (0.05, 0.05, 0.1) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinder_poses = [] + for _ in range(3): + cylinder_pose = self.get_random_pose(env, cylinder_size) + env.add_object(cylinder_urdf, cylinder_pose, 'fixed') + cylinder_poses.append(cylinder_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['yellow'], utils.COLORS['green'], utils.COLORS['orange']] + blocks = [] + for i in range(5): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Goal: red, yellow, and blue blocks are in the first, second, and third cylinder respectively. + self.add_goal(objs=blocks[:3], matches=np.ones((3, 3)), targ_poses=cylinder_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, language_goal=self.lang_template) + + # Goal: green and orange blocks are stacked on top of any cylinder. + self.add_goal(objs=blocks[3:], matches=np.ones((2, 3)), targ_poses=cylinder_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""create-pyramid-with-color-coded-ells"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""create-pyramid-with-color-coded-ells"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```create-pyramid-with-color-coded-ells: {'task-name': 'create-pyramid-with-color-coded-ells', 'task-description': ""There are four insertion ell-shaped objects ('insertion/ell.urdf') of different colors (red, blue, yellow, and green) placed randomly on the tabletop. The task is to pick up each of these objects and stack them onto a fixed-size pallet in the shape of a pyramid. The order of the pyramid from bottom to top should be red, blue, yellow, and green."", 'assets-used': ['insertion/ell.urdf', 'pallet/pallet.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class CreatePyramidWithColorCodedElls(Task): + """"""Pick up ell-shaped objects of different colors and stack them onto a pallet in the shape of a pyramid."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""stack the {color} ell on the pyramid"" + self.task_completed_desc = ""done stacking ell pyramid."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + + # Ell colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], + utils.COLORS['yellow'], utils.COLORS['green'] + ] + color_names = ['red', 'blue', 'yellow', 'green'] + + # Add Ells. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + objs = [] + for i in range(4): + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=colors[i]) + objs.append(ell_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, 0, 0.08)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: Ells are stacked in a pyramid (bottom row: red, middle row: blue, top row: yellow, green). + for i in range(4): + self.add_goal(objs=[objs[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template.format(color=color_names[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""move-piles-along-line"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""move-piles-along-line"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```move-piles-along-line: {'task-name': 'move-piles-along-line', 'task-description': 'Move three piles of small blocks, each pile a different color (red, blue, green), along three matching colored lines to three separate zones of the same color using a spatula.', 'assets-used': ['block/small.urdf', 'zone/zone.urdf', 'line/line-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class MovePilesAlongLine(Task): + """"""Move three piles of small blocks, each pile a different color (red, blue, green), + along three matching colored lines to three separate zones of the same color using a spatula."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""move the piles of blocks along the lines to the matching colored zones"" + self.task_completed_desc = ""done moving piles."" + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add three colored lines. + line_template = 'line/line-template.urdf' + line_colors = ['red', 'blue', 'green'] + line_poses = [] + for color in line_colors: + line_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + line_pose = self.get_random_pose(env, line_size) + replace = {'DIM': line_size, 'HALF': (line_size[0] / 2, line_size[1] / 2, line_size[2] / 2), 'COLOR': color} + line_urdf = self.fill_template(line_template, replace) + env.add_object(line_urdf, line_pose, 'fixed') + line_poses.append(line_pose) + + # Add three colored zones. + zone_template = 'zone/zone.urdf' + zone_poses = [] + for color in line_colors: + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2), 'COLOR': color} + zone_urdf = self.fill_template(zone_template, replace) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Add three piles of small blocks. + block_template = 'block/small.urdf' + block_colors = ['red', 'blue', 'green'] + block_ids = [] + for color in block_colors: + block_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + block_pose = self.get_random_pose(env, block_size) + replace = {'DIM': block_size, 'HALF': (block_size[0] / 2, block_size[1] / 2, block_size[2] / 2), 'COLOR': color} + block_urdf = self.fill_template(block_template, replace) + block_id = env.add_object(block_urdf, block_pose) + block_ids.append(block_id) + + # Add goals. + for i in range(3): + self.add_goal(objs=[block_ids[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i]], replace=False, + rotations=False, metric='zone', params=[(zone_poses[i], zone_size)], step_max_reward=1/3, + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-ordered-blocks-on-pallet"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-ordered-blocks-on-pallet"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-ordered-blocks-on-pallet: {'task-name': 'color-ordered-blocks-on-pallet', 'task-description': 'On a table there are six different colored blocks (red, blue, green, yellow, orange, and purple), a pallet, and a small corner structure. These colored blocks are arranged randomly within the small corner structure. The task involves picking up each colored block and placing it onto the pallet in specific color sequence: red, blue, green, yellow, orange, and finally purple.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'corner/corner-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorOrderedBlocksOnPallet(Task): + """"""Pick up each colored block and place it onto the pallet in specific color sequence: red, blue, green, yellow, orange, and finally purple."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the colored blocks onto the pallet in the following order: red, blue, green, yellow, orange, and purple"" + self.task_completed_desc = ""done placing blocks on the pallet."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.02) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['purple'] + ] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are placed on the pallet in the order of red, blue, green, yellow, orange, purple. + for i in range(6): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2], language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-ordered-container-arrangement"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-ordered-container-arrangement"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-ordered-container-arrangement: {'task-name': 'color-ordered-container-arrangement', 'task-description': 'On the tabletop, there are six containers and six blocks of different colors (red, blue, green, yellow, orange, purple). The task is to pick up each block and place it into a container of the same color, then arrange the containers in a line in the following color order: red, blue, green, yellow, orange, and purple.', 'assets-used': ['block/block.urdf', 'container/container-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorOrderedContainerArrangement(Task): + """"""Arrange six containers with blocks of matching colors in a specific color order."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""arrange the containers in the color order: red, blue, green, yellow, orange, and purple"" + self.task_completed_desc = ""done arranging containers."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define color order + color_order = ['red', 'blue', 'green', 'yellow', 'orange', 'purple'] + + # Add containers and blocks + container_template = 'container/container-template.urdf' + container_size = (0.12, 0.12, 0.02) + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_template, replace) + + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + containers = [] + blocks = [] + for color in color_order: + # Add container + container_pose = self.get_random_pose(env, container_size) + container_id = env.add_object(container_urdf, container_pose, color=utils.COLORS[color]) + containers.append(container_id) + + # Add block + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add subgoal to place block in container + self.add_goal(objs=[block_id], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/6, + language_goal=self.lang_template) + + # Add final goal to arrange containers in color order + container_poses = [self.get_random_pose(env, container_size) for _ in color_order] + self.add_goal(objs=containers, matches=np.eye(len(color_order)), targ_poses=container_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""multi-level-pyramid-construction"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""multi-level-pyramid-construction"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```multi-level-pyramid-construction: {'task-name': 'multi-level-pyramid-construction', 'task-description': 'Construct a two-level pyramid on a pallet using six blocks: three green and three blue. The first level should be a triangle created by placing the green blocks side by side. The second level should be built by placing the blue blocks on top of the green blocks, forming another triangle rotated 60 degrees with respect to the first one. The challenge lies in the precise placement of blocks, maintaining balance of the structure, and correct color arrangement.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class MultiLevelPyramidConstruction(Task): + """"""Construct a two-level pyramid on a pallet using six blocks: three green and three blue."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Construct a two-level pyramid on a pallet using six blocks: three green and three blue. The first level should be a triangle created by placing the green blocks side by side. The second level should be built by placing the blue blocks on top of the green blocks, forming another triangle rotated 60 degrees with respect to the first one."" + self.task_completed_desc = ""done constructing pyramid."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.35, 0.35, 0.01) # x, y, z dimensions for the pallet size + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_urdf = 'pallet/pallet.urdf' + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add blocks. + block_size = (0.04, 0.04, 0.04) # x, y, z dimensions for the block size + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['green']] * 3 + [utils.COLORS['blue']] * 3 # three green and three blue blocks + + blocks = [] + for color in block_colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0, 0.02), (0, 0.05, 0.02), # first level + (0, -0.025, 0.06), (0, 0.025, 0.06), (0, 0, 0.10)] # second level + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (first level: green blocks). + self.add_goal(objs=blocks[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(blocks=""the green blocks"", row=""bottom"")) + + # Goal: blocks are stacked in a pyramid (second level: blue blocks). + self.add_goal(objs=blocks[3:], matches=np.ones((3, 3)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(blocks=""the blue blocks"", row=""top""))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""align-balls-in-colored-boxes"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""align-balls-in-colored-boxes"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```align-balls-in-colored-boxes: {'task-name': 'align-balls-in-colored-boxes', 'task-description': 'On a tabletop, there are four balls and four boxes of different colors (red, blue, green, and yellow). Each ball is inside a box, but not corresponding to the color of the box. The task is to pick up each ball and place it in the box of the same color, in the specific sequence of red, blue, green and yellow from left to right. The challenge lies in the precise placement, color matching and sequence following.', 'assets-used': ['ball/ball.urdf', 'box/box-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class AlignBallsInColoredBoxes(Task): + """"""Align balls in colored boxes according to the color and sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""put the {color} ball in the {color} box"" + self.task_completed_desc = ""done aligning balls in boxes."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their sequence + colors = ['red', 'blue', 'green', 'yellow'] + + # Add boxes. + box_size = (0.12, 0.12, 0.12) + box_urdf = 'box/box-template.urdf' + box_poses = [] + boxes = [] + for i in range(4): + box_pose = self.get_random_pose(env, box_size) + box_id = env.add_object(box_urdf, box_pose, color=utils.COLORS[colors[i]]) + boxes.append(box_id) + box_poses.append(box_pose) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball.urdf' + balls = [] + for i in range(4): + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[colors[i]]) + balls.append(ball_id) + + # Goal: each ball is in the box of the same color. + for i in range(4): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[box_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""colored-balls-sorting-in-corner"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""colored-balls-sorting-in-corner"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```colored-balls-sorting-in-corner: {'task-name': 'colored-balls-sorting-in-corner', 'task-description': 'There are four balls and four corners of different colors (red, blue, green, and yellow). Each ball is located at a corner, but not corresponding to the color of the corner. The task is to pick up each ball and place it in the corner of the same color, in the specific sequence of red, blue, green and yellow, starting from the leftmost corner to the rightmost. The challenge lies in the precise placement, color matching and sequence following.', 'assets-used': ['ball/ball-template.urdf', 'corner/corner-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColoredBallsSortingInCorner(Task): + """"""Pick up each ball and place it in the corner of the same color, in the specific sequence of red, blue, green and yellow, starting from the leftmost corner to the rightmost."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} ball in the {color} corner"" + self.task_completed_desc = ""done sorting balls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define the colors and their sequence + colors = ['red', 'blue', 'green', 'yellow'] + + # Add corners. + corner_size = (0.12, 0.12, 0) + corner_urdf = 'corner/corner-template.urdf' + corner_poses = [] + for i in range(4): + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, 'fixed', color=utils.COLORS[colors[i]]) + corner_poses.append(corner_pose) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for i in range(4): + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[colors[i]]) + balls.append(ball_id) + + # Goal: each ball is in the corner of the same color. + for i in range(4): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[corner_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-ball-insertion"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-ball-insertion"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-ball-insertion: {'task-name': 'color-coordinated-ball-insertion', 'task-description': 'There are five differently-colored ell objects (red, blue, green, yellow, orange) and five sphere-shaped containers of matching colors. The task involves picking up each ell object and inserting it into the sphere container of the same color, but in a specific sequence from left to right: red, blue, green, yellow, and finally orange. The task is challenging due to the sequence, color coordination, and accuracy of insertion required.', 'assets-used': ['insertion/ell.urdf', 'sphere/sphere-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedBallInsertion(Task): + """"""Insert balls into the cylinders of the same color in the order of red, blue, green, and yellow."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""insert the {color} ball into the {color} cylinder"" + self.task_completed_desc = ""done inserting balls into cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their order + colors = ['red', 'blue', 'green', 'yellow'] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.05, 0.05, 0.1) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinder_poses = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + env.add_object(cylinder_urdf, cylinder_pose, category='fixed', color=utils.COLORS[colors[i]]) + cylinder_poses.append(cylinder_pose) + + # Add balls. + # x, y, z dimensions for the asset size + balls = [] + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + for i in range(4): + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[colors[i]]) + balls.append(ball_id) + + # Goal: each ball is in the corresponding color cylinder. + for i in range(4): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[cylinder_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-sequenced-pyramid-packing"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-sequenced-pyramid-packing"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-sequenced-pyramid-packing: {'task-name': 'color-sequenced-pyramid-packing', 'task-description': 'There are twelve cubes of different colors (three red, three green, three blue, and three yellow) scattered on the tabletop. The task is to pick up the cubes, sort them according to color into four pallets, and stack them in each pallet as a pyramid with the base layer containing two cubes and the top layer containing one cube. The challenge lies in the color-based sorting, the precise placement of cubes, and the construction of the pyramid in each pallet.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorSequencedPyramidPacking(Task): + """"""Sort cubes by color into four pallets and stack them in each pallet as a pyramid"""""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = ""sort the {color} cubes into the pallet and stack them as a pyramid"" + self.task_completed_desc = ""done sorting and stacking cubes."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallets. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.02) + pallet_urdf = 'pallet/pallet.urdf' + pallet_poses = [] + for _ in range(4): + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + pallet_poses.append(pallet_pose) + + # Cube colors. + colors = [ + utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['yellow'] + ] + + # Add cubes. + # x, y, z dimensions for the asset size + cube_size = (0.04, 0.04, 0.04) + cube_urdf = 'block/block.urdf' + + objs = [] + for i in range(12): + cube_pose = self.get_random_pose(env, cube_size) + cube_id = env.add_object(cube_urdf, cube_pose, color=colors[i%4]) + objs.append(cube_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos for pallet_pose in pallet_poses] + + # Goal: cubes are sorted by color and stacked in a pyramid in each pallet. + for i in range(4): + self.add_goal(objs=objs[i*3:(i+1)*3], matches=np.ones((3, 3)), targ_poses=targs[i*3:(i+1)*3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(color=list(utils.COLORS.keys())[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""ball-sorting-with-blocks-barrier"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""ball-sorting-with-blocks-barrier"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```ball-sorting-with-blocks-barrier: {'task-name': 'ball-sorting-with-blocks-barrier', 'task-description': 'On a tabletop, there are four balls and four zones of different colors (red, blue, green, and yellow). Each ball is located behind a line of small blocks of the same color. The task is to pick up each ball and place it into the zone of the same color, but without knocking over the blocks. The challenge lies in the precise navigation over the block barriers and color matching.', 'assets-used': ['ball/ball-template.urdf', 'zone/zone.urdf', 'block/small.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class BallSortingWithBlocksBarrier(Task): + """"""Pick up each ball and place it into the zone of the same color, but without knocking over the blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} ball in the {color} zone without knocking over the blocks"" + self.task_completed_desc = ""done sorting balls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for the balls and zones + colors = ['red', 'blue', 'green', 'yellow'] + + # Add zones and blocks. + zone_size = (0.12, 0.12, 0) + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/small.urdf' + zone_urdf = 'zone/zone.urdf' + zones = [] + blocks = [] + for color in colors: + # Add zone of specific color + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zones.append(zone_pose) + + # Add line of blocks of the same color + for _ in range(5): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for color in colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[color]) + balls.append(ball_id) + + # Goal: each ball is in a zone of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[zones[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-block-bridge"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-block-bridge"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-block-bridge: {'task-name': 'color-coordinated-block-bridge', 'task-description': 'Construct a bridge by interleaving three differently colored blocks (red, blue, and green) on a pallet in a specific sequence - red block at the edges, blue block in the middle, and a green block on top of the red and blue blocks. Repeat this sequence until a bridge is formed across the length of the pallet.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedBlockBridge(Task): + """"""Construct a bridge by interleaving three differently colored blocks (red, blue, and green) on a pallet in a specific sequence - red block at the edges, blue block in the middle, and a green block on top of the red and blue blocks. Repeat this sequence until a bridge is formed across the length of the pallet."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""construct a bridge by interleaving three differently colored blocks (red, blue, and green) on a pallet in a specific sequence"" + self.task_completed_desc = ""done constructing the bridge."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.30, 0.15, 0.02) + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object('pallet/pallet.urdf', pallet_pose, 'fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green']] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + + objs = [] + for i in range(9): # 3 sets of 3 colored blocks + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i % 3]) + objs.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0, 0.02), (0, 0.05, 0.02), # bottom layer + (0, -0.05, 0.06), (0, 0, 0.06), (0, 0.05, 0.06), # middle layer + (0, -0.05, 0.10), (0, 0, 0.10), (0, 0.05, 0.10)] # top layer + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a bridge (bottom layer: red, blue, red). + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (middle layer: green, green, green). + self.add_goal(objs=objs[3:6], matches=np.ones((3, 3)), targ_poses=targs[3:6], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (top layer: red, blue, red). + self.add_goal(objs=objs[6:], matches=np.ones((3, 3)), targ_poses=targs[6:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-cylinder-pyramid"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-cylinder-pyramid"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-cylinder-pyramid: {'task-name': 'color-coordinated-cylinder-pyramid', 'task-description': 'Construct a pyramid on a pallet using four cylinders of different colors (red, blue, green, and yellow). The first level should consist of a red cylinder and a blue cylinder side by side. The second level should consist of a green cylinder placed on top of the red and blue cylinders. The third and final level should consist of a yellow cylinder placed on top of the green cylinder. The challenge lies in the precise placement of cylinders, maintaining the balance of the structure, and correct color arrangement.', 'assets-used': ['cylinder/cylinder-template.urdf', 'pallet/pallet.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedCylinderPyramid(Task): + """"""Construct a pyramid on a pallet using four cylinders of different colors (red, blue, green, and yellow)."""""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = ""make the {row} row with {cylinder}"" + self.task_completed_desc = ""done stacking cylinder pyramid."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.15, 0.15, 0.005) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + + # Cylinder colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'] + ] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=colors[i]) + cylinders.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0.05, 0.03), + (0, 0, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: cylinders are stacked in a pyramid (bottom row: red, blue). + self.add_goal(objs=cylinders[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, + language_goal=self.lang_template.format(cylinder=""the red and blue cylinders"", row=""bottom"")) + + # Goal: cylinders are stacked in a pyramid (middle row: green). + self.add_goal(objs=cylinders[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*1, + language_goal=self.lang_template.format(cylinder=""the green cylinder"", row=""middle"")) + + # Goal: cylinders are stacked in a pyramid (top row: yellow). + self.add_goal(objs=cylinders[3:], matches=np.ones((1, 1)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*1, + language_goal=self.lang_template.format(cylinder=""the yellow cylinder"", row=""top""))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""sweep-and-sort-blocks"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""sweep-and-sort-blocks"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```sweep-and-sort-blocks: {'task-name': 'sweep-and-sort-blocks', 'task-description': 'Sweep a pile of small blocks of different colors (red, blue, green, and yellow) into their corresponding colored zones marked on the tabletop. The challenge lies in the sweeping action, precise placement, and color coordination.', 'assets-used': ['zone/zone.urdf', 'block/small.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class SweepAndSortBlocks(Task): + """"""Sweep a pile of small blocks of different colors (red, blue, green, and yellow) into their corresponding colored zones marked on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""sweep the pile of {color} blocks into the {color} square"" + self.task_completed_desc = ""done sweeping and sorting."" + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add colored zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + colors = ['red', 'blue', 'green', 'yellow'] + zone_poses = [] + for color in colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add piles of colored blocks. + block_urdf = 'block/small.urdf' + block_size = (0.04, 0.04, 0.04) + piles = [] + for color in colors: + pile = [] + for _ in range(10): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + pile.append(block_id) + piles.append(pile) + + # Add goals for each color. + for i, color in enumerate(colors): + self.add_goal(objs=piles[i], matches=np.ones((10, 1)), targ_poses=[zone_poses[i]], replace=True, + rotations=False, metric='zone', params=[(zone_poses[i], zone_size)], step_max_reward=1, + language_goal=self.lang_template.format(color=color))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""align-cylinders-in-zones"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""align-cylinders-in-zones"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```align-cylinders-in-zones: {'task-name': 'align-cylinders-in-zones', 'task-description': 'Place four differently colored cylinders (red, blue, green, yellow) each into a matching colored zone. But, the zones are surrounded by small blocks, which the robot needs to move out of the way without knocking them out of their respective zones. The challenge includes precise placement of cylinders, color matching, and careful navigation around the small blocks.', 'assets-used': ['cylinder/cylinder-template.urdf', 'zone/zone.urdf', 'block/small.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class AlignCylindersInZones(Task): + """"""Place four differently colored cylinders each into a matching colored zone. + The zones are surrounded by small blocks, which the robot needs to move out of the way + without knocking them out of their respective zones."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} cylinder in the {color} zone"" + self.task_completed_desc = ""done aligning cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Cylinder colors. + colors = ['red', 'blue', 'green', 'yellow'] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=utils.COLORS[colors[i]]) + cylinders.append(cylinder_id) + + # Add zones. + # x, y, z dimensions for the asset size + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + + zones = [] + for i in range(4): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, color=utils.COLORS[colors[i]], category='fixed') + zones.append(zone_pose) + + # Add small blocks around the zones. + # x, y, z dimensions for the asset size + block_size = (0.02, 0.02, 0.02) + block_urdf = 'block/small.urdf' + + for _ in range(16): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose) + + # Goal: each cylinder is in a matching colored zone. + for i in range(4): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[zones[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""sphere-container-color-match"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""sphere-container-color-match"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```sphere-container-color-match: {'task-name': 'sphere-container-color-match', 'task-description': 'On a tabletop, there are four spheres of different colors (red, blue, green, and yellow) inside four containers of a different color (red, blue, green, and yellow). The task is to pick up each sphere and place it into a container of the same color. The task is challenging due to the manipulation of spherical objects and the color coordination required.', 'assets-used': ['sphere/sphere.urdf', 'container/container-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class SphereContainerColorMatch(Task): + """"""Pick up each sphere and place it into a container of the same color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 4 + self.lang_template = ""put the {color} sphere in the {color} container"" + self.task_completed_desc = ""done matching spheres and containers."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and corresponding names + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + color_names = ['red', 'blue', 'green', 'yellow'] + + # Add containers. + container_size = (0.12, 0.12, 0.12) + container_urdf = 'container/container-template.urdf' + containers = [] + for i in range(4): + container_pose = self.get_random_pose(env, container_size) + container_id = env.add_object(container_urdf, container_pose, color=colors[i]) + containers.append(container_id) + + # Add spheres. + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + spheres = [] + for i in range(4): + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=colors[i]) + spheres.append(sphere_id) + + # Goal: each sphere is in a container of the same color. + for i in range(4): + self.add_goal(objs=[spheres[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(containers[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=color_names[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""insert-ell-along-square-path"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""insert-ell-along-square-path"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```insert-ell-along-square-path: {'task-name': 'insert-ell-along-square-path', 'task-description': 'On the tabletop, there is a square path marked by small blocks. Along the path, there are four colored ell-shaped blocks (red, blue, green, and yellow) and four fixtures of matching colors. The task is to pick up each ell block and insert it into the fixture of the same color. However, the robot must move each ell block along the marked square path to reach the fixture. The task is challenging because it requires precise navigation along the path, color coordination, and insertion accuracy.', 'assets-used': ['block/small.urdf', 'insertion/ell.urdf', 'insertion/fixture.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class InsertEllAlongSquarePath(Task): + """"""Pick up each ell block and insert it into the fixture of the same color. However, the robot must move each ell block along the marked square path to reach the fixture."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""move the {color} ell block into the {color} fixture"" + self.task_completed_desc = ""done inserting ell blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Ell block colors. + colors = ['red', 'blue', 'green', 'yellow'] + + # Add ell blocks and fixtures. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + fixture_urdf = 'insertion/fixture.urdf' + ell_blocks = [] + fixtures = [] + for color in colors: + # Add ell block + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[color]) + ell_blocks.append(ell_id) + + # Add fixture + fixture_pose = self.get_random_pose(env, ell_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color]) + fixtures.append(fixture_id) + + # Goal: each ell block is inserted into the fixture of the same color. + for i in range(len(colors)): + self.add_goal(objs=[ell_blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(colors), + language_goal=self.lang_template.format(color=colors[i])) + + # Add square path marked by small blocks. + path_block_size = (0.02, 0.02, 0.02) + path_block_urdf = 'block/small.urdf' + path_block_color = utils.COLORS['gray'] + for _ in range(16): + path_block_pose = self.get_random_pose(env, path_block_size) + env.add_object(path_block_urdf, path_block_pose, color=path_block_color)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-box-ball-matching"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-box-ball-matching"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-box-ball-matching: {'task-name': 'color-coordinated-box-ball-matching', 'task-description': 'On the tabletop, there are four boxes of different colors (red, blue, green, and yellow) and four balls of corresponding colors. The task is to pick up each ball and place it inside the box of the same color, however, the boxes are placed in a straight line with a row of small blocks acting as a barrier between the boxes and the balls. The challenge lies in the precise placement, color matching, and the navigation around the barrier without knocking over any small blocks.', 'assets-used': ['box/box-template.urdf', 'ball/ball-template.urdf', 'block/small.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedBoxBallMatching(Task): + """"""Pick up each ball and place it inside the box of the same color, navigate around the barrier without knocking over any small blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""put the {color} ball in the {color} box"" + self.task_completed_desc = ""done placing balls in boxes."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for the boxes and balls + colors = ['red', 'blue', 'green', 'yellow'] + + # Add boxes. + box_size = (0.05, 0.05, 0.05) + box_urdf = 'box/box-template.urdf' + box_poses = [] + for color in colors: + box_pose = self.get_random_pose(env, box_size) + env.add_object(box_urdf, box_pose, color=color, category='fixed') + box_poses.append(box_pose) + + # Add balls. + balls = [] + ball_size = (0.02, 0.02, 0.02) + ball_urdf = 'ball/ball-template.urdf' + for color in colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add small blocks as barriers. + barrier_size = (0.01, 0.01, 0.01) + barrier_urdf = 'block/small.urdf' + for _ in range(10): + barrier_pose = self.get_random_pose(env, barrier_size) + env.add_object(barrier_urdf, barrier_pose, category='fixed') + + # Goal: each ball is in the box of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[box_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""cylinder-balancing-and-placement"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""cylinder-balancing-and-placement"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```cylinder-balancing-and-placement: {'task-name': 'cylinder-balancing-and-placement', 'task-description': 'On a table, there are three differently colored cylinders (red, green, and blue) and three square zones of matching colors. The task involves picking up each cylinder and balancing it on its end at the center of the corresponding colored zone, in the sequence of red, green, and blue from left to right. The task is challenging due to precise balancing required in placing the cylinders, color matching, and sequence following.', 'assets-used': ['cylinder/cylinder-template.urdf', 'zone/zone.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CylinderBalancingAndPlacement(Task): + """"""Pick up each cylinder and balance it on its end at the center of the corresponding colored zone."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""balance the {color} cylinder in the {color} zone"" + self.task_completed_desc = ""done balancing and placing cylinders."" + self.colors = ['red', 'green', 'blue'] + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for color in self.colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.12) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for color in self.colors: + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=utils.COLORS[color]) + cylinders.append(cylinder_id) + + # Goal: each cylinder is balanced in the corresponding colored zone. + for i in range(len(cylinders)): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/3, + language_goal=self.lang_template.format(color=self.colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-sphere-and-cylinder-assembly"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-sphere-and-cylinder-assembly"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-sphere-and-cylinder-assembly: {'task-name': 'color-coordinated-sphere-and-cylinder-assembly', 'task-description': 'The robot starts with four spheres of different colors (red, blue, green, yellow) and four cylinders of matching colors. The task is to pick up each sphere and place it on top of the cylinder of the same color, forming four sphere-and-cylinder pairs. However, the challenge here is to do this in a specific color sequence - red, blue, green, and finally yellow.', 'assets-used': ['sphere/sphere-template.urdf', 'cylinder/cylinder-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedSphereAndCylinderAssembly(Task): + """"""Pick up each sphere and place it on top of the cylinder of the same color, in a specific color sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} sphere on the {color} cylinder"" + self.task_completed_desc = ""done placing spheres on cylinders."" + self.colors = ['red', 'blue', 'green', 'yellow'] + self.color_sequence = ['red', 'blue', 'green', 'yellow'] + + def reset(self, env): + super().reset(env) + + # Add spheres and cylinders. + sphere_size = (0.05, 0.05, 0.05) + cylinder_size = (0.05, 0.05, 0.1) + sphere_template = 'sphere/sphere-template.urdf' + cylinder_template = 'cylinder/cylinder-template.urdf' + + # Add spheres and cylinders of each color. + for color in self.colors: + sphere_pose = self.get_random_pose(env, sphere_size) + cylinder_pose = self.get_random_pose(env, cylinder_size) + sphere_id = env.add_object(sphere_template, sphere_pose, color=color) + cylinder_id = env.add_object(cylinder_template, cylinder_pose, color=color) + + # Goal: each sphere is on top of the cylinder of the same color. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[cylinder_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=color)) + + # The task is completed in a specific color sequence. + self.color_sequence = ['red', 'blue', 'green', 'yellow']<|endoftext|>" +" + Instructions: ========= +Please describe the task ""sequential-block-insertion"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""sequential-block-insertion"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```sequential-block-insertion: {'task-name': 'sequential-block-insertion', 'task-description': 'There are four blocks of different colors (red, blue, green, yellow) and four fixtures of matching colors. The task involves picking up each block and inserting it into the fixture of the same color, in the specific sequence of red, blue, green, and yellow. However, the challenge lies in the fact that the blocks and fixtures are initially arranged in a mixed order, demanding careful navigation, precise insertion, color matching, and sequence following.', 'assets-used': ['insertion/fixture.urdf', 'block/block.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class SequentialBlockInsertion(Task): + """"""Pick up blocks of different colors and insert them into the fixture of the same color in a specific sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert the {color} block into the {color} fixture"" + self.task_completed_desc = ""done inserting blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define the sequence of colors + colors = ['red', 'blue', 'green', 'yellow'] + + # Add fixtures. + # x, y, z dimensions for the asset size + fixture_size = (0.12, 0.12, 0) + fixture_urdf = 'insertion/fixture.urdf' + fixtures = [] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color], category='fixed') + fixtures.append(fixture_id) + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for color in colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in the fixture of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""sequential-insertion-and-stacking"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""sequential-insertion-and-stacking"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```sequential-insertion-and-stacking: {'task-name': 'sequential-insertion-and-stacking', 'task-description': 'The tabletop contains three fixtures and three ell-shaped blocks of different colors - red, blue, and green. The task is to first pick up and insert each ell block into the corresponding colored fixture in the sequence of red, blue, and green. After successful insertion, the robot must pick up the three blocks again from the fixtures and stack them in a corner of the tabletop in the same color sequence - red at the bottom, blue in the middle, and green on top.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf', 'corner/corner-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class SequentialInsertionAndStacking(Task): + """"""Pick up and insert each ell block into the corresponding colored fixture in the sequence of red, blue, and green. After successful insertion, pick up the three blocks again from the fixtures and stack them in a corner of the tabletop in the same color sequence - red at the bottom, blue in the middle, and green on top."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""insert the {color} ell block into the {color} fixture and then stack them in the corner"" + self.task_completed_desc = ""done inserting and stacking."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add fixtures. + fixture_size = (0.12, 0.12, 0) + fixture_urdf = 'insertion/fixture.urdf' + fixture_poses = [] + colors = ['red', 'blue', 'green'] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + env.add_object(fixture_urdf, fixture_pose, category='fixed', color=utils.COLORS[color]) + fixture_poses.append(fixture_pose) + + # Add ell blocks. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + ells = [] + for color in colors: + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[color]) + ells.append(ell_id) + + # Goal: each ell block is in the corresponding colored fixture. + for i in range(3): + self.add_goal(objs=[ells[i]], matches=np.ones((1, 1)), targ_poses=[fixture_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/3) + self.lang_goals.append(self.lang_template.format(color=colors[i])) + + # Add corner. + corner_size = (0.12, 0.12, 0) + corner_pose = self.get_random_pose(env, corner_size) + corner_urdf = 'corner/corner-template.urdf' + env.add_object(corner_urdf, corner_pose, category='fixed') + + # Goal: ell blocks are stacked in the corner in the color sequence - red at the bottom, blue in the middle, and green on top. + stack_poses = [(0, 0, 0.04), (0, 0, 0.08), (0, 0, 0.12)] + targs = [(utils.apply(corner_pose, i), corner_pose[1]) for i in stack_poses] + self.add_goal(objs=ells, matches=np.ones((3, 3)), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/3, + language_goal=""stack the ell blocks in the corner in the color sequence - red at the bottom, blue in the middle, and green on top"")<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-block-shifting"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-block-shifting"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-block-shifting: {'task-name': 'color-coordinated-block-shifting', 'task-description': 'On a tabletop, there are three zones marked in three different colors (red, blue, and green) and nine blocks of matching colors (three red, three blue, and three green). Each zone initially contains three blocks of a single color, but the colors of the blocks and zones do not match. The task involves picking up each block and precisely placing it in the zone of the same color. However, there are a few small blocks randomly scattered in the path between the zones. The robot has to strategically navigate around these blocks without knocking them over while transporting the blocks to the corresponding zones. The challenge lies in the precise navigation, placement of the blocks, color matching while avoiding the blocks.', 'assets-used': ['zone/zone.urdf', 'block/block.urdf', 'block/small.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedBlockShifting(Task): + """"""Pick up each block and precisely place it in the zone of the same color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""move the {color} blocks to the {color} zone"" + self.task_completed_desc = ""done moving blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_colors = ['yellow', 'blue', 'green'] + zone_poses = [] + for color in zone_colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + blocks = [] + for color in zone_colors: + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add small blocks as obstacles. + small_block_size = (0.02, 0.02, 0.02) + small_block_urdf = 'stacking/block.urdf' + for _ in range(5): + small_block_pose = self.get_random_pose(env, small_block_size) + env.add_object(small_block_urdf, small_block_pose) + + # Goal: each block is in the zone of the same color. + for i in range(9): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i//3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/9, + language_goal=self.lang_template.format(color=zone_colors[i//3]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""guided-block-path"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""guided-block-path"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```guided-block-path: {'task-name': 'guided-block-path', 'task-description': 'On the tabletop, there are four colored blocks (red, blue, green, and yellow) and four lines of the corresponding colors. The task is to pick up each block and move it along the line of the same color from start to end. The challenge lies in precise navigation along the line, color coordination, and block manipulation.', 'assets-used': ['block/block.urdf', 'line/line-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class GuidedBlockPath(Task): + """"""Pick up each block and move it along the line of the same color from start to end."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""move the {color} block along the {color} line from start to end"" + self.task_completed_desc = ""done moving blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and corresponding names + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + color_names = ['red', 'blue', 'green', 'yellow'] + + # Add lines and blocks. + # x, y, z dimensions for the asset size + line_size = (0.3, 0.01, 0.01) + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + line_urdf = 'line/line-template.urdf' + + blocks = [] + lines = [] + for i in range(4): + # Add line + line_pose = self.get_random_pose(env, line_size) + env.add_object(line_urdf, line_pose, color=colors[i], category='fixed') + lines.append(line_pose) + + # Add block + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Add goal + self.add_goal(objs=[block_id], matches=np.ones((1, 1)), targ_poses=[line_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=color_names[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""mixed-color-block-barrier-insertion"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""mixed-color-block-barrier-insertion"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```mixed-color-block-barrier-insertion: {'task-name': 'mixed-color-block-barrier-insertion', 'task-description': 'There are four different colored blocks (red, blue, green, and yellow), and four fixtures in corresponding colors. Two barriers, each made of three blocks (orange, purple, and brown), are placed in between the blocks and fixtures, forming a path that the robot must navigate. The task involves picking up each colored block, navigating the barriers, and inserting each block into the fixture of the same color. The fixtures are arranged in a sequence from left to right: red, blue, green, and yellow, providing a challenge in precise navigation, color coordination, and insertion.', 'assets-used': ['block/block.urdf', 'insertion/fixture.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class MixedColorBlockBarrierInsertion(Task): + """"""Pick up each colored block, navigate the barriers, and insert each block into the fixture of the same color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert the {color} block into the {color} fixture"" + self.task_completed_desc = ""done inserting blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for blocks and fixtures + colors = ['red', 'blue', 'green', 'yellow'] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for color in colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add fixtures. + fixture_size = (0.06, 0.06, 0.06) + fixture_urdf = 'insertion/fixture.urdf' + fixtures = [] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color]) + fixtures.append(fixture_id) + + # Add barriers. + barrier_size = (0.12, 0.04, 0.04) + barrier_colors = ['orange', 'purple', 'brown'] + for _ in range(2): + for color in barrier_colors: + barrier_pose = self.get_random_pose(env, barrier_size) + env.add_object(block_urdf, barrier_pose, color=utils.COLORS[color]) + + # Goal: each block is inserted into the fixture of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""ball-in-bowl-obstacle-course"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""ball-in-bowl-obstacle-course"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```ball-in-bowl-obstacle-course: {'task-name': 'ball-in-bowl-obstacle-course', 'task-description': 'With the tabletop setup consisting of a maze of small blocks, the task requires the robot to pick up four balls of different colors (red, blue, green, yellow) and place each of them into the corresponding colored bowls strategically positioned at different corners of the maze, without knocking over any blocks, demanding careful navigation and color coordination.', 'assets-used': ['ball/ball-template.urdf', 'block/small.urdf', 'bowl/bowl.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class BallInBowlObstacleCourse(Task): + """"""Navigate through a maze of blocks, pick up balls of different colors and place them in the corresponding colored bowls."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""navigate through the maze and place the {color} ball in the {color} bowl"" + self.task_completed_desc = ""done placing balls in bowls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks to form a maze. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/small.urdf' + for _ in range(10): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose, category='fixed') + + # Add balls of different colors. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + colors = ['red', 'blue', 'green', 'yellow'] + balls = [] + for color in colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add bowls of different colors at different corners of the maze. + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowls = [] + for color in colors: + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_id = env.add_object(bowl_urdf, bowl_pose, color=color, category='fixed') + bowls.append(bowl_id) + + # Goal: each ball is in the bowl of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(bowls[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""ball-in-bowl-obstacle-course-new"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""ball-in-bowl-obstacle-course-new"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```ball-in-bowl-obstacle-course-new: {'task-name': 'ball-in-bowl-obstacle-course-new', 'task-description': 'With the tabletop setup consisting of a maze of small blocks, the task requires the robot to pick up four balls of different colors (red, blue, green, yellow) and place each of them into the corresponding colored bowls strategically positioned at different corners of the maze, without knocking over any blocks, demanding careful navigation and color coordination.', 'assets-used': ['ball/ball-template.urdf', 'block/small.urdf', 'bowl/bowl.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class BallInBowlObstacleCourseNew(Task): + """"""Navigate through a maze of blocks, pick up balls of different colors and place them in the corresponding colored bowls."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""put the {color} ball in the {color} bowl"" + self.task_completed_desc = ""done placing balls in bowls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks to form a maze. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/small.urdf' + for _ in range(10): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose, category='fixed') + + # Add balls of different colors. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + colors = ['red', 'blue', 'green', 'yellow'] + balls = [] + for color in colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add bowls of different colors at different corners of the maze. + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowls = [] + for color in colors: + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_id = env.add_object(bowl_urdf, bowl_pose, color=color, category='fixed') + bowls.append(bowl_id) + + # Goal: each ball is in the bowl of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(bowls[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-arch-construction"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-arch-construction"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-arch-construction: {'task-name': 'color-coordinated-arch-construction', 'task-description': 'The task is to construct an arch using six blocks: three red and three blue. The blocks are initially placed in a container. The robot needs to pick each block and place it on a pallet in the following arrangement: place two red blocks in parallel on the pallet, then place a blue block on top of the red blocks to form an arch. Repeat the process with the remaining blocks, placing them on top of the first arch to form a second layer. The task is challenging due to the need for precise placement of the blocks and maintaining the balance of the structure.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'container/container-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedArchConstruction(Task): + """"""Construct an arch using six blocks: three red and three blue."""""" + + def __init__(self): + super().__init__() + self.max_steps = 6 + self.lang_template = ""construct an arch using six blocks: three red and three blue"" + self.task_completed_desc = ""done constructing arch."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.15, 0.15, 0.005) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['blue'], + utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['blue']] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0.05, 0.02), + (0, 0, 0.06), (0, -0.05, 0.08), + (0, 0.05, 0.08), (0, 0, 0.12)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in an arch (bottom row: red, red, blue). + self.add_goal(objs=blocks[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) + + # Goal: blocks are stacked in an arch (top row: red, red, blue). + self.add_goal(objs=blocks[3:], matches=np.ones((3, 3)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template)<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-zone-arrangement"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-zone-arrangement"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-zone-arrangement: {'task-name': 'color-coordinated-zone-arrangement', 'task-description': 'On the tabletop, there are nine blocks of three different colors (three red, three blue, and three green) and three pallets of matching colors (one red, one blue, one green). The task is to pick up each block and place it on the pallet of the same color, arranging the blocks on each pallet in a line. However, there are a few small blocks randomly scattered on the tabletop, which the robot has to navigate around without knocking them over while transporting the blocks to the corresponding pallets. The challenge lies in the precise navigation, placement of the blocks, color matching, and maintaining the balance on the pallets.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'block/small.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedZoneArrangement(Task): + """"""Pick up blocks of different colors and place them on the pallets of the same color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""place the {color} blocks on the {color} pallet"" + self.task_completed_desc = ""done arranging blocks on pallets."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallets. + # x, y, z dimensions for the asset size + pallet_size = (0.12, 0.12, 0.02) + pallet_urdf = 'pallet/pallet.urdf' + pallet_colors = ['red', 'blue', 'green'] + pallet_poses = [] + for color in pallet_colors: + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed', color=utils.COLORS[color]) + pallet_poses.append(pallet_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + for color in pallet_colors: + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add small blocks as obstacles. + small_block_size = (0.02, 0.02, 0.02) + small_block_urdf = 'block/small.urdf' + for _ in range(5): + small_block_pose = self.get_random_pose(env, small_block_size) + env.add_object(small_block_urdf, small_block_pose) + + # Goal: each block is on the pallet of the same color. + for i in range(9): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[pallet_poses[i // 3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 9, + language_goal=self.lang_template.format(color=pallet_colors[i // 3]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-cylinder-stand-assembly"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-cylinder-stand-assembly"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-cylinder-stand-assembly: {'task-name': 'color-coordinated-cylinder-stand-assembly', 'task-description': 'The robot starts with four cylinders of different colors (red, blue, green, yellow) and four stands of matching colors. The task is to pick up each cylinder and place it on top of the stand of the same color, forming four cylinder-and-stand pairs. However, the challenge here is to do this in a specific color sequence - green, yellow, blue, and finally red, while also ensuring that the cylinders do not topple over.', 'assets-used': ['cylinder/cylinder-template.urdf', 'stacking/stand.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedCylinderStandAssembly(Task): + """"""Pick up each cylinder and place it on top of the stand of the same color, in a specific color sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""place the {color} cylinder on the {color} stand"" + self.task_completed_desc = ""done placing cylinders on stands."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their sequence + colors = ['green', 'yellow', 'blue', 'red'] + color_sequence = [utils.COLORS[color] for color in colors] + + # Add stands. + stand_size = (0.04, 0.04, 0.04) + stand_urdf = 'stacking/stand.urdf' + stand_poses = [] + for i in range(4): + stand_pose = self.get_random_pose(env, stand_size) + env.add_object(stand_urdf, stand_pose, color=color_sequence[i], category='fixed') + stand_poses.append(stand_pose) + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=color_sequence[i]) + cylinders.append(cylinder_id) + + # Goal: each cylinder is on the stand of the same color, in the specified color sequence. + for i in range(4): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[stand_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-ball-stacking"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-ball-stacking"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-ball-stacking: {'task-name': 'color-coordinated-ball-stacking', 'task-description': 'There are four balls of different colors (red, blue, green, yellow), and four containers of matching colors on the table. The task is to pick up each ball and stack it on top of the corresponding colored container. However, the stacking should be done in a specific color sequence - blue at the bottom, followed by yellow, then green, and finally red at the top. This task enforces challenging skills due to the precision required for stacking the balls, color coordination, and sequencing.', 'assets-used': ['ball/ball-template.urdf', 'container/container-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedBallStacking(Task): + """"""Stack balls on top of the corresponding colored containers in a specific color sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""stack the balls on top of the corresponding colored containers in the sequence blue, yellow, green, red"" + self.task_completed_desc = ""done stacking balls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define the color sequence + color_sequence = ['blue', 'yellow', 'green', 'red'] + + # Add containers. + container_size = (0.12, 0.12, 0.12) + container_urdf = 'container/container-template.urdf' + container_poses = [] + containers = [] + for color in color_sequence: + container_pose = self.get_random_pose(env, container_size) + container_id = env.add_object(container_urdf, container_pose, color=utils.COLORS[color]) + container_poses.append(container_pose) + containers.append(container_id) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for color in color_sequence: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[color]) + balls.append(ball_id) + + # Goal: each ball is stacked on top of the corresponding colored container in the color sequence. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[container_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(obj=color_sequence[i])) + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_ball = np.random.rand() > 0.5 + urdf = ball_urdf if is_ball else container_urdf + size = ball_size if is_ball else container_size + pose = self.get_random_pose(env, obj_size=size) + color = np.random.choice(list(utils.COLORS.keys())) + + obj_id = env.add_object(urdf, pose, color=utils.COLORS[color]) + n_distractors += 1<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coded-blocks-on-corner"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coded-blocks-on-corner"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coded-blocks-on-corner: {'task-name': 'color-coded-blocks-on-corner', 'task-description': 'On a tabletop, there are four blocks of different colors (red, blue, green, and yellow) and a corner structure. The task involves picking up each block and placing it in the corner structure in a specific color sequence: from left to right, place red, blue, green, and finally yellow. The blocks must be arranged such that they form a straight line along the corner. The challenge lies in the precise placement, color coordination, and maintaining the balance of the blocks along the corner.', 'assets-used': ['block/block.urdf', 'corner/corner-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCodedBlocksOnCorner(Task): + """"""Pick up blocks of different colors and place them in a corner structure in a specific color sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""place the blocks in the corner in the sequence red, blue, green, yellow"" + self.task_completed_desc = ""done placing blocks in the corner."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add corner structure. + corner_size = (0.15, 0.15, 0.05) + corner_pose = self.get_random_pose(env, corner_size) + corner_urdf = 'corner/corner-template.urdf' + env.add_object(corner_urdf, corner_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, 0, 0.08)] + targs = [(utils.apply(corner_pose, i), corner_pose[1]) for i in place_pos] + + # Goal: blocks are placed in the corner in the sequence red, blue, green, yellow. + for i in range(4): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, + language_goal=self.lang_template.format(blocks=""the red, blue, green, yellow blocks""))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""insertion-in-color-sequenced-zones"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""insertion-in-color-sequenced-zones"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```insertion-in-color-sequenced-zones: {'task-name': 'insertion-in-color-sequenced-zones', 'task-description': 'On the table, there are four differently-colored insertion ell objects (red, blue, green, yellow) and four zones on the tabletop marked in the same colors. Initially, each ell is placed in a zone but not corresponding to the color of the zone. The task is to pick up each ell and place it in the zone of the same color, in the specific sequence of red, blue, green, and yellow from left to right, requiring careful navigation, precise placement, color matching, and sequence following.', 'assets-used': ['insertion/ell.urdf', 'zone/zone.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertionInColorSequencedZones(Task): + """"""Pick up each ell and place it in the zone of the same color, in the specific sequence of red, blue, green, and yellow from left to right."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} ell in the {color} zone"" + self.task_completed_desc = ""done placing ells in color sequenced zones."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Ell colors. + colors = ['red', 'blue', 'green', 'yellow'] + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for i in range(4): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[colors[i]]) + zone_poses.append(zone_pose) + + # Add ells. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + ells = [] + for i in range(4): + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[colors[i]]) + ells.append(ell_id) + + # Goal: each ell is in the zone of the same color. + for i in range(4): + self.add_goal(objs=[ells[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-zone-stacking"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-zone-stacking"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-zone-stacking: {'task-name': 'color-coordinated-zone-stacking', 'task-description': 'On the tabletop, there are three zones and nine blocks of three different colors (red, blue, green). Each color has three blocks and the blocks are scattered randomly on the table. The task is to pick up the blocks and stack them in the zones to form a pyramid shape. Each pyramid should contain blocks of the same color with two blocks on the base and one block on top. The zones with the pyramids should be arranged in a straight line in the following color order: red, blue, green from left to right. The challenge lies in the color coordination, precise stacking and the arrangement of the zones.', 'assets-used': ['block/block.urdf', 'zone/zone.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedZoneStacking(Task): + """"""Pick up blocks of different colors and stack them in zones to form a pyramid."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""stack the blocks in the zones to form a pyramid"" + self.task_completed_desc = ""done stacking blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for _ in range(3): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(9): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i//3]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(zone_poses[i//3], place_pos[i%3]), zone_poses[i//3][1]) for i in range(9)] + + # Goal: blocks are stacked in a pyramid in each zone. + for i in range(3): + self.add_goal(objs=blocks[i*3:(i+1)*3], matches=np.ones((3, 3)), targ_poses=targs[i*3:(i+1)*3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(blocks=""the red, blue and green blocks"", + row=""bottom""))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-cylinder-ball-match"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-cylinder-ball-match"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-cylinder-ball-match: {'task-name': 'color-coordinated-cylinder-ball-match', 'task-description': 'On the tabletop, there are four cylinders of different colors (red, blue, green, and yellow) and four balls of corresponding colors. The task is to pick up each ball and place it on top of the cylinder of the same color without the ball rolling off. However, there are small blocks scattered randomly on the table that the robot has to navigate around without knocking them over. The challenge lies in the precise placement of the balls on top of the cylinders, color matching, and navigation around the blocks.', 'assets-used': ['cylinder/cylinder-template.urdf', 'ball/ball-template.urdf', 'block/small.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedCylinderBallMatch(Task): + """"""Pick up each ball and place it on top of the cylinder of the same color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} ball on the {color} cylinder"" + self.task_completed_desc = ""done placing balls on cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.1) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinder_colors = ['red', 'blue', 'green', 'yellow'] + cylinder_poses = [] + cylinders = [] + for color in cylinder_colors: + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=color) + cylinder_poses.append(cylinder_pose) + cylinders.append(cylinder_id) + + # Add balls. + # x, y, z dimensions for the asset size + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for color in cylinder_colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add blocks as obstacles. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/small.urdf' + for _ in range(5): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose) + + # Goal: each ball is on top of the cylinder of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[cylinder_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=cylinder_colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""multi-level-insertion-and-zone-matching"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""multi-level-insertion-and-zone-matching"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```multi-level-insertion-and-zone-matching: {'task-name': 'multi-level-insertion-and-zone-matching', 'task-description': 'There are three levels of zones marked on the tabletop - the first level is red, second is blue, and third is green. On each level, there are large, medium, and small ell-shaped objects in corresponding colors. The task is to pick up each ell object from its current position and insert it into the corresponding colored zone on the same level, but in a specific order - large, medium, and small. The challenge lies in the precise control of insertion, color coordination, and the multi-level structure of the environment.', 'assets-used': ['zone/zone.urdf', 'insertion/ell.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class MultiLevelInsertionAndZoneMatching(Task): + """"""Pick up ell objects from their current position and insert them into the corresponding colored zone on the same level, in a specific order - large, medium, and small."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert the {size} {color} ell into the {color} zone on the same level"" + self.task_completed_desc = ""done inserting."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_sizes = [(0.12, 0.12, 0), (0.12, 0.12, 0.05), (0.12, 0.12, 0.1)] + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + zone_colors = ['red', 'blue', 'green'] + for i in range(3): + zone_pose = self.get_random_pose(env, zone_sizes[i]) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[zone_colors[i]]) + zone_poses.append(zone_pose) + + # Add ell objects. + ell_sizes = [(0.08, 0.08, 0.02), (0.06, 0.06, 0.015), (0.04, 0.04, 0.01)] + ell_urdf = 'insertion/ell.urdf' + ells = [] + for i in range(3): + for j in range(3): + ell_pose = self.get_random_pose(env, ell_sizes[j]) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[zone_colors[i]]) + ells.append(ell_id) + + # Goal: each ell object is in the corresponding colored zone on the same level. + for i in range(9): + self.add_goal(objs=[ells[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i//3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/9, + language_goal=self.lang_template.format(size=['large', 'medium', 'small'][i%3], color=zone_colors[i//3]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-cued-ball-corner-sorting"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-cued-ball-corner-sorting"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-cued-ball-corner-sorting: {'task-name': 'color-cued-ball-corner-sorting', 'task-description': 'On a tabletop, there are four different colored balls (red, blue, green, yellow) and four corners marked with corresponding colors using the corner template. The task involves picking up each ball and precisely placing it in the corner of the same color. However, there is a rectangular zone in the middle of the table marked by small blocks. The robot has to strategically navigate around this zone without touching the blocks while transporting the balls to the corresponding corners. The challenge lies in the precise navigation, placement of the balls, and color matching while avoiding the blocks.', 'assets-used': ['ball/ball-template.urdf', 'corner/corner-template.urdf', 'block/block_for_anchors.urdf', 'zone/zone.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCuedBallCornerSorting(Task): + """"""Pick up each colored ball and place it in the corner of the same color while avoiding a zone marked by small blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} ball in the {color} corner"" + self.task_completed_desc = ""done sorting balls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add corners. + corner_size = (0.05, 0.05, 0.05) + corner_urdf = 'corner/corner-template.urdf' + corner_colors = ['red', 'blue', 'green', 'yellow'] + corner_poses = [] + for color in corner_colors: + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, color=color, category='fixed') + corner_poses.append(corner_pose) + + # Add balls. + balls = [] + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + for color in corner_colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add zone. + zone_size = (0.2, 0.2, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + zone_urdf = 'zone/zone.urdf' + env.add_object(zone_urdf, zone_pose, 'fixed') + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block_for_anchors.urdf' + for _ in range(4): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose) + + # Goal: each ball is in the corner of the same color. + for i in range(4): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[corner_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=corner_colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""cylinder-ring-stack"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""cylinder-ring-stack"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```cylinder-ring-stack: {'task-name': 'cylinder-ring-stack', 'task-description': 'On the tabletop, there are four differently colored cylinders (red, blue, green, yellow) and four blocks of matching colors. The task involves picking up each block and stacking it on top of the corresponding colored cylinder. However, each cylinder and block pair should be stacked inside a differently colored container (color sequence: red cylinder and block in blue container, blue in green, green in yellow, and yellow in red). The task offers challenges in multi-object manipulation, color coordination, and precise stacking in a confined space.', 'assets-used': ['cylinder/cylinder-template.urdf', 'block/block.urdf', 'container/container-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CylinderRingStack(Task): + """"""Pick up each block and stack it on top of the corresponding colored cylinder. + Each cylinder and block pair should be stacked inside a differently colored container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""stack the {color} block on the {color} cylinder in the {container_color} container"" + self.task_completed_desc = ""done stacking."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for cylinders, blocks and containers + colors = ['red', 'blue', 'green', 'yellow'] + container_colors = ['blue', 'green', 'yellow', 'red'] + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=utils.COLORS[colors[i]]) + cylinders.append(cylinder_id) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[colors[i]]) + blocks.append(block_id) + + # Add containers. + container_size = (0.12, 0.12, 0.12) + container_urdf = 'container/container-template.urdf' + containers = [] + for i in range(4): + container_pose = self.get_random_pose(env, container_size) + container_id = env.add_object(container_urdf, container_pose, color=utils.COLORS[container_colors[i]]) + containers.append(container_id) + + # Goal: each block is stacked on the corresponding colored cylinder inside a differently colored container. + for i in range(4): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[cylinder_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i], container_color=container_colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-coordinated-sphere-on-pallet-pyramid"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-coordinated-sphere-on-pallet-pyramid"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-coordinated-sphere-on-pallet-pyramid: {'task-name': 'color-coordinated-sphere-on-pallet-pyramid', 'task-description': 'On the tabletop, there are three pallets and nine blocks of three different colors (red, blue, green). Each color has three blocks and the blocks are scattered randomly on the table. The task is to pick up the blocks and stack them on the pallets to form a pyramid shape. Each pyramid should contain blocks of the same color with two blocks on the base and one block on top. Additionally, there are three spheres of matching colors (red, blue, green). The task is to pick up each sphere and place it on top of the corresponding colored pyramid. The pallets with the pyramids and spheres should be arranged in a straight line in the following color order: red, blue, green from left to right. The challenge lies in the color coordination, precise stacking, and the arrangement of the pallets.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'sphere/sphere.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedSphereOnPalletPyramid(Task): + """"""Build a pyramid of colored blocks on pallets and place a matching colored sphere on top."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""build a pyramid of {color} blocks on the pallet and place the {color} sphere on top"" + self.task_completed_desc = ""done building color-coordinated pyramids."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Pallets and Blocks + pallet_size = (0.15, 0.15, 0.01) + block_size = (0.04, 0.04, 0.04) + pallet_urdf = 'pallet/pallet.urdf' + block_urdf = 'block/block.urdf' + + # Colors for blocks and spheres + colors = ['red', 'blue', 'green'] + color_objects = {} + + # Add pallets and blocks + for color in colors: + # Add pallet + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + + # Add blocks + block_ids = [] + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + block_ids.append(block_id) + + color_objects[color] = {'pallet': pallet_pose, 'blocks': block_ids} + + # Spheres + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + + # Add spheres + for color in colors: + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS[color]) + color_objects[color]['sphere'] = sphere_id + + # Goals + for color in colors: + # Goal: blocks are stacked in a pyramid on the pallet + block_poses = [(0, -0.02, 0.02), (0, 0.02, 0.02), (0, 0, 0.06)] + targs = [(utils.apply(color_objects[color]['pallet'], i), color_objects[color]['pallet'][1]) for i in block_poses] + + self.add_goal(objs=color_objects[color]['blocks'], matches=np.ones((3, 3)), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(color=color)) + + # Goal: sphere is placed on top of the pyramid + sphere_pose = (0, 0, 0.1) + targ = (utils.apply(color_objects[color]['pallet'], sphere_pose), color_objects[color]['pallet'][1]) + + self.add_goal(objs=[color_objects[color]['sphere']], matches=np.ones((1, 1)), targ_poses=[targ], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2], + language_goal=self.lang_template.format(color=color))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-sequenced-sphere-placement"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-sequenced-sphere-placement"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-sequenced-sphere-placement: {'task-name': 'color-sequenced-sphere-placement', 'task-description': 'On the tabletop, there are four spheres of different colors (red, blue, green, and yellow) and four colored squares of matching colors. The task involves picking up each sphere and precisely placing it in the center of the square of the same color. However, the spheres must be placed in a specific sequence - red first, then blue, then green, and finally yellow. The task is challenging due to the need for precise placement, color coordination, and sequence following.', 'assets-used': ['sphere/sphere.urdf', 'square/square-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorSequencedSpherePlacement(Task): + """"""Pick up spheres of different colors and place them in the center of the square of the same color in a specific sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} sphere in the {color} square"" + self.task_completed_desc = ""done placing spheres."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their sequence + colors = ['red', 'blue', 'green', 'yellow'] + + # Add squares of different colors + square_size = (0.1, 0.1, 0.005) + square_urdf = 'square/square-template.urdf' + square_poses = [] + for color in colors: + square_pose = self.get_random_pose(env, square_size) + env.add_object(square_urdf, square_pose, 'fixed', color=color) + square_poses.append(square_pose) + + # Add spheres of different colors + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + spheres = [] + for color in colors: + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=color) + spheres.append(sphere_id) + + # Goal: each sphere is in the square of the same color, in the correct sequence + for i in range(len(colors)): + self.add_goal(objs=[spheres[i]], matches=np.ones((1, 1)), targ_poses=[square_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(colors), + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""color-ordered-insertion-new"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""color-ordered-insertion-new"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```color-ordered-insertion-new: {'task-name': 'color-ordered-insertion-new', 'task-description': 'There are four differently-colored ell objects (red, blue, green, yellow) and a corresponding set of color-coded fixtures. The task involves picking up each ell object and inserting it into the matching color fixture in a specific order: from left to right, insert red, blue, green, and finally yellow. The challenge lies in the precise manipulation of the ell objects and the color-coordination required.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf']} + +### + +Implementation: + ```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorOrderedInsertionNew(Task): + """"""Insert differently-colored ell objects into the matching color fixture in a specific order."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""put the {color} L shape block in the L shape hole"" + self.task_completed_desc = ""done with insertion."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their order + colors = ['red', 'blue', 'green', 'yellow'] + color_order = {color: i for i, color in enumerate(colors)} + + # Add fixtures. + fixture_size = (0.12, 0.12, 0.02) + fixture_urdf = 'insertion/fixture.urdf' + fixtures = [] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color], category='fixed') + fixtures.append(fixture_id) + + # Add ell objects. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + ells = [] + for color in colors: + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[color]) + ells.append(ell_id) + + # Goal: each ell is inserted into the matching color fixture in the correct order. + for i, ell in enumerate(ells): + self.add_goal(objs=[ell], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(ells), + language_goal=self.lang_template.format(color=colors[i]))<|endoftext|>" +" + Instructions: ========= +Please describe the task ""put-blocks-between-zones"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""put-blocks-between-zones"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```put-blocks-between-zones: {'task-name': 'put-blocks-between-zones', 'task-description': 'Arrange four differently colored blocks (red, blue, green, and yellow) between two designated zones on the tabletop.', 'assets-used': ['zone/zone.urdf', 'block/block.urdf']} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import copy + +class PutBlocksBetweenZones(Task): + """"""Arrange four differently colored blocks (red, blue, green, and yellow) between two designated zones on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Arrange the blocks between the zones in the order: red, blue, green, yellow"" + self.task_completed_desc = ""done arranging blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone1_pose = self.get_random_pose(env, zone_size) + zone2_pose = copy.deepcopy(zone1_pose) + zone2_pose = (utils.apply(zone1_pose, (0,0.1,0)), zone2_pose[1]) + env.add_object(zone_urdf, zone1_pose, 'fixed') + env.add_object(zone_urdf, zone2_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Goal: blocks are arranged between the zones in the order: red, blue, green, yellow. + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, 0.1, 0.03)] + targs = [(utils.apply(zone1_pose, i), zone1_pose[1]) for i in place_pos] + + # Add goal + self.add_goal(objs=blocks, matches=np.ones((4, 4)), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, symmetries=[np.pi/2]*4, language_goal=self.lang_template) +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""put-blues-around-red"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""put-blues-around-red"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```put-blues-around-red: {'task-name': 'put-blues-around-red', 'task-description': 'place the blue blocks around the red block.', 'assets-used': ['block/block_for_anchors.urdf']} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class PlaceBluesAroundRed(Task): + """"""Pick up the blue blocks one by one and place them around the red block, forming a circle."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""place the blue blocks around the red block"" + self.task_completed_desc = ""done placing blue blocks around red block."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add red block. + red_block_size = (0.04, 0.04, 0.04) + red_block_urdf = 'block/block_for_anchors.urdf' + red_block_pose = self.get_random_pose(env, red_block_size) + red_block_id = env.add_object(red_block_urdf, red_block_pose, 'fixed') + + # Add blue blocks. + blue_blocks = [] + blue_block_size = (0.02, 0.02, 0.02) + blue_block_urdf = 'block/block_for_anchors.urdf' + N = 4 + + for _ in range(N): + blue_block_pose = self.get_random_pose(env, blue_block_size) + blue_block_id = env.add_object(blue_block_urdf, blue_block_pose, color=utils.COLORS['blue']) + blue_blocks.append(blue_block_id) + + # Calculate target poses for blue blocks to form a circle around the red block. + radius = 0.06 # radius of the circle + angles = np.linspace(0, 2*np.pi, N, endpoint=False) # angles for each blue block + targ_poses = [] + for angle in angles: + x = red_block_pose[0][0] + radius * np.cos(angle) + y = red_block_pose[0][1] + radius * np.sin(angle) + z = red_block_pose[0][2] + targ_poses.append(((x, y, z), red_block_pose[1])) + + # Add goal. + self.add_goal(objs=blue_blocks, matches=np.eye(N), targ_poses=targ_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1., language_goal=self.lang_template) +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""place-blue-on-line-ends"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""place-blue-on-line-ends"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```place-blue-on-line-ends: {'task-name': 'place-blue-on-line-ends', 'task-description': 'Pick up each blue box and accurately place it at the end of a green line.', 'assets-used': ['line/line-template.urdf', 'box/box-template.urdf']} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class PlaceBlueOnLineEnds(Task): + """"""Pick up each blue box and accurately place it at the end of a green line."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""place the blue box at the end of the green line"" + self.task_completed_desc = ""done placing blue boxes on line ends."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add lines. + line_size = (0.3, 0.01, 0.01) + line_template = 'line/line-template.urdf' + replace = {'DIM': line_size} + line_urdf = self.fill_template(line_template, replace) + + line_colors = ['green'] + line_poses = [] + + line_pose = self.get_random_pose(env, line_size) + color = utils.COLORS[line_colors[0]] + env.add_object(line_urdf, line_pose, 'fixed', color=color) + line_poses.append(utils.apply(line_pose, (-0.15,0,0))) + line_poses.append(utils.apply(line_pose, (0.15,0,0))) + + # Add blue boxes. + box_size = (0.04, 0.04, 0.04) + box_urdf = 'box/box-template.urdf' + box_color = utils.COLORS['blue'] + boxes = [] + for _ in range(2): + box_pose = self.get_random_pose(env, box_size) + box_id = env.add_object(box_urdf, box_pose, color=box_color) + boxes.append(box_id) + + # Goal: each blue box is at the end of a different colored line. + for i in range(2): + language_goal = self.lang_template.format(line_colors[0]) + self.add_goal(objs=[boxes[i]], matches=np.ones((1, 1)), targ_poses=[line_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, language_goal=language_goal) +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""stack-three-layer-red-wall"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""stack-three-layer-red-wall"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```stack-three-layer-red-wall: {'task-name': 'block-on-cylinder-on-pallet', 'task-description': 'On the tabletop, there are three cylinders of different colors (red, blue, and green) and three blocks of the same colors. The task involves picking up each block and placing it on the corresponding colored cylinder, which are located in specific positions on a pallet. Starting with the red block and cylinder, followed by blue and finally green. The challenge lies in the precise placement of the blocks on the cylinders, while maintaining color coordination.', 'assets-used': ['block/block.urdf', 'cylinder/cylinder-template.urdf', 'pallet/pallet.urdf']} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class StackThreeLayerRedWall(Task): + """"""Build a wall by stacking blocks. The wall should consist of three layers with each layer having three red blocks aligned in a straight line."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""stack the red blocks to form a three-layer wall"" + self.task_completed_desc = ""done stacking blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + block_size = (0.05, 0.05, 0.03) # x, y, z dimensions for the block size + block_urdf = 'block/block_for_anchors.urdf' # URDF for the block + block_color = utils.COLORS['red'] # Color for the block + + # We need 9 blocks for a three-layer wall with each layer having three blocks. + blocks = [] + for _ in range(9): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_color) + blocks.append(block_id) + + # Define target poses for the blocks to form a three-layer wall. + # The target poses are defined relative to a base pose. + base_pose = ((0.5, 0.0, 0.0), (0, 0, 0, 1)) + target_poses = [] + for i in range(3): # three layers + for j in range(3): # three blocks per layer + target_pos = (j * block_size[0], 0, i * block_size[2]) + target_pose = (utils.apply(base_pose, target_pos), (0, 0, 0, 1)) + target_poses.append(target_pose) + + # Goal: all blocks are stacked to form a three-layer wall. + self.add_goal(objs=blocks[3*i:3*(i+1)], matches=np.ones((3, 3)), targ_poses=target_poses[3*i:3*(i+1)], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3., language_goal=self.lang_template) +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""block-on-cylinder-on-pallet"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""block-on-cylinder-on-pallet"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```block-on-cylinder-on-pallet: {'task-name': 'block-on-cylinder-on-pallet', 'assets-used': ['pallet/pallet.urdf', 'cylinder/cylinder-template.urdf', 'block/block.urdf'], 'task-description': 'Pick up each block and place it on the corresponding colored cylinder, which are located in specific positions on a pallet.'} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BlockOnCylinderOnPallet(Task): + """"""Pick up each block and place it on the corresponding colored cylinder, which are located in specific positions on a pallet."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""place the {} cylinder on the pallet"" + self.lang_template_2 = ""place the {} block on the {} cylinder"" + + self.task_completed_desc = ""done placing blocks on cylinders and cylinder on pallet."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.35, 0.35, 0.01) + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_urdf = 'pallet/pallet.urdf' + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Define colors. + block_colors = ['red'] + cylinder_colors = ['blue'] + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.06) + cylinder_template = 'cylinder/cylinder-template.urdf' + cylinders = [] + + + replace = {'DIM': cylinder_size, 'HALF': (cylinder_size[0] / 2, cylinder_size[1] / 2, cylinder_size[2] / 2), 'COLOR': block_colors[0]} + cylinder_urdf = self.fill_template(cylinder_template, replace) + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose) + cylinders.append(cylinder_id) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=cylinder_colors[0]) + blocks.append(block_id) + + # Goal: place the cylinder on top of the pallet + self.add_goal(objs=[cylinders[0]], matches=np.ones((1, 1)), targ_poses=[pallet_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=self.lang_template.format(cylinder_colors[0])) + + + # Goal: place the block on top of the cylinder + language_goal = self.lang_template_2.format(block_colors[0], cylinder_colors[0]) + self.add_goal(objs=[blocks[0]], matches=np.ones((1, 1)), targ_poses=[pallet_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=language_goal) +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""ball-on-box-on-container"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""ball-on-box-on-container"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```ball-on-box-on-container: {'task-name': 'ball-on-box-on-container', 'assets-used': ['container/container-template.urdf', 'box/box-template.urdf', 'ball/ball.urdf'], 'task-description': 'Pick up each ball and place it on the corresponding colored box, which are located in specific positions on a container.'} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BallOnBoxOnContainer(Task): + """"""Pick up each ball and place it on the corresponding colored box, which are located in specific positions on a container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""place the {} box on the container"" + self.lang_template_2 = ""place the {} ball on the {} box"" + + self.task_completed_desc = ""done placing balls on boxs and box on container."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + container_size = (0.2, 0.2, 0.06) + container_pose = self.get_random_pose(env, container_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_template, replace) + + env.add_object(container_urdf, container_pose, 'fixed') + + # Define colors. + ball_colors = ['red'] + box_colors = ['blue'] + + # Add boxs. + box_size = (0.04, 0.04, 0.06) + box_template = 'box/box-template.urdf' + boxs = [] + + + replace = {'DIM': box_size, 'HALF': (box_size[0] / 2, box_size[1] / 2, box_size[2] / 2), 'COLOR': ball_colors[0]} + box_urdf = self.fill_template(box_template, replace) + box_pose = self.get_random_pose(env, box_size) + box_id = env.add_object(box_urdf, box_pose) + boxs.append(box_id) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball.urdf' + balls = [] + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=box_colors[0]) + balls.append(ball_id) + + # Goal: place the box on top of the container + self.add_goal(objs=[boxs[0]], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=self.lang_template.format(box_colors[0])) + + + # Goal: place the ball on top of the box + language_goal = self.lang_template_2.format(ball_colors[0], box_colors[0]) + self.add_goal(objs=[balls[0]], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=language_goal) +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""align-rope-along-line"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""align-rope-along-line"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```align-rope-along-line: {'task-name': 'align-rope-along-line', 'assets-used': ['line/line-template.urdf'], 'task-description': 'Align a deformable rope along a straight line marked on the tabletop.'} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula + +class AlignRopeAlongLine(Task): + """"""Align a deformable rope along a straight line marked on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""align the rope along the line"" + self.task_completed_desc = ""done aligning."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add line. + length = np.random.uniform(0.18, 0.25) + line_size = (length, 0.01, 0.01) + line_pose = self.get_random_pose(env, line_size) + line_template = 'line/line-template.urdf' + replace = {'DIM': line_size, 'HALF': (line_size[0] / 2, line_size[1] / 2, line_size[2] / 2)} + line_urdf = self.fill_template(line_template, replace) + env.add_object(line_urdf, line_pose, 'fixed') + + # Add rope. + rope_size = (length, 0.01, 0.01) + rope_pose = self.get_random_pose(env, rope_size) + corner1_pose = utils.apply(line_pose, (length / 2, 0.01, 0.01)) + corner2_pose = utils.apply(line_pose, (-length / 2, 0.01, 0.01)) + rope_id, targets, matches = self.make_rope(env, (corner1_pose, corner2_pose), n_parts=15) + + # Goal: rope is aligned with the line. + self.add_goal(objs=rope_id, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""align-rope-cross-zone"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""align-rope-cross-zone"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```align-rope-cross-zone: {'task-name': 'align-rope-cross-zone', 'assets-used': ['zone/zone.urdf'], 'task-description': 'Align a deformable rope across the diagonal of a zone marked on the tabletop.'} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula + +class AlignRopeCrossZone(Task): + """"""Align a deformable rope across the diagonal of a zone marked on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""align the rope across the diagonal of a zone"" + self.task_completed_desc = ""done aligning."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zone. + length = 0.12 + zone_size = (length, length, 0.01) + zone_pose = self.get_random_pose(env, zone_size) + zone_urdf = 'zone/zone.urdf' + env.add_object(zone_urdf, zone_pose, 'fixed') + + # Add rope. + rope_size = (length, 0.01, 0.01) + rope_pose = self.get_random_pose(env, rope_size) + corner1_pose = utils.apply(zone_pose, (length / 2, length / 2, 0.01)) + corner2_pose = utils.apply(zone_pose, (-length / 2, -length / 2, 0.01)) + rope_id, targets, matches = self.make_rope(env, (corner1_pose, corner2_pose), n_parts=10) + + # Goal: rope is aligned with the diagonal of the zone. + self.add_goal(objs=rope_id, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""put-kit-in-bowl"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""put-kit-in-bowl"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```put-kit-in-bowl: {'task-name': 'put-kit-in-bowl', 'assets-used': ['stacking/block.urdf', 'bowl/bowl.urdf', 'kitting/object-template.urdf'], 'task-description': 'Place the specific kit in a bowl of specified color.'} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import random +import pybullet as p +import os + + +class PutKitInBowl(Task): + """"""Place the specific kit in a bowl of specified color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""put the {pick} in a {place} bowl"" + self.task_completed_desc = ""done placing kit in bowls."" + self.additional_reset() + + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_objects = np.random.randint(1, n_bowls + 1) + colors, selected_color_names = utils.get_colors(mode=self.mode, n_colors=2) + block_urdf = 'stacking/block.urdf' + block_size = (0.04, 0.04, 0.04) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_id = env.add_object(bowl_urdf, bowl_pose, category='fixed', color=colors[1]) + bowl_poses.append(bowl_pose) + + # Add kits. + objects_ids = [] + obj_shapes = self.get_kitting_shapes(n_objects) + + for i in range(n_objects): + scale = utils.map_kit_scale((0.03, 0.03, 0.02)) + shape = os.path.join(self.assets_root, 'kitting', + f'{obj_shapes[i]:02d}.obj') + template = 'kitting/object-template.urdf' + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': colors[0]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + obj_pose = self.get_random_pose(env, block_size) + obj_id = env.add_object(urdf, obj_pose) + objects_ids.append(obj_id) + + # Goal: put each block in a different bowl. + pick_name = selected_color_names[0] + "" "" + utils.assembling_kit_shapes[obj_shapes[i]] + language_goal = (self.lang_template.format(pick=pick_name, place=selected_color_names[1])) + self.add_goal(objs=[obj_id], matches=np.ones((1, 1)), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / n_objects, language_goal=language_goal) + + # Only one mistake allowed. + self.max_steps = len(objects_ids) + 1 + + # Colors of distractor objects. + distractor_bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c not in selected_color_names] + distractor_block_colors = [utils.COLORS[c] for c in utils.COLORS if c not in selected_color_names] + + # Add distractors. + n_distractors = 0 + max_distractors = 6 + while n_distractors < max_distractors: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = distractor_block_colors if is_block else distractor_bowl_colors + pose = self.get_random_pose(env, size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1<|endoftext|>" +" + Instructions: ========= +Please describe the task ""kit-in-bowl-in-zone"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""kit-in-bowl-in-zone"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```kit-in-bowl-in-zone: {'task-name': 'kit-in-bowl-in-zone', 'assets-used': ['zone/zone.urdf', 'bowl/bowl.urdf', 'kitting/object-template.urdf'], 'task-description': 'Pick up each kit and place it on the corresponding colored bowl, which are located in specific positions on a zone.'} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import os + +class KitInBowlInZone(Task): + """"""Pick up each kit and place it on the corresponding colored bowl, which are located in specific positions on a zone."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""place the {} bowl on the zone"" + self.lang_template_2 = ""place the {} on the {} bowl"" + + self.task_completed_desc = ""done placing kits on bowls and bowl on zone."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zone. + zone_size = (0.2, 0.2, 0.01) + zone_pose = self.get_random_pose(env, zone_size) + zone_urdf = 'zone/zone.urdf' + env.add_object(zone_urdf, zone_pose, 'fixed') + + # Define colors. + kit_colors = ['red'] + bowl_colors = ['blue'] + + # Add bowls. + bowl_size = (0.04, 0.04, 0.06) + bowls = [] + bowl_urdf = 'bowl/bowl.urdf' + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_id = env.add_object(bowl_urdf, bowl_pose) + bowls.append(bowl_id) + + # Add kits. + kit_size = utils.map_kit_scale((0.03, 0.03, 0.02)) + obj_shapes = self.get_kitting_shapes(1) + shape = os.path.join(self.assets_root, 'kitting', + f'{obj_shapes[0]:02d}.obj') + template = 'kitting/object-template.urdf' + replace = {'FNAME': (shape,), 'SCALE': kit_size, 'COLOR': kit_colors[0]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + kit_urdf = self.fill_template(template, replace) + kits = [] + kit_pose = self.get_random_pose(env, kit_size) + kit_id = env.add_object(kit_urdf, kit_pose, color=bowl_colors[0]) + kits.append(kit_id) + + # Goal: place the bowl on top of the zone + self.add_goal(objs=[bowls[0]], matches=np.ones((1, 1)), targ_poses=[zone_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=self.lang_template.format(bowl_colors[0])) + + + # Goal: place the kit on top of the bowl + pick_name = kit_colors[0] + "" "" + utils.assembling_kit_shapes[obj_shapes[0]] + language_goal = self.lang_template_2.format(pick_name, bowl_colors[0]) + self.add_goal(objs=[kits[0]], matches=np.ones((1, 1)), targ_poses=[zone_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=language_goal) +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""move-kit-from-zone-to-cylinder"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""move-kit-from-zone-to-cylinder"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```move-kit-from-zone-to-cylinder: {'task-name': 'move-kit-from-zone-to-cylinder', 'assets-used': ['cylinder/cylinder-template.urdf', 'zone/zone.urdf', 'kitting/object-template.urdf', 'kitting/object-template.urdf'], 'task-description': 'Place the specific kit from a zone to a cylinder.'} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import random +import pybullet as p +import os +import copy + +class MoveKitFromZoneToCylinder(Task): + """"""Place the specific kit from a zone to a cylinder."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""put the {pick} from zone to {place} cylinder."" + self.task_completed_desc = ""done placing kit in zones."" + self.additional_reset() + + + def reset(self, env): + super().reset(env) + n_zones = 3 + n_objects = 3 + colors, color_names = utils.get_colors(mode=self.mode, n_colors=n_objects) + + # Add zones and objects + # x, y, z dimensions for the asset size + cylinder_size = (0.12, 0.12, 0) + cylinder_template = 'cylinder/cylinder-template.urdf' + cylinder_poses = [] + + zone_size = (0.06, 0.06, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + objects_ids = [] + obj_shapes = self.get_kitting_shapes(n_objects) + + for i in range(n_zones): + # add zone + zone_pose = self.get_random_pose(env, zone_size) + zone_id = env.add_object(zone_urdf, zone_pose, category='fixed', color=colors[i]) + zone_poses.append(zone_pose) + + # add kit + scale = utils.map_kit_scale((0.03, 0.03, 0.02)) + shape = os.path.join(self.assets_root, 'kitting', + f'{obj_shapes[i]:02d}.obj') + template = 'kitting/object-template.urdf' + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + obj_pose = zone_pose + obj_id = env.add_object(urdf, obj_pose) + objects_ids.append(obj_id) + + # add cylinder + cylinder_pose = self.get_random_pose(env, zone_size) + template = 'kitting/object-template.urdf' + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': colors[i]} + cylinder_urdf = self.fill_template(cylinder_template, replace) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, category='fixed', color=colors[i]) + cylinder_poses.append(cylinder_pose) + + # Goal: put a specific kit from a zone to the top of a cylinder + target_idx = np.random.randint(n_zones) + pick_name = color_names[target_idx] + "" "" + utils.assembling_kit_shapes[obj_shapes[target_idx]] + language_goal = (self.lang_template.format(pick=pick_name, place=color_names[target_idx])) + self.add_goal(objs=[objects_ids[target_idx]], matches=np.ones((1, 1)), targ_poses=[cylinder_poses[target_idx]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / n_objects, language_goal=language_goal) +<|endoftext|>" +" + Instructions: ========= +Please describe the task ""move-bowl-from-pallet-to-corner"" in natural languages and format the answer in a python dictionary with keys ""task-name"" and value type string, ""task-description"" (one specific sentence) and value type string, and ""assets-used"" and value type list of strings. + +========= +Now write the pybullet simulation code for the task ""move-bowl-from-pallet-to-corner"" in python code block starting with ```python. + + +### + +"," +Descriptions: + ```move-bowl-from-pallet-to-corner: {'task-name': 'move-bowl-from-pallet-to-corner', 'assets-used': ['corner/corner-template.urdf', 'pallet/pallet.urdf', 'bowl/bowl.urdf'], 'task-description': 'Place the specific bowl from a pallet to a corner.'} + +### + +Implementation: + ```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import random +import pybullet as p +import os +import copy + +class MoveBowlFromPalletToCorner(Task): + """"""Place the specific bowl from a pallet to a corner."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""put the {pick} from pallet to {place} corner."" + self.task_completed_desc = ""done placing bowl around corner."" + self.additional_reset() + + + def reset(self, env): + super().reset(env) + n_pallets = 3 + n_objects = 3 + colors, color_names = utils.get_colors(mode=self.mode, n_colors=n_objects) + # Add pallets and objects + # x, y, z dimensions for the asset size + corner_size = (0.12, 0.12, 0) + corner_urdf = 'corner/corner-template.urdf' + corner_poses = [] + + pallet_size = (0.06, 0.06, 0) + pallet_urdf = 'pallet/pallet.urdf' + pallet_poses = [] + objects_ids = [] + bowl_shapes = self.get_kitting_shapes(n_objects) + + for i in range(n_pallets): + # add pallet + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_id = env.add_object(pallet_urdf, pallet_pose, category='fixed', color=colors[i]) + pallet_poses.append(pallet_pose) + + # add kit + bowl_urdf = 'bowl/bowl.urdf' + bowl_pose = pallet_pose + bowl_id = env.add_object(bowl_urdf, bowl_pose, color=colors[i]) + objects_ids.append(bowl_id) + + # add corner + corner_pose = self.get_random_pose(env, pallet_size) + corner_id = env.add_object(corner_urdf, corner_pose, category='fixed', color=colors[i]) + corner_poses.append(corner_pose) + + # Goal: put a specific kit from a pallet to the top of a corner + target_idx = np.random.randint(n_pallets) + pick_name = color_names[target_idx] + "" "" + 'bowl' + language_goal = (self.lang_template.format(pick=pick_name, place=color_names[target_idx])) + self.add_goal(objs=[objects_ids[target_idx]], matches=np.ones((1, 1)), targ_poses=[corner_poses[target_idx]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / n_objects, language_goal=language_goal) +<|endoftext|>" diff --git a/misc/finetune_instructions_prompt.txt b/misc/finetune_instructions_prompt.txt new file mode 100644 index 0000000000000000000000000000000000000000..3f5d5ab615e8b06947a233ab6b799ee894984f10 --- /dev/null +++ b/misc/finetune_instructions_prompt.txt @@ -0,0 +1,5 @@ +========= +Please describe the task "TASK_NAME_TEMPLATE" in natural languages and format the answer in a python dictionary with keys "task-name" and value type string, "task-description" (one specific sentence) and value type string, and "assets-used" and value type list of strings. + +========= +Now write the pybullet simulation code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. diff --git a/misc/generate_all_demo.sh b/misc/generate_all_demo.sh new file mode 100644 index 0000000000000000000000000000000000000000..1bfa6254c9d2dee49fbbfec866ff21893f2684c2 --- /dev/null +++ b/misc/generate_all_demo.sh @@ -0,0 +1 @@ +python cliport/demos.py n=5 task=color-ordered-blocks-on-pallet mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=color-linked-ball-bowl-ordering mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=build-cylinder-structure mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=build-bridge mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=pyramid-blocks-assemble mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=sort-and-assemble-block-castle mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=stack-blocks-in-container mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=corner-sort-cylinders mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=align-pair-colored-blocks-along-line mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=color-specific-container-fill mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=colored-cylinder-in-square mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=construct-colorful-arch mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=color-coordinated-cylinders-in-boxes mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=insert-sphere-into-container mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=build-wheel mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=push-piles-into-letter mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=create-pyramid-with-color-coded-ells mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=color-coordinated-sphere-insertion mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=move-piles-along-line mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=multi-level-block-construction mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=build-car mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=color-coordinated-insertion mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=triangle-block-arrangement mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=colorful-block-tower-on-cylinder-base mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=manipulating-two-ropes mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=construct-corner-building mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=color-coordinated-container-sorting mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=construct-corner-blocks mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=sort-insert-color-coordinated-blocks mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=insert-blocks-into-fixture mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=symmetric-block-bridge-construction mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=connect-boxes-with-rope mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=vertical-insertion-blocks mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=cylinder-stand-alignment mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=insert-blocks-lineup mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=create-pyramid-blocks-and-container mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=mix-piles mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=rainbow-stack mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=align-cylinders-in-square mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=align-balls-in-colored-zones mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=multicolor-block-bridge mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=align-spheres-in-colored-zones mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=color-blocks-in-cylinder-maze mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=sort-and-stack-clr-blocks mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=corner-block-challenge mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=stack-color-coordinated-blocks mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=assemble-single-car mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=color-structured-block-tower mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=color-sorted-block-race mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=build-house mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=sphere-align-stand mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=color-coordinated-block-tower mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=color-sorted-container-stack mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=color-ordered-insertion mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=block-pyramid-with-limited-space mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=sorting-blocks-into-pallets mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=place-ball-in-elevated-bowl mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=Four-corner-pyramid-challenge mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=color-coordinated-cylinder-tower mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True;python cliport/demos.py n=5 task=build-two-circles mode=test disp=False record.save_video=True +record.blender_render=True +regenerate_data=True record.add_text=True; \ No newline at end of file diff --git a/misc/generate_all_gif.py b/misc/generate_all_gif.py new file mode 100644 index 0000000000000000000000000000000000000000..8a7ff002fd08801f8b8e44b199d8932ce4c42281 --- /dev/null +++ b/misc/generate_all_gif.py @@ -0,0 +1,37 @@ +import os +import json + +# generated_tasks = json.load(open('prompts/data/generated_tasks.json')) +generated_tasks = json.load(open('prompts/data/base_tasks.json')) + +generated_tasks = ([g['task-name'] for k, g in generated_tasks.items()]) + +# generated_tasks = [ 'build-house' ] +output_directory = 'output/output_gifs' +for data_folder in os.listdir('data'): + folder = os.path.join('data', data_folder, 'videos') + # print(data_folder) + if data_folder[:-5] in generated_tasks: + if os.path.exists(folder): + try: + input_file = os.path.join(folder, "input.txt") + with open(input_file, "w") as file: + video_files = [f for f in os.listdir(folder) if f.endswith(".mp4")] + for video_file in video_files: + file.write(f"file '{ video_file}'\n") + + # Concatenate the videos within the subfolder + concatenated_file = os.path.join(output_directory, f"{data_folder}.mp4") + ffmpeg_concat_command = f"ffmpeg -f concat -safe 0 -i {input_file} -c copy {concatenated_file}" + os.system(f"rm {concatenated_file}") + os.system(ffmpeg_concat_command) + + # Convert the concatenated video to a GIF + gif_file = os.path.join(output_directory, f"{data_folder}.gif") + os.system(f"rm {gif_file}") + ffmpeg_gif_command = f"ffmpeg -i {concatenated_file} -frames:v 800 {gif_file}" + os.system(ffmpeg_gif_command) + # os.system(f"rm {concatenated_file}") + + except Exception as e: + print(e) \ No newline at end of file diff --git a/misc/generate_primitive_mesh.py b/misc/generate_primitive_mesh.py new file mode 100644 index 0000000000000000000000000000000000000000..3032679b8ef6192ac25c24528bf4f98ea2c2a2bd --- /dev/null +++ b/misc/generate_primitive_mesh.py @@ -0,0 +1,15 @@ +import numpy as np +import trimesh + +# generate unit length mesh to replace primitives + +box = trimesh.creation.box(extents=[1, 1, 1]) +trimesh.exchange.export.export_mesh(box, "box.obj") + + +cylinder = trimesh.creation.cylinder(radius=1, height=1) +trimesh.exchange.export.export_mesh(cylinder, "cylinder.obj") + + +sphere = trimesh.creation.icosphere() +trimesh.exchange.export.export_mesh(sphere, "sphere.obj") diff --git a/misc/job_create.py b/misc/job_create.py new file mode 100644 index 0000000000000000000000000000000000000000..a82614e7167de227ace2519d0af25dde63a97704 --- /dev/null +++ b/misc/job_create.py @@ -0,0 +1,16 @@ +import os +import openai +import pandas as pd + +openai.api_key = os.getenv("OPENAI_API_KEY") + +file = 'prompts/finetune_data_new.jsonl' +file_id = (openai.File.create( + file=open(file, "rb"), + purpose='fine-tune' +))["id"] + +# subprocess.run('openai api files.create -f prompts/finetune_data_new.jsonl -p fine-tune') +# pd.read_json(path_or_buf=file, lines=True) +# print(openai.FineTuningJob.create(training_file=file_id, +# model="gpt-3.5-turbo", suffix='GenSimNew')) \ No newline at end of file diff --git a/misc/job_query.py b/misc/job_query.py new file mode 100644 index 0000000000000000000000000000000000000000..358eb02c24d033fd3bb97e16db2b6f4bb15cb7ee --- /dev/null +++ b/misc/job_query.py @@ -0,0 +1,11 @@ +import os +import openai +import pandas as pd + +openai.api_key = os.getenv("OPENAI_API_KEY") +print(openai.FineTuningJob.list(limit=10)) +print("==============================================") +latest_job = openai.FineTuningJob.list(limit=10)["data"][0]["id"] +print(openai.FineTuningJob.retrieve(latest_job)) +print("==============================================") +print(openai.FineTuningJob.list_events(id=latest_job, limit=1)) diff --git a/misc/list_remaining_tasks.py b/misc/list_remaining_tasks.py new file mode 100644 index 0000000000000000000000000000000000000000..294453e3d5566acaf0131f02004280cc21bc79af --- /dev/null +++ b/misc/list_remaining_tasks.py @@ -0,0 +1,40 @@ +import os + +def check_missing_task_data(target_task): + for existing_folder in os.listdir("data"): + if target_task + "-train" == existing_folder: + color_subdir = os.path.join('data', existing_folder, 'color') + if os.path.exists(color_subdir) and len(os.listdir(os.path.join('data', existing_folder, 'color'))) >= 40: + return False + return True + +total_tasks = os.listdir("cliport/tasks") + os.listdir("cliport/generated_tasks") + +total_tasks = [t.replace("_", "-")[:-3] for t in total_tasks if 'pycache' not in t and 'init' not in t \ + and 'README' not in t and 'extended' not in t and 'gripper' not in t and 'primitive' not in t\ + and 'generated' not in t and 'camera' not in t and t != 'task'] +print(total_tasks) +remaining_tasks = [t for t in total_tasks if (check_missing_task_data(t)) ] +# print(f"run sh scripts/generate_gpt_datasets.sh data {' '.join(remaining_tasks)}") + +# print(f"sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data \ +# '[{','.join(remaining_tasks)}]' gpt10_task_indomain" +# ) +for t in total_tasks: + print("sh scripts/train_test_single_task.sh data " + t) + +s = '' +for t in total_tasks: + s = s + f"python cliport/demos.py n=5 task={t} mode=test disp=False record.save_video=True +regenerate_data=True record.add_text=True +record.blender_render=True ;\n" +print(s) + +s = '' +for t in total_tasks: + s = s + f"cp -r data/{t}-test/videos output/code_video_website/{t}-videos\n" +print(s) + + +print("sh scripts/test_all_singletask.sh data \"" + ' '.join(total_tasks) +"\"") +# for t in ['color-specific-container-fill', 'build-two-circles', 'push-piles-into-letter', 'insert-blocks-lineup', 'align-pair-colored-blocks-along-line', 'color-blocks-in-cylinder-maze']: +# s = s + f"python cliport/demos.py n=5 task={t} mode=test disp=False record.save_video=True +regenerate_data=True record.add_text=True;" +# print(s) \ No newline at end of file diff --git a/misc/make_grid_video.py b/misc/make_grid_video.py new file mode 100644 index 0000000000000000000000000000000000000000..c99e90226e59059c8eff50df15cd0535283e8293 --- /dev/null +++ b/misc/make_grid_video.py @@ -0,0 +1,113 @@ +import cv2 +import numpy as np +import IPython +import os + +# Define the grid dimensions +num_rows = 6 + +output_folder = "output/output_gifs/" +total_tasks = os.listdir(output_folder) +# Load videos +videos = [cv2.VideoCapture(os.path.join(output_folder, s)) + for s in total_tasks if s.endswith("mp4") and not s.startswith("grid")] +num_cols = len(videos) // num_rows + 1 + +print(f"num_rows: {num_rows} num_cols: {num_cols}") + +# Get the dimensions of the videos +video_width = int(videos[0].get(cv2.CAP_PROP_FRAME_WIDTH)) +video_height = int(videos[0].get(cv2.CAP_PROP_FRAME_HEIGHT)) + +# Set up the output frame +output_width = video_width * num_cols +output_height = video_height * num_rows + +output_filename = output_folder + 'grid_video.mp4' +fourcc = cv2.VideoWriter_fourcc(*'mp4v') +output_video = cv2.VideoWriter(output_filename, fourcc, 30.0, (output_width, output_height)) + + +max_length = 200 + +# Read all frames +video_frames = [[] for _ in range(len(videos))] +for i, video in enumerate((videos)): + while True: + ret, frame = video.read() + if not ret: + break + video_frames[i].append(frame) + if len(video_frames) == 0 : + continue + # print(max_length, len(video_frames[i])) + repeat_ratio = max_length // len(video_frames[i]) + left_ratio = max_length % len(video_frames[i]) + + video_frames[i] = video_frames[i] * repeat_ratio + video_frames[i] += video_frames[i][:left_ratio] +# Pad with repeated video + +video_frames = [v for v in video_frames if len(v) == max_length] + +# Resize and arrange the frames +print(len(video_frames), len(video_frames[0])) + +for j, video_frame in enumerate(zip(*video_frames)): + output_frame = 255 * np.ones((output_height, output_width, 3), np.uint8) + for i, frame in enumerate(video_frame): + # Resize the frame to a smaller size for the zoom-out effect + + # Calculate the row and column indices for placing the frame in the output frame + row = i // num_cols + col = i % num_cols + + # Calculate the coordinates for placing the resized frame in the output frame + x = col * (video_width ) + y = row * (video_height ) + + # Place the resized frame in the output frame + output_frame[y:y+frame.shape[0], x:x+frame.shape[1]] = frame + output_video.write(output_frame) + +output_video.release() +zoomed_output_filename = output_folder + 'grid_video_zoomed.mp4' +output_video = cv2.VideoCapture(output_filename) +fourcc = cv2.VideoWriter_fourcc(*'mp4v') +grid_video = cv2.VideoWriter(zoomed_output_filename, fourcc, 30.0, (video_width, video_height)) + + +stop = 50 + +# Create the zoom-out effect +for idx in range(max_length): + if idx < stop: + ratio = 0.2 + else: + ratio = 0.2 + 0.8 * float(idx - stop) / (max_length - stop) + + ret, frame = output_video.read() + if not ret: + break + + # Apply the zoom-out effect by resizing the frame with the current ratio + center = frame.shape[0] // 2, frame.shape[1] // 2 + size = int(ratio * center[0]), int(ratio * center[1]) + zoomed_frame = frame[center[0]-size[0]:center[0]+size[0],center[1]-size[1]:center[1]+size[1]] + # cv2.resize(frame, None, fx=ratio, fy=ratio) + + # And then resize to video image size + resized_image = cv2.resize(zoomed_frame, (video_width, video_height)) + + # Display the zoomed frame + cv2.imshow('Zoom Out Grid', resized_image) + grid_video.write(resized_image) + + # Exit if 'q' is pressed + if cv2.waitKey(1) & 0xFF == ord('q'): + break + +# Release the grid video and close all windows +grid_video.release() +output_video.release() +cv2.destroyAllWindows() \ No newline at end of file diff --git a/misc/make_zoom_grid.py b/misc/make_zoom_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..2fc0858c69a96fa9ff6a4857ba5262adffef14b0 --- /dev/null +++ b/misc/make_zoom_grid.py @@ -0,0 +1,68 @@ +import cv2 +import numpy as np +import IPython +import os + +# Define the grid dimensions +num_rows = 6 + +output_folder = "output/output_gifs/" +total_tasks = os.listdir(output_folder) +# Load videos +videos = [cv2.VideoCapture(os.path.join(output_folder, s)) + for s in total_tasks if s.endswith("mp4") and not s.startswith("grid")] +num_cols = len(videos) // num_rows + 1 + +print(f"num_rows: {num_rows} num_cols: {num_cols}") + +# Get the dimensions of the videos +video_width = 640 # int(videos[0].get(cv2.CAP_PROP_FRAME_WIDTH)) +video_height = 480 # int(videos[0].get(cv2.CAP_PROP_FRAME_HEIGHT)) + +# Set up the output frame +output_width = video_width * num_cols +output_height = video_height * num_rows + +output_filename = output_folder + 'gslide_output2.mp4' +zoomed_output_filename = output_folder + 'zoom_gslide_output.mp4' +output_video = cv2.VideoCapture(output_filename) +fourcc = cv2.VideoWriter_fourcc(*'mp4v') +resolution_factor = 2 +grid_video = cv2.VideoWriter(zoomed_output_filename, fourcc, 30.0, (resolution_factor * video_width, resolution_factor * video_height)) + +print(video_width, video_height) +stop = 100 +max_length = 500 +# Create the zoom-out effect +for idx in range(max_length): + if idx < stop: + ratio = 0.23 + elif idx > max_length - stop: + ratio = 1 + else: + ratio = 0.23 + 0.8 * float(idx - stop) / (max_length - 2 * stop) + print(idx) + ret, frame = output_video.read() + if not ret: + break + + # Apply the zoom-out effect by resizing the frame with the current ratio + center = frame.shape[0] // 2, frame.shape[1] // 2 + size = int(ratio * center[0]), int(ratio * center[1]) + zoomed_frame = frame[center[0]-size[0]:center[0]+size[0],center[1]-size[1]:center[1]+size[1]] + + # And then resize to video image size + resized_image = cv2.resize(zoomed_frame, (resolution_factor * video_width, resolution_factor * video_height)) + + # Display the zoomed frame + cv2.imshow('Zoom Out Grid', resized_image) + grid_video.write(resized_image) + + # Exit if 'q' is pressed + if cv2.waitKey(1) & 0xFF == ord('q'): + break + +# Release the grid video and close all windows +grid_video.release() +output_video.release() +cv2.destroyAllWindows() \ No newline at end of file diff --git a/misc/manual_edit_note.txt b/misc/manual_edit_note.txt new file mode 100644 index 0000000000000000000000000000000000000000..1c0613632ec37050abf4e38db1a9f6d50e9dc245 --- /dev/null +++ b/misc/manual_edit_note.txt @@ -0,0 +1,32 @@ +Before writing the code for the task "TASK_NAME_TEMPLATE". Here are some APIs that are defined. Please confirm that you understand these APIs. + +""" +TASK_CLASS_IMPLEMENTATION + + + def add_object(self, urdf, pose, category='rigid'): + """List of (fixed, rigid, or deformable) objects in env.""" + fixed_base = 1 if category == 'fixed' else 0 + obj_id = pybullet_utils.load_urdf( + p, + os.path.join(self.assets_root, urdf), + pose[0], + pose[1], + useFixedBase=fixed_base) + self.obj_ids[category].append(obj_id) + return obj_id +""" + + +Note that the objects need to obey physics and not collide with each other, and the object goal poses need to be above the table with lower bound x=0.25, y=-0.5 and upper bound x=0.75, y=0.5. When there are multiple objects for a multi-step pick-and-place task, there are often multiple subgoals. Once the task and environment are generated, an agent with a pick and place primitive will follow the defined goal to accomplish the tasks. + +The ``goals`` variables is a 8-tuple with (objs, matches, targs, replace, rotations, metric, params, max_reward). +- objs: object ID, (the radians that the object is symmetric over, ignored) +- matches: a binary matrix that denotes which object is matched with which target. This matrix has dimension len(objs) x len(targs). +- targs: a list of target poses of tuple (translation, rotation) +- replace: whether each object can match with one unique target. +- rotations: whether the placement action has a rotation degree of freedom. +- metric: `pose` or `zone` that the object needs to be transported to +- params: has to be (obj_pts, zones) if the metric is `zone` +- max_reward: subgoal reward threshold + diff --git a/misc/prepare_finetune_gpt.py b/misc/prepare_finetune_gpt.py new file mode 100644 index 0000000000000000000000000000000000000000..784923bf4380983904d1db86d8a4c4ff363e86ad --- /dev/null +++ b/misc/prepare_finetune_gpt.py @@ -0,0 +1,115 @@ +import cv2 +import numpy as np +import IPython +import os + +import openai +import pandas as pd +import json +import subprocess + + +# create dataset by loading the python file +def format_prompt(task_name): + instruction_text = open('misc/finetune_instructions_prompt.txt').read() + instruction_text = instruction_text.replace("TASK_NAME_TEMPLATE", task_name) + prompt_text = "\n Instructions: " + instruction_text + "\n\n###\n\n" + return prompt_text + +def format_completion(task_name, descriptions, code): + completion_text = f" \nDescriptions: \n ```{task_name}: {descriptions} \n\n###\n\n" + completion_text += "Implementation: \n ```python\n" + code + "<|endoftext|>" + return completion_text + +# test if using the finetuned model can generate better task coed than the base model +# https://platform.openai.com/docs/guides/fine-tuning +data_path = 'prompts/data' +def load_offline_memory(): + """get the current task descriptions, assets, and code""" + base_task_path = os.path.join(data_path, "base_tasks.json") + base_asset_path = os.path.join(data_path, "base_assets.json") + base_task_code_path = os.path.join(data_path, "base_task_codes.json") + + base_tasks = json.load(open(base_task_path)) + base_assets = json.load(open(base_asset_path)) + base_task_codes = json.load(open(base_task_code_path)) + + generated_task_path = os.path.join(data_path, "generated_tasks.json") + generated_asset_path = os.path.join(data_path, "generated_assets.json") + generated_task_code_path = os.path.join(data_path, "generated_task_codes.json") + + # print("original base task num:", len(base_tasks)) + base_tasks.update(json.load(open(generated_task_path))) + # base_assets.update(json.load(open(generated_asset_path))) + + for task in json.load(open(generated_task_code_path)): + if task not in base_task_codes: + base_task_codes.append(task) + + # print("current base task num:", len(base_tasks)) + return base_tasks, base_assets, base_task_codes + + +code_buffer = {} +base_tasks, base_assets, base_task_codes = load_offline_memory() +TOTAL_DATASET_TOKENS = 0 + +added_tasks = [] +df = pd.DataFrame() +for task_file in base_task_codes: + ## TODO(lirui): consider adding more structure here. + task_name = task_file[:-3].replace("_", "-") + if task_name in added_tasks: + continue + + if task_name not in base_tasks: + print(f"{task_name} missing") + continue + + added_tasks.append(task_name) + task_description = base_tasks[task_name] + + if os.path.exists("cliport/tasks/" + task_file): + task_code = open("cliport/tasks/" + task_file).read() + + # the generated cliport task path + elif os.path.exists("cliport/generated_tasks/" + task_file): + task_code = open("cliport/generated_tasks/" + task_file).read() + + prompt = format_prompt(task_name) + completion = format_completion(task_name, task_description, task_code) + + # rough estimates + TOTAL_DATASET_TOKENS += len(prompt) / 4 + TOTAL_DATASET_TOKENS += len(completion) / 4 + new_row = { 'prompt': prompt, + 'completion': completion} + new_row = pd.DataFrame([new_row]) + df = pd.concat([df, new_row], axis=0, ignore_index=True) + +df.to_csv("misc/finetune_data.csv", index=False) +print("======================================") +print("estimate number of tokens:", TOTAL_DATASET_TOKENS) +print("estimate price for davinci:", TOTAL_DATASET_TOKENS / 1000 * 0.03) +print("total number of instructions:", len(df)) +print("======================================") +# actual finetuning + +## prepared_data.csv --> prepared_data_prepared.json +subprocess.run('openai tools fine_tunes.prepare_data --file misc/finetune_data.csv --quiet'.split()) + +print("now you can run \n openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix 'GenSim'") +# Model Training Usage +# Ada $0.0004 / 1K tokens $0.0016 / 1K tokens +# Curie $0.0030 / 1K tokens $0.0120 / 1K tokens +# Davinci $0.0300 / 1K tokens $0.1200 / 1K tokens + +# ## Start fine-tuning +# openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix "GenSim" +# subprocess.run('openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix "GenSim"'.split()) + + +# Tracking Finetune Status +# openai api fine_tunes.follow -i +# openai api fine_tunes.get -i +# openai wandb sync \ No newline at end of file diff --git a/misc/prepare_finetune_gpt_new.py b/misc/prepare_finetune_gpt_new.py new file mode 100644 index 0000000000000000000000000000000000000000..dffc9bbcfddd1489cf599914b9beddb22c124242 --- /dev/null +++ b/misc/prepare_finetune_gpt_new.py @@ -0,0 +1,117 @@ +import cv2 +import numpy as np +import IPython +import os + +import openai +import pandas as pd +import json +import subprocess + + +# create dataset by loading the python file +def format_prompt(task_name): + instruction_text = open('misc/finetune_instructions_prompt.txt').read() + instruction_text = instruction_text.replace("TASK_NAME_TEMPLATE", task_name) + prompt_text = "\n Instructions: " + instruction_text + "\n\n###\n\n" + return prompt_text + +def format_completion(task_name, descriptions, code): + completion_text = f" \nDescriptions: \n ```{task_name}: {descriptions} \n\n###\n\n" + completion_text += "Implementation: \n ```python\n" + code + "<|endoftext|>" + return completion_text + +# test if using the finetuned model can generate better task coed than the base model +# https://platform.openai.com/docs/guides/fine-tuning +data_path = 'prompts/data' +def load_offline_memory(): + """get the current task descriptions, assets, and code""" + base_task_path = os.path.join(data_path, "base_tasks.json") + base_asset_path = os.path.join(data_path, "base_assets.json") + base_task_code_path = os.path.join(data_path, "base_task_codes.json") + + base_tasks = json.load(open(base_task_path)) + base_assets = json.load(open(base_asset_path)) + base_task_codes = json.load(open(base_task_code_path)) + + generated_task_path = os.path.join(data_path, "generated_tasks.json") + generated_asset_path = os.path.join(data_path, "generated_assets.json") + generated_task_code_path = os.path.join(data_path, "generated_task_codes.json") + + # print("original base task num:", len(base_tasks)) + base_tasks.update(json.load(open(generated_task_path))) + # base_assets.update(json.load(open(generated_asset_path))) + + for task in json.load(open(generated_task_code_path)): + if task not in base_task_codes: + base_task_codes.append(task) + + # print("current base task num:", len(base_tasks)) + return base_tasks, base_assets, base_task_codes + + +code_buffer = {} +base_tasks, base_assets, base_task_codes = load_offline_memory() +TOTAL_DATASET_TOKENS = 0 + +added_tasks = [] +df = pd.DataFrame() +for task_file in base_task_codes: + ## TODO(lirui): consider adding more structure here. + task_name = task_file[:-3].replace("_", "-") + if task_name in added_tasks: + continue + + if task_name not in base_tasks: + print(f"{task_name} missing") + continue + + added_tasks.append(task_name) + task_description = base_tasks[task_name] + + if os.path.exists("cliport/tasks/" + task_file): + task_code = open("cliport/tasks/" + task_file).read() + + # the generated cliport task path + elif os.path.exists("cliport/generated_tasks/" + task_file): + task_code = open("cliport/generated_tasks/" + task_file).read() + + prompt = format_prompt(task_name) + completion = format_completion(task_name, task_description, task_code) + + # rough estimates + TOTAL_DATASET_TOKENS += len(prompt) / 4 + TOTAL_DATASET_TOKENS += len(completion) / 4 + + new_row = {"messages": [{"role": "system", "content": "You are an AI in robot simulation code and task design."}, + {"role": "user", "content": prompt}, { + "role": "assistant", "content": completion}]} + new_row = pd.DataFrame([new_row]) + df = pd.concat([df, new_row], axis=0, ignore_index=True) + +df.to_csv("misc/finetune_data.csv",index=False) +print("======================================") +print("estimate number of tokens:", TOTAL_DATASET_TOKENS) +print("estimate price for davinci:", TOTAL_DATASET_TOKENS / 1000 * 0.03) +print("total number of instructions:", len(df)) +print("======================================") +# actual finetuning + +## prepared_data.csv --> prepared_data_prepared.json +subprocess.run('openai tools fine_tunes.prepare_data --file misc/finetune_data.csv --quiet'.split()) + +print("now you can run \n openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix 'GenSim'") +# Model Training Usage +# Ada $0.0004 / 1K tokens $0.0016 / 1K tokens +# Curie $0.0030 / 1K tokens $0.0120 / 1K tokens +# Davinci $0.0300 / 1K tokens $0.1200 / 1K tokens + +# ## Start fine-tuning +# openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix "GenSim" +# subprocess.run('openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix "GenSim"'.split()) + + +# Tracking Finetune Status +# openai api fine_tunes.follow -i +# openai api fine_tunes.get -i +# openai wandb sync \ No newline at end of file diff --git a/misc/purge_task.py b/misc/purge_task.py new file mode 100644 index 0000000000000000000000000000000000000000..9a6adfc4475ce5ca273883ad04a0d1098467fa6b --- /dev/null +++ b/misc/purge_task.py @@ -0,0 +1,37 @@ +import os +import json +import argparse + +# remove some tasks from the list +parser = argparse.ArgumentParser() + +parser.add_argument( + "--files", "-f", type=str, default="exps" +) +args = parser.parse_args() + + + +data_path = "prompts/data" +generated_task_path = os.path.join(data_path, "generated_tasks.json") +generated_task_code_path = os.path.join(data_path, "generated_task_codes.json") + +generated_tasks = json.load(open(generated_task_path)) +generated_task_codes = json.load(open(generated_task_code_path)) + + +task_names = args.files.split(",") +print("Task names:", task_names) +for task_name in task_names: + task_name = task_name.replace("_", "-") + print("purge task:", task_name) + task_name_py = task_name.replace("-", "_") + ".py" + del generated_tasks[task_name] + generated_task_codes.remove(task_name_py) + os.system("rm cliport/generated_tasks/" + task_name_py) + +with open(generated_task_code_path, "w") as outfile: + json.dump(generated_task_codes, outfile, indent=4) + +with open(generated_task_path, "w") as outfile: + json.dump(generated_tasks, outfile, indent=4) \ No newline at end of file diff --git a/misc/pyBulletSimImporter.py b/misc/pyBulletSimImporter.py new file mode 100644 index 0000000000000000000000000000000000000000..572f779bbb3409161d436920521c27c380498750 --- /dev/null +++ b/misc/pyBulletSimImporter.py @@ -0,0 +1,201 @@ +from bpy.types import ( + Operator, + OperatorFileListElement, + Panel +) +from bpy.props import ( + StringProperty, + CollectionProperty +) +from bpy_extras.io_utils import ImportHelper +import bpy +import pickle +from os.path import splitext, join, basename + +bl_info = { + "name": "PyBulletSimImporter", + "author": "Huy Ha ", + "version": (0, 0, 1), + "blender": (2, 92, 0), + "location": "3D View > Toolbox > Animation tab > PyBullet Simulation Importer", + "description": "Imports PyBullet Simulation Results", + "warning": "", + "category": "Animation", +} + + +class ANIM_OT_import_pybullet_sim(Operator, ImportHelper): + bl_label = "Import simulation" + bl_idname = "pybulletsim.import" + bl_description = "Imports a PyBullet Simulation" + bl_options = {'REGISTER', 'UNDO'} + files: CollectionProperty( + name="Simulation files", + type=OperatorFileListElement, + ) + directory: StringProperty(subtype='DIR_PATH') + filename_ext = ".pkl" + filter_glob: StringProperty( + default='*.pkl', + options={'HIDDEN'}) + skip_frames: bpy.props.IntProperty( + name="Skip Frames", default=10, min=1, max=100) + max_frames: bpy.props.IntProperty( + name="Max Frames", default=-1, min=-1, max=100000) + + def execute(self, context): + for file in self.files: + filepath = join(self.directory, file.name) + print(f'Processing {filepath}') + with open(filepath, 'rb') as pickle_file: + data = pickle.load(pickle_file) + collection_name = splitext(basename(filepath))[0] + collection = bpy.data.collections.new(collection_name) + bpy.context.scene.collection.children.link(collection) + context.view_layer.active_layer_collection = \ + context.view_layer.layer_collection.children[-1] + + for obj_key in data: + pybullet_obj = data[obj_key] + # Load mesh of each link + # register material + + + if pybullet_obj['type'] == 'mesh': + extension = pybullet_obj['mesh_path'].split( + ".")[-1].lower() + # Handle different mesh formats + if 'obj' in extension: + bpy.ops.import_scene.obj( + filepath=pybullet_obj['mesh_path'], + axis_forward='Y', axis_up='Z') + elif 'dae' in extension: + bpy.ops.wm.collada_import( + filepath=pybullet_obj['mesh_path']) + elif 'stl' in extension: + bpy.ops.import_mesh.stl( + filepath=pybullet_obj['mesh_path']) + else: + print("Unsupported File Format:{}".format(extension)) + pass + elif pybullet_obj['type'] == 'cube': + bpy.ops.mesh.primitive_cube_add() + elif pybullet_obj['type'] == "sphere": + bpy.ops.mesh.primitive_uv_sphere_add() # radius=pybullet_obj['mesh_scale'][0] + elif pybullet_obj['type'] == "cylinder": + bpy.ops.mesh.primitive_cylinder_add() # radius=pybullet_obj['mesh_scale'][0], length=pybullet_obj['mesh_scale'][-1] + + # Delete lights and camera + parts = 0 + final_objs = [] + for import_obj in context.selected_objects: + bpy.ops.object.select_all(action='DESELECT') + import_obj.select_set(True) + if 'Camera' in import_obj.name \ + or 'Light' in import_obj.name\ + or 'Lamp' in import_obj.name: + bpy.ops.object.delete(use_global=True) + else: + scale = pybullet_obj['mesh_scale'] + if scale is not None: + # if type(scale) is list: + import_obj.scale.x = scale[0] + import_obj.scale.y = scale[1] + import_obj.scale.z = scale[2] + + final_objs.append(import_obj) + parts += 1 + + bpy.ops.object.select_all(action='DESELECT') + for obj in final_objs: + if obj.type == 'MESH': + obj.select_set(True) + if len(context.selected_objects): + context.view_layer.objects.active =\ + context.selected_objects[0] + # join them + bpy.ops.object.join() + blender_obj = context.view_layer.objects.active + blender_obj.name = obj_key + + if pybullet_obj['mesh_material_name'] is not None: + # register material + material_name = pybullet_obj['mesh_material_name'] + material_color = pybullet_obj['mesh_material_color'] + + print("registering material:", material_name) + mat = bpy.data.materials.new(name=material_name) + mat.use_nodes = True + nodes = mat.node_tree.nodes + links = mat.node_tree.links + for node in nodes: + nodes.remove(node) + mat_node = nodes.new(type='ShaderNodeBsdfPrincipled') + mat_node.inputs["Base Color"].default_value = material_color + mat_node_output = mat_node.outputs['BSDF'] + output_node = nodes.new(type='ShaderNodeOutputMaterial') + links.new(output_node.inputs['Surface'], mat_node_output) + + # attach material + # obj.data.materials[0] = bpy.data.materials[material_name] + if obj.data.materials: + obj.data.materials[0] = bpy.data.materials[material_name] + else: + obj.data.materials.append(bpy.data.materials[material_name]) + + # Keyframe motion of imported object + for frame_count, frame_data in enumerate( + pybullet_obj['frames']): + if frame_count % self.skip_frames != 0: + continue + if self.max_frames > 1 and frame_count > self.max_frames: + print('Exceed max frame count') + break + percentage_done = frame_count / \ + len(pybullet_obj['frames']) + print(f'\r[{percentage_done*100:.01f}% | {obj_key}]', + '#' * int(60*percentage_done), end='') + pos = frame_data['position'] + orn = frame_data['orientation'] + context.scene.frame_set( + frame_count // self.skip_frames) + # Apply position and rotation + blender_obj.location.x = pos[0] + blender_obj.location.y = pos[1] + blender_obj.location.z = pos[2] + blender_obj.rotation_mode = 'QUATERNION' + blender_obj.rotation_quaternion.x = orn[0] + blender_obj.rotation_quaternion.y = orn[1] + blender_obj.rotation_quaternion.z = orn[2] + blender_obj.rotation_quaternion.w = orn[3] + bpy.ops.anim.keyframe_insert_menu( + type='Rotation') + bpy.ops.anim.keyframe_insert_menu( + type='Location') + return {'FINISHED'} + + +class VIEW3D_PT_pybullet_recorder(Panel): + bl_space_type = 'VIEW_3D' + bl_region_type = 'UI' + bl_category = "Animation" + bl_label = 'PyBulletSimImporter' + + def draw(self, context): + layout = self.layout + row = layout.row() + row.operator("pybulletsim.import") + + +def register(): + bpy.utils.register_class(VIEW3D_PT_pybullet_recorder) + bpy.utils.register_class(ANIM_OT_import_pybullet_sim) + + +def unregister(): + bpy.utils.unregister_class(VIEW3D_PT_pybullet_recorder) + bpy.utils.unregister_class(ANIM_OT_import_pybullet_sim) + + +if __name__ == "__main__": + register() diff --git a/misc/pyBulletSimRecorder.py b/misc/pyBulletSimRecorder.py new file mode 100644 index 0000000000000000000000000000000000000000..9398bd6064ce53d51e7d886aef08486be84521f3 --- /dev/null +++ b/misc/pyBulletSimRecorder.py @@ -0,0 +1,245 @@ +import pybullet as p +import PySimpleGUI as sg +import pickle +from os import getcwd +from urdfpy import URDF +from os.path import abspath, dirname, basename, splitext +from transforms3d.affines import decompose +from transforms3d.quaternions import mat2quat +import numpy as np + + +class PyBulletRecorder: + class LinkTracker: + def __init__(self, + name, + body_id, + link_id, + link_origin, + mesh_path, + mesh_scale, + mesh_material=None): + self.body_id = body_id + self.link_id = link_id + self.mesh_path = mesh_path + self.mesh_scale = mesh_scale + self.mesh_material = mesh_material + decomposed_origin = decompose(link_origin) + orn = mat2quat(decomposed_origin[1]) + orn = [orn[1], orn[2], orn[3], orn[0]] + self.link_pose = [decomposed_origin[0], + orn] + self.name = name + + def transform(self, position, orientation): + return p.multiplyTransforms( + position, orientation, + self.link_pose[0], self.link_pose[1], + ) + + def get_keyframe(self): + if self.link_id == -1: + position, orientation = p.getBasePositionAndOrientation( + self.body_id) + position, orientation = self.transform( + position=position, orientation=orientation) + else: + link_state = p.getLinkState(self.body_id, + self.link_id, + computeForwardKinematics=True) + position, orientation = self.transform( + position=link_state[4], + orientation=link_state[5]) + return { + 'position': list(position), + 'orientation': list(orientation) + } + + def __init__(self): + self.states = [] + self.links = [] + + def register_object(self, body_id, urdf_path, global_scaling=1, color=None): + link_id_map = dict() + n = p.getNumJoints(body_id) + link_id_map[str(p.getBodyInfo(body_id)[0].decode('gb2312'))] = -1 + + for link_id in range(0, n): + link_id_map[str(p.getJointInfo(body_id, link_id)[ + 12].decode('gb2312'))] = link_id + + dir_path = dirname(abspath(urdf_path)) + file_name = splitext(basename(urdf_path))[0] + robot = URDF.load(urdf_path) + for link in robot.links: + # print("robot link:", body_id, link.name, link_id_map.keys()) + if link.name not in link_id_map: + print("skip links !! ", link.name, link_id_map, len(robot.links), p.getBodyInfo(body_id)[0].decode('gb2312')) + continue + + link_id = link_id_map[link.name] + + if len(link.visuals) > 0: + for i, link_visual in enumerate(link.visuals): + mesh_material = None + if link_visual.material is not None: + mesh_material = link_visual.material + if color is not None: + mesh_material.name = mesh_material.name + f"_{np.random.randint(100)}" # mark it + mesh_material.color = color + + if link_visual.geometry.mesh is not None: + print("use mesh", i, link_id_map.keys()) + + mesh_scale = [global_scaling, + global_scaling, global_scaling]\ + if link_visual.geometry.mesh.scale is None \ + else link_visual.geometry.mesh.scale * global_scaling + + self.links.append(('mesh', + PyBulletRecorder.LinkTracker( + name=file_name + f'_{body_id}_{link.name}_{i}', + body_id=body_id, + link_id=link_id, + link_origin= # If link_id == -1 then is base link, + # PyBullet will return + # inertial_origin @ visual_origin, + # so need to undo that transform + (np.linalg.inv(link.inertial.origin) + if link_id == -1 + else np.identity(4)) @ + link_visual.origin * global_scaling, + mesh_path=dir_path + '/' + + link_visual.geometry.mesh.filename, + mesh_scale=mesh_scale, + mesh_material=mesh_material))) + + if link_visual.geometry.box is not None: + print("use box", i, link_id_map.keys(), link_visual.geometry.box.__dict__) + # import IPython; IPython.embed() + mesh_scale = link_visual.geometry.box.size / 2 + self.links.append(('box', + PyBulletRecorder.LinkTracker( + name=file_name + f'_{body_id}_{link.name}_{i}', + body_id=body_id, + link_id=link_id, + link_origin= (np.linalg.inv(link.inertial.origin) + if link_id == -1 + else np.identity(4)) @ + link_visual.origin * global_scaling, + mesh_path='box', + mesh_scale=mesh_scale, + mesh_material=mesh_material))) + + + if link_visual.geometry.cylinder is not None: + print("use cylinder", i, link_id_map.keys(), link_visual.geometry.cylinder.__dict__) + mesh_scale = [link_visual.geometry.cylinder.radius, link_visual.geometry.cylinder.radius, link_visual.geometry.cylinder.length] + self.links.append(('cylinder', + PyBulletRecorder.LinkTracker( + name=file_name + f'_{body_id}_{link.name}_{i}', + body_id=body_id, + link_id=link_id, + link_origin= (np.linalg.inv(link.inertial.origin) + if link_id == -1 + else np.identity(4)) @ + link_visual.origin * global_scaling, + mesh_path='cylinder', + mesh_scale=mesh_scale, + mesh_material=mesh_material))) + + + if link_visual.geometry.sphere is not None: + print("use sphere", i, link_id_map.keys(), link_visual.geometry.sphere.__dict__) + mesh_scale = [link_visual.geometry.sphere.radius, link_visual.geometry.sphere.radius, link_visual.geometry.sphere.radius] + self.links.append(('sphere', + PyBulletRecorder.LinkTracker( + name=file_name + f'_{body_id}_{link.name}_{i}', + body_id=body_id, + link_id=link_id, + link_origin= (np.linalg.inv(link.inertial.origin) + if link_id == -1 + else np.identity(4)) @ + link_visual.origin * global_scaling, + mesh_path='sphere', + mesh_scale=mesh_scale, + mesh_material=mesh_material))) + + def add_keyframe(self): + # Ideally, call every p.stepSimulation() + current_state = {} + for name, link in self.links: + current_state[link.name] = link.get_keyframe() + self.states.append(current_state) + + def prompt_save(self): + layout = [[sg.Text('Do you want to save previous episode?')], + [sg.Button('Yes'), sg.Button('No')]] + window = sg.Window('PyBullet Recorder', layout) + save = False + while True: + event, values = window.read() + if event in (None, 'No'): + break + elif event == 'Yes': + save = True + break + window.close() + + if save: + layout = [[sg.Text('Where do you want to save it?')], + [sg.Text('Path'), sg.InputText(getcwd())], + [sg.Button('OK')]] + window = sg.Window('PyBullet Recorder', layout) + event, values = window.read() + window.close() + self.save(values[0]) + self.reset() + + def reset(self): + self.states = [] + + def get_formatted_output(self): + retval = {} + for geo_name, link in self.links: + if geo_name == 'mesh': + retval[link.name] = { + 'type': 'mesh', + 'mesh_path': link.mesh_path, + 'mesh_scale': link.mesh_scale, + 'frames': [state[link.name] for state in self.states] + } + if geo_name == 'box': + # print("retval: box!") + retval[link.name] = { + 'type': 'cube', + 'name': link.name, + 'mesh_scale': link.mesh_scale, + 'frames': [state[link.name] for state in self.states] + } + if geo_name == 'cylinder': + retval[link.name] = { + 'type': 'cylinder', + 'name': link.name, + 'mesh_scale': link.mesh_scale, + 'frames': [state[link.name] for state in self.states] + } + if geo_name == 'sphere': + retval[link.name] = { + 'type': 'sphere', + 'name': link.name, + 'mesh_scale': link.mesh_scale, + 'frames': [state[link.name] for state in self.states] + } + if link.mesh_material is not None: + retval[link.name]['mesh_material_name'] = link.mesh_material.name + retval[link.name] ['mesh_material_color'] = link.mesh_material.color + + return retval + + def save(self, path): + if path is None: + print("[Recorder] Path is None.. not saving") + else: + print("[Recorder] Saving state to {}".format(path)) + pickle.dump(self.get_formatted_output(), open(path, 'wb')) diff --git a/misc/run_figure2_blender.sh b/misc/run_figure2_blender.sh new file mode 100644 index 0000000000000000000000000000000000000000..d42e7d34947c37ea36b0f49869fda7645920be97 --- /dev/null +++ b/misc/run_figure2_blender.sh @@ -0,0 +1,10 @@ +python cliport/demos.py n=3 task=build-bridge mode=test disp=False record.save_video=True +regenerate_data=True record.add_text=True +record.blender_render=True ; +python cliport/demos.py n=3 task=block_on_cylinder_on_pallet mode=test disp=False record.save_video=True +regenerate_data=True record.add_text=True +record.blender_render=True ; +python cliport/demos.py n=3 task=build-two-circles mode=test disp=False record.save_video=True +regenerate_data=True record.add_text=True +record.blender_render=True ; +python cliport/demos.py n=3 task=Four_corner_pyramid_challenge mode=test disp=False record.save_video=True +regenerate_data=True record.add_text=True +record.blender_render=True ; +python cliport/demos.py n=3 task=align_cylinders_in_zones mode=test disp=False record.save_video=True +regenerate_data=True record.add_text=True +record.blender_render=True ; +python cliport/demos.py n=3 task=build_car mode=test disp=False record.save_video=True +regenerate_data=True record.add_text=True +record.blender_render=True ; +python cliport/demos.py n=3 task=construct_corner_blocks mode=test disp=False record.save_video=True +regenerate_data=True record.add_text=True +record.blender_render=True ; +python cliport/demos.py n=3 task=color_ordered_insertion mode=test disp=False record.save_video=True +regenerate_data=True record.add_text=True +record.blender_render=True ; +python cliport/demos.py n=3 task=align_pair_colored_blocks_along_line mode=test disp=False record.save_video=True +regenerate_data=True record.add_text=True +record.blender_render=True ; +python cliport/demos.py n=3 task=palletizing_boxes mode=test disp=False record.save_video=True +regenerate_data=True record.add_text=True +record.blender_render=True ; diff --git a/misc/snapshot_all_tasks.py b/misc/snapshot_all_tasks.py new file mode 100644 index 0000000000000000000000000000000000000000..8ef142a21a5c9f45e7ba6ca55249abc199ac4847 --- /dev/null +++ b/misc/snapshot_all_tasks.py @@ -0,0 +1,34 @@ +import cv2 +import numpy as np +import IPython +import os + +output_folder = "output/output_gifs/" +output_img_folder = "output/output_imgs/" + +total_tasks = [s for s in sorted(os.listdir(output_folder)) if s.endswith("mp4") and not s.startswith("grid")] +print(total_tasks) + +# Load videos +videos = [cv2.VideoCapture(os.path.join(output_folder, s)) + for s in total_tasks ] + +# Read all frames +video_frames = [[] for _ in range(len(videos))] +for i, video in enumerate(videos): + frame = None + + while True: + prev_frame = frame + ret, frame = video.read() + if not ret: + break + + # last frame + try: + frame = prev_frame[:550,:] + print(f"write {output_img_folder}/{total_tasks[i]}_img.png") + cv2.imwrite(f"{output_img_folder}/{total_tasks[i]}_img.png", frame) + except: + print("failed:", total_tasks[i]) + diff --git a/misc/tsne_visualize_chatgpt_embeddings_for_task.py b/misc/tsne_visualize_chatgpt_embeddings_for_task.py new file mode 100644 index 0000000000000000000000000000000000000000..fc619b92f37457e2070cd7ce78a9ca6c11707df0 --- /dev/null +++ b/misc/tsne_visualize_chatgpt_embeddings_for_task.py @@ -0,0 +1,197 @@ +import torch +import torch.nn +import torchvision.models as models +from copy import deepcopy +import cv2 + +import cv2 +import numpy as np +import sys +import itertools +import os +import IPython +import matplotlib +matplotlib.use("Agg") + +import matplotlib.pyplot as plt +import pandas as pd + +import openai +from sklearn.manifold import TSNE +from sklearn.decomposition import PCA, KernelPCA +import seaborn as sns + +import time +from matplotlib.offsetbox import OffsetImage, AnnotationBbox +import colorsys +from torchvision import datasets +import argparse +import matplotlib.patheffects as PathEffects +from sklearn.cluster import KMeans + + +sns.set_style("white") +sns.set_palette("muted") + +font = { + "size": 22, +} + +matplotlib.rc("font", **font) +sns.set_context("paper", font_scale=3.0) + + +plt_param = {'legend.fontsize': 60, + 'axes.labelsize': 80, + 'axes.titlesize':80, + 'font.size' : 80 , + 'xtick.labelsize':80, + 'ytick.labelsize':80, + 'lines.linewidth': 10, + 'lines.color': (0,0,0)} + +plt.rcParams.update(plt_param) + +openai.api_key ="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" +GPT_MODEL = "gpt4" +EMBEDDING_MODEL = "text-embedding-ada-002" + + +def normalize_numpy_array(arr): + return arr / (arr.max(axis=-1, keepdims=True) - arr.min(axis=-1, keepdims=True)) + +def fashion_scatter( + x, class_labels, fig_name, class_names, add_text=True +): + # choose a color palette with seaborn. + x = np.array(x) + class_labels = np.array(class_labels) + num_classes = np.max(class_labels) + 1 + + # create a scatter plot. + fig_size1, fig_size2 = 140 * 0.8, 80 * 0.6 + plt.clf() + plt.cla() + f = plt.figure(figsize=(fig_size1, fig_size2)) + ax = plt.subplot() + + # divide by a scale + # x = normalize_numpy_array(x) + for x_i in range(num_classes): + mask = class_labels == x_i + if mask.sum() > 0: + sc = ax.scatter( + x[mask, 0], + x[mask, 1], + lw=0, + s=1500, + label=class_names[x_i] + # c=rgb_color[mask], + ) # 40 + if add_text: + txts = [] + for i in range(len(class_names)): + xtext, ytext = x[i, :] # np.median(x[i, :], axis=0) + txt = ax.text(xtext, ytext, str(class_names[i]), fontsize=40) # 24 + txt.set_path_effects( + [PathEffects.Stroke(linewidth=5, foreground="w"), PathEffects.Normal()] + ) + txts.append(txt) + + # ax.legend(loc='upper left', bbox_to_anchor=(1, 1)) + ax.axis("on") + # ax.axis("tight") + plt.savefig(fig_name +".pdf") + plt.clf() + print("save figure to ", fig_name) + +def compute_embedding(response): + while True: + try: + print('ping openai api') + response_embedding = openai.Embedding.create( + model=EMBEDDING_MODEL, + input=response, + ) + + response_embedding = np.array(response_embedding["data"][0]['embedding']) + return response_embedding + except Exception as e: + print(e) + +def draw_latent_plot( + max_num=80, + method="pca+tsne", + fig_name="", +): + # query: (response, embeddings) + latents = [] + class_labels = [] + label_sets = [] + + # chatgpt embedding + total_tasks = [os.path.join("cliport/tasks", x) for x in os.listdir("cliport/tasks")] + [os.path.join("cliport/generated_tasks", x) for x in os.listdir("cliport/generated_tasks")] + total_tasks = [t for t in total_tasks if 'pycache' not in t and 'init' not in t \ + and 'README' not in t and 'extended' not in t and 'gripper' not in t and 'primitive' not in t\ + and 'task.py' not in t and 'camera' not in t and 'seq' not in t] + cache_embedding_path = "output/output_embedding/task_cache_embedding.npz" + cache_embedding = {} + + if os.path.exists(cache_embedding_path): + cache_embedding = dict(np.load(cache_embedding_path)) + + print(total_tasks) + + for idx, task_name in enumerate(total_tasks): + if task_name in cache_embedding: + code_embedding = cache_embedding[task_name] + else: + code = open(task_name).read() + code_embedding = compute_embedding(code) + + latents.append(code_embedding) + label_sets.append(task_name.split("/")[-1][:-3]) + cache_embedding[task_name] = code_embedding + class_labels.append(idx) + + latents = np.array(latents) + print("latents shape:", latents.shape) + np.savez(cache_embedding_path, **cache_embedding) + + n_clusters = 6 + kmeans = KMeans(n_clusters=n_clusters, init="k-means++", random_state=42) + kmeans.fit(latents) + cluster_labels = kmeans.labels_ + + if method == "pca+tsne": + # reduce dimension to the number of datapoints + pca = PCA(random_state=123, n_components=min(50, max_num)) # kernel PCA + + X_embedded = pca.fit_transform(latents) + print( + "Variance explained per principal component: {}".format( + pca.explained_variance_ratio_[:5] + ) + ) + print("PCA data shape:", X_embedded.shape) + X_embedded = TSNE(random_state=123, perplexity=20).fit_transform(X_embedded) + + if method == "pca": + pca = KernelPCA(random_state=123, n_components=2) # kernel PCA + X_embedded = pca.fit_transform(latents[:, :5]) + + if method == "tsne": + X_embedded = TSNE(random_state=123).fit_transform(latents) # perplexity + + fashion_scatter(X_embedded, class_labels, fig_name, label_sets) + fashion_scatter(X_embedded, cluster_labels, fig_name + "_cluster", label_sets) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Generate chat-gpt embeddings") + """ + load task descriptions from the tasks folder and embed + """ + parser.add_argument("--file", type=str, default="task_embedding") + args = parser.parse_args() + draw_latent_plot(fig_name=f'output/output_embedding/{args.file}') \ No newline at end of file diff --git a/notebooks/affordance.py b/notebooks/affordance.py new file mode 100644 index 0000000000000000000000000000000000000000..cfb3da7d1eab04bd940fc9331e8d9c78c4c8a3ed --- /dev/null +++ b/notebooks/affordance.py @@ -0,0 +1,246 @@ +import os +import sys +import json + +import numpy as np +from cliport import tasks +from cliport import agents +from cliport.utils import utils + +import torch +import cv2 +from cliport.dataset import RavensDataset +from cliport.environments.environment import Environment +from torch.utils.data import DataLoader +import IPython + +import matplotlib +import numpy as np +import matplotlib.pyplot as plt + +train_demos = 10 # number training demonstrations used to train agent +n_eval = 1 # number of evaluation instances +mode = 'test' # val or test + +agent_name = 'cliport' +model_task = 'place-red-in-green' # multi-task agent conditioned with language goals +task_type = 'gpt5_mixcliport2' # gpt5_mixcliport2 +model_folder = f'exps/exp-{task_type}_task_new_demo{train_demos}_2023-08-01_16-13-10-smaller' # path to pre-trained checkpoint +ckpt_name = 'last.ckpt' # name of checkpoint to load + +draw_grasp_lines = True +affordance_heatmap_scale = 30 + +### Uncomment the task you want to evaluate on ### +# eval_task = 'align-rope' +# eval_task = 'assembling-kits-seq-seen-colors' +# eval_task = 'assembling-kits-seq-unseen-colors' +# eval_task = 'packing-shapes' +# eval_task = 'packing-boxes-pairs-seen-colors' +# eval_task = 'packing-boxes-pairs-unseen-colors' +# eval_task = 'packing-seen-google-objects-seq' +# eval_task = 'packing-unseen-google-objects-seq' +# eval_task = 'packing-seen-google-objects-group' +# eval_task = 'packing-unseen-google-objects-group' +# eval_task = 'put-block-in-bowl-seen-colors' +# eval_task = 'put-block-in-bowl-unseen-colors' +eval_task = 'place-red-in-green' +# eval_task = 'stack-block-pyramid-seq-unseen-colors' +# eval_task = 'separating-piles-seen-colors' +# eval_task = 'separating-piles-unseen-colors' +# eval_task = 'towers-of-hanoi-seq-seen-colors' +# eval_task = 'towers-of-hanoi-seq-unseen-colors' + + +root_dir = os.environ['GENSIM_ROOT'] +assets_root = os.path.join(root_dir, 'cliport/environments/assets/') +config_file = 'eval.yaml' + +vcfg = utils.load_hydra_config(os.path.join(root_dir, f'cliport/cfg/{config_file}')) +vcfg['data_dir'] = os.path.join(root_dir, 'data') +vcfg['mode'] = mode + +vcfg['model_task'] = model_task +vcfg['eval_task'] = eval_task +vcfg['agent'] = agent_name + +# Model and training config paths +model_path = os.path.join(root_dir, model_folder) +if model_folder[-7:] == 'smaller': + vcfg['train_config'] = f"{model_path}/{model_folder[9:-8]}-{vcfg['agent']}-n{train_demos}-train/.hydra/config.yaml" + vcfg['model_path'] = f"{model_path}/{model_folder[9:-8]}-{vcfg['agent']}-n{train_demos}-train/checkpoints/" +else: + vcfg['train_config'] = f"{model_path}/{model_folder[9:]}-{vcfg['agent']}-n{train_demos}-train/.hydra/config.yaml" + vcfg['model_path'] = f"{model_path}/{model_folder[9:]}-{vcfg['agent']}-n{train_demos}-train/checkpoints/" +tcfg = utils.load_hydra_config(vcfg['train_config']) + +# Load dataset +ds = RavensDataset(os.path.join(vcfg['data_dir'], f'{vcfg["eval_task"]}-{vcfg["mode"]}'), + tcfg, + n_demos=n_eval, + augment=False) + +eval_run = 0 +name = '{}-{}-{}-{}'.format(vcfg['eval_task'], vcfg['agent'], n_eval, eval_run) +print(f'\nEval ID: {name}\n') + +# Initialize agent +utils.set_seed(eval_run, torch=True) +agent = agents.names[vcfg['agent']](name, tcfg, DataLoader(ds), DataLoader(ds)) + +# Load checkpoint +ckpt_path = os.path.join(vcfg['model_path'], ckpt_name) +print(f'\nLoading checkpoint: {ckpt_path}') +agent.load(ckpt_path) + + + +env = Environment( + assets_root, + disp=False, + shared_memory=False, + hz=480, + record_cfg=vcfg['record'] +) + + + + +episode = 0 +num_eval_instances = min(n_eval, ds.n_episodes) + +for i in range(num_eval_instances): + print(f'\nEvaluation Instance: {i + 1}/{num_eval_instances}') + + # Load episode + episode, seed = ds.load(i) + goal = episode[-1] + total_reward = 0 + np.random.seed(seed) + + # Set task + task_name = vcfg['eval_task'] + task = tasks.names[task_name]() + task.mode = mode + + # Set environment + env.seed(seed) + env.set_task(task) + obs = env.reset() + info = env.info + reward = 0 + + step = 0 + done = False + + # Rollout + while (step <= task.max_steps) and not done: + print(f"Step: {step} ({task.max_steps} max)") + + # Get batch + if step == task.max_steps-1: + batch = ds.process_goal((obs, None, reward, info), perturb_params=None) + else: + batch = ds.process_sample((obs, None, reward, info), augment=False) + + fig, axs = plt.subplots(2, 2, figsize=(13, 7)) + + # Get color and depth inputs + img = batch['img'] + img = torch.from_numpy(img) + color = np.uint8(img.detach().cpu().numpy())[:,:,:3] + color = color.transpose(1,0,2) + depth = np.array(img.detach().cpu().numpy())[:,:,3] + depth = depth.transpose(1,0) + + # Display input color + axs[0,0].imshow(color) + axs[0,0].axes.xaxis.set_visible(False) + axs[0,0].axes.yaxis.set_visible(False) + axs[0,0].set_title('Input RGB') + + # Display input depth + axs[0,1].imshow(depth) + axs[0,1].axes.xaxis.set_visible(False) + axs[0,1].axes.yaxis.set_visible(False) + axs[0,1].set_title('Input Depth') + + # Display predicted pick affordance + axs[1,0].imshow(color) + axs[1,0].axes.xaxis.set_visible(False) + axs[1,0].axes.yaxis.set_visible(False) + axs[1,0].set_title('Pick Affordance') + + # Display predicted place affordance + axs[1,1].imshow(color) + axs[1,1].axes.xaxis.set_visible(False) + axs[1,1].axes.yaxis.set_visible(False) + axs[1,1].set_title('Place Affordance') + + # Get action predictions + l = str(info['lang_goal']) + act = agent.act(obs, info, goal=None) + pick, place = act['pick'], act['place'] + + # Visualize pick affordance + pick_inp = {'inp_img': batch['img'], 'lang_goal': l} + pick_conf = agent.attn_forward(pick_inp)[0] + print("pick_conf:", pick_conf.shape, pick, place) + # IPython.embed() + logits = pick_conf.detach().cpu().numpy() + + pick_conf = pick_conf.detach().cpu().numpy() + argmax = np.argmax(pick_conf) + argmax = np.unravel_index(argmax, shape=pick_conf.shape) + p0 = argmax[:2] + + p0_theta = (argmax[2] * (2 * np.pi / pick_conf.shape[2])) * -1.0 + + line_len = 30 + pick0 = (pick[0] + line_len/2.0 * np.sin(p0_theta), pick[1] + line_len/2.0 * np.cos(p0_theta)) + pick1 = (pick[0] - line_len/2.0 * np.sin(p0_theta), pick[1] - line_len/2.0 * np.cos(p0_theta)) + + if draw_grasp_lines: + axs[1,0].plot((pick1[0], pick0[0]), (pick1[1], pick0[1]), color='r', linewidth=1) + + # Visualize place affordance + place_inp = {'inp_img': batch['img'], 'p0': pick, 'lang_goal': l} + place_conf = agent.trans_forward(place_inp)[0] + + place_conf = place_conf.permute(1, 2, 0) + place_conf = place_conf.detach().cpu().numpy() + argmax = np.argmax(place_conf) + argmax = np.unravel_index(argmax, shape=place_conf.shape) + p1_pix = argmax[:2] + p1_theta = (argmax[2] * (2 * np.pi / place_conf.shape[2]) + p0_theta) * -1.0 + + line_len = 30 + place0 = (place[0] + line_len/2.0 * np.sin(p1_theta), place[1] + line_len/2.0 * np.cos(p1_theta)) + place1 = (place[0] - line_len/2.0 * np.sin(p1_theta), place[1] - line_len/2.0 * np.cos(p1_theta)) + + if draw_grasp_lines: + axs[1,1].plot((place1[0], place0[0]), (place1[1], place0[1]), color='g', linewidth=1) + + # Overlay affordances on RGB input + pick_logits_disp = np.uint8(logits * 255 * affordance_heatmap_scale).transpose(2,1,0) + place_logits_disp = np.uint8(np.sum(place_conf, axis=2)[:,:,None] * 255 * affordance_heatmap_scale).transpose(1,0,2)# .transpose(1,2,0) + + pick_logits_disp_masked = np.ma.masked_where(pick_logits_disp < 0, pick_logits_disp) + place_logits_disp_masked = np.ma.masked_where(place_logits_disp < 0, place_logits_disp) + # IPython.embed() + + axs[1][0].imshow(pick_logits_disp_masked, alpha=0.75) + axs[1][1].imshow(place_logits_disp_masked, cmap='viridis', alpha=0.75) + + print(f"Lang Goal: {str(info['lang_goal'])}") + print(os.getcwd()) + plt.savefig(f'./test_{step}.png') + + # Act with the predicted actions + obs, reward, done, info = env.step(act) + step += 1 + + if done: + print("Done. Success.") + else: + print("Max steps reached. Task failed.") \ No newline at end of file diff --git a/notebooks/affordances.ipynb b/notebooks/affordances.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..736f921a521095cf0b8d7c3e6406bed2390d2e6a --- /dev/null +++ b/notebooks/affordances.ipynb @@ -0,0 +1,526 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "29bef37d", + "metadata": {}, + "source": [ + "# Affordance Heatmaps\n", + "\n", + "This notebook visualizes the pick and place affordance predictions of a pre-trained `multi-language-conditioned` agent from the quickstart guide.\n", + "\n", + "### Setup\n", + "\n", + "- Set the root folder environment variable with `export CLIPORT_ROOT=`\n", + "- Complete the [quickstart guide](https://github.com/cliport/cliport#quickstart) in README.md\n", + "- Generate `val` and `test` splits for the task you want to evaluate on by running `python cliport/demos.py n=10 mode=test task=stack-block-pyramid-seq-seen-colors`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ca5b47c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: CUDA_VISIBLE_DEVICES=0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pybullet build time: Aug 16 2021 17:58:31\n" + ] + } + ], + "source": [ + "# set GPU\n", + "%env CUDA_VISIBLE_DEVICES=0\n", + "\n", + "import os\n", + "import sys\n", + "import json\n", + "\n", + "import numpy as np\n", + "from cliport import tasks\n", + "from cliport import agents\n", + "from cliport.utils import utils\n", + "\n", + "import torch\n", + "import cv2\n", + "from cliport.dataset import RavensDataset\n", + "from cliport.environments.environment import Environment\n", + "\n", + "%matplotlib inline\n", + "import matplotlib\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "8bd6b5a6", + "metadata": {}, + "source": [ + "### Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4d00e1a8", + "metadata": {}, + "outputs": [], + "source": [ + "train_demos = 1000 # number training demonstrations used to train agent\n", + "n_eval = 1 # number of evaluation instances\n", + "mode = 'test' # val or test\n", + "\n", + "agent_name = 'cliport'\n", + "model_task = 'multi-language-conditioned' # multi-task agent conditioned with language goals\n", + "\n", + "model_folder = 'cliport_quickstart' # path to pre-trained checkpoint\n", + "ckpt_name = 'steps=400000-val_loss=0.00014655.ckpt' # name of checkpoint to load\n", + "\n", + "draw_grasp_lines = True\n", + "affordance_heatmap_scale = 30\n", + "\n", + "### Uncomment the task you want to evaluate on ###\n", + "# eval_task = 'align-rope'\n", + "# eval_task = 'assembling-kits-seq-seen-colors'\n", + "# eval_task = 'assembling-kits-seq-unseen-colors'\n", + "# eval_task = 'packing-shapes'\n", + "# eval_task = 'packing-boxes-pairs-seen-colors'\n", + "# eval_task = 'packing-boxes-pairs-unseen-colors'\n", + "# eval_task = 'packing-seen-google-objects-seq'\n", + "# eval_task = 'packing-unseen-google-objects-seq'\n", + "# eval_task = 'packing-seen-google-objects-group'\n", + "# eval_task = 'packing-unseen-google-objects-group'\n", + "# eval_task = 'put-block-in-bowl-seen-colors'\n", + "# eval_task = 'put-block-in-bowl-unseen-colors'\n", + "eval_task = 'stack-block-pyramid-seq-seen-colors'\n", + "# eval_task = 'stack-block-pyramid-seq-unseen-colors'\n", + "# eval_task = 'separating-piles-seen-colors'\n", + "# eval_task = 'separating-piles-unseen-colors'\n", + "# eval_task = 'towers-of-hanoi-seq-seen-colors'\n", + "# eval_task = 'towers-of-hanoi-seq-unseen-colors'" + ] + }, + { + "cell_type": "markdown", + "id": "c812da35", + "metadata": {}, + "source": [ + "### Load Configs and Agent" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0042f541", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Eval ID: stack-block-pyramid-seq-seen-colors-cliport-1-0\n", + "\n", + "Attn FCN - Stream One: plain_resnet_lat, Stream Two: clip_lingunet_lat, Stream Fusion: add\n", + "Transport FCN - Stream One: plain_resnet_lat, Stream Two: clip_lingunet_lat, Stream Fusion: conv\n", + "Agent: stack-block-pyramid-seq-seen-colors-cliport-1-0, Logging: False\n", + "\n", + "Loading checkpoint: /home/mshr/cliport/cliport_quickstart/multi-language-conditioned-cliport-n1000-train/checkpoints/steps=400000-val_loss=0.00014655.ckpt\n" + ] + } + ], + "source": [ + "root_dir = os.environ['CLIPORT_ROOT']\n", + "assets_root = os.path.join(root_dir, 'cliport/environments/assets/')\n", + "config_file = 'eval.yaml' \n", + "\n", + "vcfg = utils.load_hydra_config(os.path.join(root_dir, f'cliport/cfg/{config_file}'))\n", + "vcfg['data_dir'] = os.path.join(root_dir, 'data')\n", + "vcfg['mode'] = mode\n", + "\n", + "vcfg['model_task'] = model_task\n", + "vcfg['eval_task'] = eval_task\n", + "vcfg['agent'] = agent_name\n", + "\n", + "# Model and training config paths\n", + "model_path = os.path.join(root_dir, model_folder)\n", + "vcfg['train_config'] = f\"{model_path}/{vcfg['model_task']}-{vcfg['agent']}-n{train_demos}-train/.hydra/config.yaml\"\n", + "vcfg['model_path'] = f\"{model_path}/{vcfg['model_task']}-{vcfg['agent']}-n{train_demos}-train/checkpoints/\"\n", + "\n", + "tcfg = utils.load_hydra_config(vcfg['train_config'])\n", + "\n", + "# Load dataset\n", + "ds = RavensDataset(os.path.join(vcfg['data_dir'], f'{vcfg[\"eval_task\"]}-{vcfg[\"mode\"]}'), \n", + " tcfg, \n", + " n_demos=n_eval,\n", + " augment=False)\n", + "\n", + "eval_run = 0\n", + "name = '{}-{}-{}-{}'.format(vcfg['eval_task'], vcfg['agent'], n_eval, eval_run)\n", + "print(f'\\nEval ID: {name}\\n')\n", + "\n", + "# Initialize agent\n", + "utils.set_seed(eval_run, torch=True)\n", + "agent = agents.names[vcfg['agent']](name, tcfg, None, ds)\n", + "\n", + "# Load checkpoint\n", + "ckpt_path = os.path.join(vcfg['model_path'], ckpt_name)\n", + "print(f'\\nLoading checkpoint: {ckpt_path}')\n", + "agent.load(ckpt_path)" + ] + }, + { + "cell_type": "markdown", + "id": "2a6832c0", + "metadata": {}, + "source": [ + "### Spawn Environment" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4a0da7c9", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize environment and task.\n", + "env = Environment(\n", + " assets_root,\n", + " disp=False,\n", + " shared_memory=False,\n", + " hz=480,\n", + " record_cfg=False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0c138c4a", + "metadata": {}, + "source": [ + "### Evaluate Agent" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b86bb611", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Evaluation Instance: 1/1\n", + "text argument:/home/mshr/cliport/cliport/environments/assets/\n", + "int args: [Step: 0 (12 max)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mshr/cliport_env/lib/python3.8/site-packages/torch/nn/functional.py:3060: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n", + " warnings.warn(\"Default upsampling behavior when mode={} is changed \"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lang Goal: put the brown block on the lightest brown block\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step: 1 (12 max)\n", + "Lang Goal: put the green block on the middle brown block\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step: 2 (12 max)\n", + "Lang Goal: put the red block on the darkest brown block\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step: 3 (12 max)\n", + "Lang Goal: put the blue block on the brown and green blocks\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step: 4 (12 max)\n", + "Lang Goal: put the gray block on the green and red blocks\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step: 5 (12 max)\n", + "Lang Goal: put the yellow block on the blue and gray blocks\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done. Success.\n" + ] + } + ], + "source": [ + "episode = 0\n", + "num_eval_instances = min(n_eval, ds.n_episodes)\n", + "\n", + "for i in range(num_eval_instances):\n", + " print(f'\\nEvaluation Instance: {i + 1}/{num_eval_instances}')\n", + " \n", + " # Load episode\n", + " episode, seed = ds.load(i)\n", + " goal = episode[-1]\n", + " total_reward = 0\n", + " np.random.seed(seed)\n", + "\n", + " # Set task\n", + " task_name = vcfg['eval_task']\n", + " task = tasks.names[task_name]()\n", + " task.mode = mode\n", + " \n", + " # Set environment\n", + " env.seed(seed)\n", + " env.set_task(task)\n", + " obs = env.reset()\n", + " info = env.info\n", + " reward = 0\n", + " \n", + " step = 0\n", + " done = False\n", + " \n", + " # Rollout\n", + " while (step <= task.max_steps) and not done:\n", + " print(f\"Step: {step} ({task.max_steps} max)\")\n", + " \n", + " # Get batch\n", + " if step == task.max_steps-1:\n", + " batch = ds.process_goal((obs, None, reward, info), perturb_params=None)\n", + " else:\n", + " batch = ds.process_sample((obs, None, reward, info), augment=False)\n", + "\n", + " fig, axs = plt.subplots(2, 2, figsize=(13, 7))\n", + " \n", + " # Get color and depth inputs\n", + " img = batch['img']\n", + " img = torch.from_numpy(img)\n", + " color = np.uint8(img.detach().cpu().numpy())[:,:,:3]\n", + " color = color.transpose(1,0,2)\n", + " depth = np.array(img.detach().cpu().numpy())[:,:,3]\n", + " depth = depth.transpose(1,0)\n", + " \n", + " # Display input color\n", + " axs[0,0].imshow(color)\n", + " axs[0,0].axes.xaxis.set_visible(False)\n", + " axs[0,0].axes.yaxis.set_visible(False)\n", + " axs[0,0].set_title('Input RGB')\n", + " \n", + " # Display input depth\n", + " axs[0,1].imshow(depth)\n", + " axs[0,1].axes.xaxis.set_visible(False)\n", + " axs[0,1].axes.yaxis.set_visible(False) \n", + " axs[0,1].set_title('Input Depth')\n", + " \n", + " # Display predicted pick affordance\n", + " axs[1,0].imshow(color)\n", + " axs[1,0].axes.xaxis.set_visible(False)\n", + " axs[1,0].axes.yaxis.set_visible(False)\n", + " axs[1,0].set_title('Pick Affordance')\n", + " \n", + " # Display predicted place affordance\n", + " axs[1,1].imshow(color)\n", + " axs[1,1].axes.xaxis.set_visible(False)\n", + " axs[1,1].axes.yaxis.set_visible(False)\n", + " axs[1,1].set_title('Place Affordance')\n", + " \n", + " # Get action predictions\n", + " l = str(info['lang_goal'])\n", + " act = agent.act(obs, info, goal=None)\n", + " pick, place = act['pick'], act['place']\n", + " \n", + " # Visualize pick affordance\n", + " pick_inp = {'inp_img': batch['img'], 'lang_goal': l}\n", + " pick_conf = agent.attn_forward(pick_inp)\n", + " logits = pick_conf.detach().cpu().numpy()\n", + "\n", + " pick_conf = pick_conf.detach().cpu().numpy()\n", + " argmax = np.argmax(pick_conf)\n", + " argmax = np.unravel_index(argmax, shape=pick_conf.shape)\n", + " p0 = argmax[:2]\n", + " p0_theta = (argmax[2] * (2 * np.pi / pick_conf.shape[2])) * -1.0\n", + " \n", + " line_len = 30\n", + " pick0 = (pick[0] + line_len/2.0 * np.sin(p0_theta), pick[1] + line_len/2.0 * np.cos(p0_theta))\n", + " pick1 = (pick[0] - line_len/2.0 * np.sin(p0_theta), pick[1] - line_len/2.0 * np.cos(p0_theta))\n", + "\n", + " if draw_grasp_lines:\n", + " axs[1,0].plot((pick1[0], pick0[0]), (pick1[1], pick0[1]), color='r', linewidth=1)\n", + " \n", + " # Visualize place affordance\n", + " place_inp = {'inp_img': batch['img'], 'p0': pick, 'lang_goal': l}\n", + " place_conf = agent.trans_forward(place_inp)\n", + "\n", + " place_conf = place_conf.permute(1, 2, 0)\n", + " place_conf = place_conf.detach().cpu().numpy()\n", + " argmax = np.argmax(place_conf)\n", + " argmax = np.unravel_index(argmax, shape=place_conf.shape)\n", + " p1_pix = argmax[:2]\n", + " p1_theta = (argmax[2] * (2 * np.pi / place_conf.shape[2]) + p0_theta) * -1.0\n", + " \n", + " line_len = 30\n", + " place0 = (place[0] + line_len/2.0 * np.sin(p1_theta), place[1] + line_len/2.0 * np.cos(p1_theta))\n", + " place1 = (place[0] - line_len/2.0 * np.sin(p1_theta), place[1] - line_len/2.0 * np.cos(p1_theta))\n", + "\n", + " if draw_grasp_lines:\n", + " axs[1,1].plot((place1[0], place0[0]), (place1[1], place0[1]), color='g', linewidth=1)\n", + " \n", + " # Overlay affordances on RGB input\n", + " pick_logits_disp = np.uint8(logits * 255 * affordance_heatmap_scale).transpose(1,0,2)\n", + " place_logits_disp = np.uint8(np.sum(place_conf, axis=2)[:,:,None] * 255 * affordance_heatmap_scale).transpose(1,0,2) \n", + "\n", + " pick_logits_disp_masked = np.ma.masked_where(pick_logits_disp < 0, pick_logits_disp)\n", + " place_logits_disp_masked = np.ma.masked_where(place_logits_disp < 0, place_logits_disp)\n", + "\n", + " axs[1][0].imshow(pick_logits_disp_masked, alpha=0.75)\n", + " axs[1][1].imshow(place_logits_disp_masked, cmap='viridis', alpha=0.75)\n", + " \n", + " print(f\"Lang Goal: {str(info['lang_goal'])}\")\n", + " plt.show()\n", + " \n", + " # Act with the predicted actions\n", + " obs, reward, done, info = env.step(act)\n", + " step += 1\n", + " \n", + " if done:\n", + " print(\"Done. Success.\")\n", + " else:\n", + " print(\"Max steps reached. Task failed.\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/dataset.ipynb b/notebooks/dataset.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c19ad84df816b277de06872ebcb544a883a33919 --- /dev/null +++ b/notebooks/dataset.ipynb @@ -0,0 +1,484 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "61a62ae2", + "metadata": {}, + "source": [ + "# Dataset Visualizer\n", + "\n", + "This notebook visualizes a pre-generated dataset of expert demonstrations collected with demos.py\n", + "\n", + "### Setup\n", + "\n", + "- Set the root folder environment variable with `export CLIPORT_ROOT=`\n", + "- Run `python cliport/demos.py n=10 mode=train task=stack-block-pyramid-seq-seen-colors` to generate a dataset for `train`, `val`, or `test`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4c529fa2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pybullet build time: Aug 16 2021 17:58:31\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "import numpy as np\n", + "import hydra\n", + "\n", + "from cliport.dataset import RavensDataset\n", + "from cliport.utils import utils\n", + "from cliport import tasks\n", + "from cliport.environments.environment import Environment\n", + "\n", + "import torch\n", + "\n", + "%matplotlib inline\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "6a78065c", + "metadata": {}, + "source": [ + "### Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6aa08d59", + "metadata": {}, + "outputs": [], + "source": [ + "### task settings\n", + "mode = 'train'\n", + "augment = True\n", + "\n", + "### Uncomment the task you want to generate ###\n", + "# task = 'align-rope'\n", + "# task = 'assembling-kits-seq-seen-colors'\n", + "# task = 'assembling-kits-seq-unseen-colors'\n", + "# task = 'assembling-kits-seq-full'\n", + "# task = 'packing-shapes'\n", + "# task = 'packing-boxes-pairs-seen-colors'\n", + "# task = 'packing-boxes-pairs-unseen-colors'\n", + "# task = 'packing-boxes-pairs-full'\n", + "# task = 'packing-seen-google-objects-seq'\n", + "# task = 'packing-unseen-google-objects-seq'\n", + "# task = 'packing-seen-google-objects-group'\n", + "# task = 'packing-unseen-google-objects-group'\n", + "# task = 'put-block-in-bowl-seen-colors'\n", + "# task = 'put-block-in-bowl-unseen-colors'\n", + "# task = 'put-block-in-bowl-full'\n", + "task = 'stack-block-pyramid-seq-seen-colors'\n", + "# task = 'stack-block-pyramid-seq-unseen-colors'\n", + "# task = 'stack-block-pyramid-seq-full'\n", + "# task = 'separating-piles-seen-colors'\n", + "# task = 'separating-piles-unseen-colors'\n", + "# task = 'separating-piles-full'\n", + "# task = 'towers-of-hanoi-seq-seen-colors'\n", + "# task = 'towers-of-hanoi-seq-unseen-colors'\n", + "# task = 'towers-of-hanoi-seq-full'\n", + "\n", + "### visualization settings\n", + "max_episodes = 1\n", + "max_steps = 100" + ] + }, + { + "cell_type": "markdown", + "id": "62941225", + "metadata": {}, + "source": [ + "### Load configs" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "73676ada", + "metadata": {}, + "outputs": [], + "source": [ + "# Load configs\n", + "root_dir = os.environ['CLIPORT_ROOT']\n", + "config_file = 'train.yaml' \n", + "cfg = utils.load_hydra_config(os.path.join(root_dir, f'cliport/cfg/{config_file}'))\n", + "\n", + "# Override defaults\n", + "cfg['task'] = task\n", + "cfg['mode'] = mode\n", + "\n", + "data_dir = os.path.join(root_dir, 'data')" + ] + }, + { + "cell_type": "markdown", + "id": "a48693b6", + "metadata": {}, + "source": [ + "### Load dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "75fea08d", + "metadata": {}, + "outputs": [], + "source": [ + "task = tasks.names[cfg['task']]()\n", + "task.mode = mode\n", + "\n", + "ds = RavensDataset(os.path.join(data_dir, f'{cfg[\"task\"]}-{cfg[\"mode\"]}'), cfg, augment=augment)" + ] + }, + { + "cell_type": "markdown", + "id": "caec9950", + "metadata": {}, + "source": [ + "### Iterate through dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "11b25625", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Episode: 1/1000\n", + "\n", + "Step: 1/8\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Language Goal: put the brown block on the lightest brown block\n", + "Step Reward: 0\n", + "Total Reward: 0\n", + "\n", + "Step: 2/8\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Language Goal: put the green block on the middle brown block\n", + "Step Reward: 0.16666666666666666\n", + "Total Reward: 0.16666666666666666\n", + "\n", + "Step: 3/8\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Language Goal: put the red block on the darkest brown block\n", + "Step Reward: 0.16666666666666666\n", + "Total Reward: 0.3333333333333333\n", + "\n", + "Step: 4/8\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Language Goal: put the red block on the darkest brown block\n", + "Step Reward: 0.0\n", + "Total Reward: 0.3333333333333333\n", + "\n", + "Step: 5/8\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Language Goal: put the blue block on the brown and green blocks\n", + "Step Reward: 0.16666666666666669\n", + "Total Reward: 0.5\n", + "\n", + "Step: 6/8\n" + ] + }, + { + "data": { + "image/png": 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rP+XQ89HlJB5dP+omWatMyR0yFiNz2GrWf0D50OE/o80ZjrQQX926nSe6DiU2MBZBMis41bbmSKffMBqM16vqbSIyCzgFuAI4FnhndZNlTIVoiDZ1GMiYaCs3azq2M/+f+rj96Bex4mdDxB9+Fh0ZyRtO3OnsZPDo5Ww+Kc78B13E9Sw1Umg8iYx969JBy88I4tqYtprikb6ltG8ezh7HFbggd2RIaYwUtRElciq8bv4jXPeJJFw8HzZv8yz+LoWtbRFlSta1ZJLtbzqQY97zIG9tXw8Q+Q6W2fF+epfFaN3iLwiCU4k3ti02ZJ1EhhE1VLUHuElEtgL3isgXgWeATwNvAZLAjcA/q+qgiJwKfB/4OvAvQB9wmar+QEQuAt4GqIj8E3C7qr7eP9URIvIlYD/gl8AFqjpUocusW6Ih2hAGrZFfEZJOmlef8iAbX9LF0L/sizz9vOcuGSCC09nB459ZwkkHPkX/7oXow3ORcG930BbJM7G2OtDcnLL8jCBqlraaYsQNvZ5DwUg0EWP+sj11nZevX/QIX/h/f8PBnxxG93QDjA9KEmXC/WCTCbblS3nyfbP5u5N+x6z4QE11rPSuyrDgz+LVD6Mdd8LW41o569V319S1GEYjoap/EpGNwEnAhcAq4AggBfwQ+DfgI/7mi4B5wBLgOOBWEfmzql4pIi8lv3vkW4BXA0PAPcA7gG+U85oagWiINhUGM/ZyryRNsTSDMQdHBI15vfVBQ0K7OjhkxWYS4rJzRyeLc+ZjyzexdhD+P90sLO3qtvyMIBaIpLZ4aNsS5o+42dEj/cmLZzcP1n1e/uNLbuerH30FB39a0e6e8UFJaiGSZCF3yGCi7NcdzKy/e4GLF/wZgOEaE+JveOkD3Dz7RbiDCRbeEaNz/RC9+zZz0lv/wrxEX81dj2E0GJuBOcBFwGGquhtARD6DJ9w+Etr2X1V1GLhTRP4PT5T9+wTH/i9V3ewf72Y8QWjMkGiINky0VZqh4H77rkejON4EqcOZuJcne+OjoZwFJp1YO5MQ1ER4JFEVa0TVED3drSzIhAK8++VseE6SQzp3NkRe/v2xd/HNS07hoMuHYHh4zE0y8sg4wZZlXVu2D0+9dx5nnXIvc+L9pGpkMvRcFjf1cNHhdwPwjfTL6Hhe2LvcYWlyT81ek2E0EEvwdEAr8MDoO8pr7oUL8B5V7Q/93wDsM8mxt4Z+DxSxvVEE0RBtKgxlIpGUhmEkE4Sic/CCCZHVG5zKxBjKxHFSgop6E6hC4Ym1/XEMw3OElnjK8jOCKGINqRpCR2JAKiuUuiik2hzaYsMNk5dnHXs/vz7veJZ87ykY8UVs2NoWZXIFWyzG0EkHM/j+Pbx9ye9x0Lpxc03MHgInRus25ZmBBSxv2VXtJBmGUQAReQmeaPsZcAlwiKpuKrD5bBFpCwm3fYFH/d+10ItWN0SiZa1I9vgNo+wMZ+KMjjrLM2B+OBNjKJOgZcuYqFPxGo2BcBvbZ+xnqgNG3BhOoWkEjKph0SNri/ZnEuAMe39810AV6FnpkJBMw+Rl0klz0nkP8Hv3aJZ8/0kklc4OShJVwoLNnyj7uXfsy7Gv/SuLkz2k3PoS3e4LbagMM2vdMHfedwiLT/5DtZNkGEYOItIJnIwXPfL7qvqwiHwT+LKIXKyq20VkCXCoqv4qtOsnROSjeBEnXwd83F++DVhZwUtoaCKhlFQ9EWFUjlQm5ok2RzxNFhJZmc5moJ/hTBwniFEygVADrzGJwMgst+4aI/WC5x5p5axWkEzIFdkXACrC4IFDnpWtgfo3W2IjHHPew/w5dTiLf/hYTbhJjlrXEgmGjz+AzX83zKtX/YmEZOrOSuqq4CbHBHTXE8KWY2cxr6mviqkyDCPEzSKSBly8edq+xFhgkEvwAo/cKyLzgE3A/wCBaNsK7MEbAzcAvEdVn/TXfRv4iYh0A3eo6hvLfymNSyRacKpCKlNflVjUGcnEaB91ecwee9G/tIVkbC+9w0lig9kNo1FrW84yBNQR3NaM5WVEUai7xmI90/bKbTxzaBdLbonTunmIoQVJBufEOHT5cw2Zj23xYRafvZ6htWto/tMzkIluyP+gT8vpmsX6d67ikNc+xaHNe3FVvDks6wxXHZK7Y2SSXi+fk4I9w610xC3Ct2FUG1VdPsn6IeCj/qfQNp/GmxYgd/kz5AQZyT2fql5ebFqNiYlE7aFAyo1m5VuvpDOOL7Yke/JeEVAvP/qHmmgf8JYpGgpEkv+Y6kCsI2V5GVFcc0OuKY6ct4nuzt1scPYHR3jhDJeXH/w4yVi6YfNxeftuHv1gkqHPr6b5T89E19omgr5oDY//fTOvPPxhHHHr3sp9+GlP8sJxXQCsbO9hdtNA3V+zYRhGJYnEG1VVGElHIikNg4iy/ox2Vn23C3bt8RbG40hbK4NzHZx0nHQ6jwtWATdJFcg0CbGYa3kZUVTFXFdrjCd2LqSr1yWTjNHWNYgj2vB5eEDXdh7/kJC4fDmxx9dXOzl5Sc1tYfgze3l5x3Oe62ADWEbbYiMc2LV99H+6wZ9TY+qIyKvxxlrFgG+p6mernCTDiBSRaF0rQjpj1plK03roHp74l7nsf1Ur8Rd2sOGClfQfMEx7Vw/xjMPInmacdDi0fygQSYjA8pZuhqamtOVlRFFg2BpSNcXgw7OZ1zfoTcOxtpPhxZZ/AKtm7eT3fz+Hgz47D3qqnZrxNM8dYklbj1maDKNIRCQG/DdwGrARuF9EblLVx6ubssZGVe8AllY7HYZHJGoUVUibS11VaF/YR8/+nczd1k3/yhTtXYOAlx+SktAcbd5Etrlj2kZdJUUY6fLmaLO8jCaKWO93DfF872zmPqpZYe0t/8Y4/sBn+eNHVsE7q52S/KTV3oOGMQWOAdaq6joAEbkOOAMvaIZhGERFtAEZs85UheHBBIteGAZXcfpiZOaM5UOix8meo81n3Jg2EVTAjYO6flQSI3KoWkOylugeaKHLVXC8qJHtzwsDxzbRFEtXO2mR4SX7P8fz1U5EHqzzyjCmzBLghdD/jXjh5bMQkYuAiwBixI5upbMyqYsKUZ+b0hhjmmOue9mzU1Xn51sXCdGGiom2arEzSdO6jZBK0brFYWTJWD7E+8cGsIWtbVkELxCBkS7FsXyMLIowYlNr1Ayr5+5k67uGeXZnJ+Io+y705j21PIw+Cg0bLMYwyomqXglcCdApc/RYeUWVU1Q5JB7Pmp6pIQjPI+zq+HW5y6ZyzOnsO9Exw8dTF01Pr4P1Nr1+Q6F1kahVVMG1xn5VmPuwQDoNIsx/eIQNh8VwYt6DJ8G0O+HgI3l6eVS8j5tQsHyMLIqYpa3GmNfaz7x9+0f/W/7VBmZpM4wpswlYFvq/1F9mgNf2ajTBBtlCKFfATSa6Com6YFl4fa6QK7RvvuXBstA6zWTG71sCJhVtInIV3uzn21X1UH/ZHOBHwHJgPfAWVd0j3myiVwCvwZuA7x2q+pdiEqKF4sgbZWXnUS5uYgWShp79AcmgKrhpIdmtWYJtojna3LjgtmQQy8dpU+6yZmNHDaMydZoCGStrBXHzuNA7jTRbvJGP+4E1IrICT6ydA5xX3SRFB4nZeOa8Yinf8kLb5+4bXp+77VSOGSwLC7YyTUdTjKXtauBrwDWhZZcCv1XVz4rIpf7/S4DTgTX+51i8GdXH+SSPR3Az1tivCl0pdp8U+u8Kij82LSzWYOI52mJATC0fZ8bVlLmsZcxSE1ncSTo8nNweE2O6XE2Zy5lZtbOZ7NnOxZ71xkNV0yJyMfArvJD/V6nqY1VOVjRwYo1pZQvj5HmHFOPeWMi9slSukbnpymTALY+VDYoQbap6l4gsz1l8BnCq//u7wB14FdwZwDXqzXh6r4h0ichiVd0y8UlArbEfLYZjOLnuuBPM0ZZqF4ip5eMMKHdZ86JHNviLP2JMxcMgXA2INWqnTSXqNFVIZaxnfLqUr8ljRBlVvRW4tdrpiBQiZmWDwiIrbG3L57qY6/44EYUsd7nnCG+Ts2253CIDpjumbWGo0toKLPR/54v+swQYV8FlRQCa2wWuNfajhDPk4KTHXCInnKNNwI0BLhbZqPTMqKyFy1lyQae5bEWEqVoeJsKsEiWhpHVa04JOMuYqbhjGDDHBFiKf1awY61mwX66om8xFMt/yica8qVs2t8iAGQciUVWVaXT7hiMAJZcvVcxCEykkq1ufUdfIfHO0qUCqAxPeZWY6ZS1cztr2X6wpE211h4mD0lKKOq1t/8VqHSSGYcwIc4vMphhr21T2myjISbCskEjLc8zpRoucCtMVbdsCFxERWQxs95dPL/qPYg3+iJHodbJ7DELCLYtAuDlYHpaH0pU1teAIhlGAktdpVtZmhque+3DMMUuy0ZiYlS1ErqgKi6ipjG3L5y4ZRKKcSJgVstD5vzWdKv5aZsB0RdtNwAXAZ/3vn4eWX+zPZH8s0DPpeLYAd/JNjMohQYeBH49k1DUyZ0ybCqgjpNvU8rA8lKyseRHtTFgbRh5KWqdZWZsZGddheCSOm3GIxV3i8Qxxx7WxnEbDIPFIzMgVHYodq1astW2iKJBTSYurFXGLDCgm5P+1eAO054nIRuDjeBXbj0XkQmAD8BZ/81vxQiOvxQuP/M7ikiGIVXDlwwUnJTjDQrxfRudfyzRDplnJtLgQ7hRWcFKMF2kwbkwbeJEj1cHycIZUoqzZ1BqVIyr32hq62VSqnJmlbXqkMw4jw4nRqsdNxUilYiSTKeIx6xk0GoBGnZNtMvLMhzalKJCF5lgLH2eicW/h84f2KXfwkTDFRI88t8CqcdPQ+xG23jflVCjYmLbSIwrxfiG5W4gNgriKBL0BwTMpkGqPMbCPi+s/DfF+oXlXtmvkqLXNZ7Q9KpBpAo2r5eEMKXdZs4Zk5YiUTFLJ19fSsFSkTqO0wWYaiUzGQVWyvfMdJZNxcMxV0mgAJJ6odhKiST6hVqylbbKJtov5n+f85ZyTLR+Rsb+KdaCVFMlAcpdDco8ioQdsVHiFnrGmHgUc+pe4INC8U3BSOq7lmW88m4pnuXNGBLfJKtSoYw3J8hMVC1suZnGrLK55HkwLkeBZ9YSbyJiDh91To+5xbBxbXnKtbMH3VOdby2dJC8i1ok0W4ETdss7Jlo9oiDY10VZSFJp3OmPWMs1eFxBuwyX6lOadDig09WrWusnMBrERaOoWhudZozDqWKOncYmqmKxHVAXXrNrTwhNqIVcQUcTxGgh2T426xuZkm9wilvtd7KTb4f2KDTgSFm7hc1XBLTIgGqINLPJgCYkNQ3L3WGCQQgIsvNxJQcs2b4G4OeH9841ty4flYaSxhqRhVI4KesyUnGoLfHFC9ZcoIoxzmZz0GGZZNmqMhhdsUHwkyOmMact3jnwiLizOCgi6SrtFBkRDtGnOvGDGjHBSgmR8a5kWFm2jSJ7t/OWKeOPg8kSOHD2kQLJbybR449uM6GKWNsOoDDVd1iJilQ2El07jXkbkEgyjOGxOtuKZKHJkIZfGfPOwhZfnCsHw8nzHrrBbZEAkRJuQRzAY0yc0bi0QbgHjgonI+MotNy9UQsItZ3mwLD4IzrDgJiwjo8x0Gj+GYUwNVVCzalcXU21GDSH53PyMiZlK2P5CESEn2z6PVa4abpEBkRBtYGPaSsnonGqBdSxkJcuqx0KCLZ9o1kCTaUi4ja4bO5AouDFwmywfo4xq9d2e6oF675awJ6Q0WFkzDKMozMpWHLnh9/ORz90x3za5xw0vn+h/JlM1KxtERbQpNjFzKfGFWmBJGxVxuZtNsU0RFmqQfcxMEjQ0DsGIJtaQNCaj3kVppVB7FxqGMRkiZmUrlkIBQybabqrHDS8LR6j086iaVjaIimjDJmYuJaOBRAL3xxkf0P/WnP+hZelWMStbDWANScOoBBLtDpJGV+YRzhqjsZDYBFY2R5CmJnRkZHoBN+qVAiJXRNDAI2wm96uAa2S1go+EiYZos5D/JUUF1PEsbOqQP8gIZFXcWdEiYdSlclwUyTz7q+NZ2iwPo07EG5JRxOrJMezRKR7FoulGGSvXRhSYaE42R4jNmU1q5SLiTz6P29dvwi0gz32QpgRDJx1McscA8sRzKJmZ3a98AU2q6BYZEA3RhjX4S4qAxgR1dZyRDPKIsNB4t0LCLWu/HNyELxItD6ONNSSNmWDthSlhQX8MwyjIRG6RjuB0zWLnaSvYeaTS9eQBLLzhadye3sqmMarkuW960Aq2vDQOOovOQw+nuSdDx4NbyGzeNr1z5Ag+Taemd5wSExnRZmOhSkc4EEm+4WzjBFxIvI0TaHmCmOSKvnSLfyJr1EUea0gaRgWIyvswCmmoFezVaFSQrDnZ1B1zkXSE2Owu+l68H7teBG6Ly56DHZp3r2bWbU97FrdGJ0dQSVOCXYd24CYAUboPBIix66BlLLx/Acmdg8gLW3H39hV/jpClLQpukQGREG1SyH3PmB4ht0Vx/b+5VrTsTScWb3kscsE6FT9qpOVf9IlKQ9IwGgHrIKk8BYJuFb2vYVSCQm6RvoVtz6kr2HqCognPHU+TLltOFvauOIh5j6Ro/v3j6Eg0LD9VI2Rtk9ZWRjpCjVx/VWqWsullTeA2MfexTjpvfLB4l8mIuUUGREK0AfbCLBGi3kTlXuRI7yEe5yKZK8gCkZb9vGfvE14eEm8aAzeO5V+tYA1Jw6gM1XwnNsr7WD0vD8mI562jgibd0vUi2uvSKDUiY1a2cGQwfwzbztNWsOswRcNz3jrKu065i3d0/ZnfDSznh+e+Ch5f29hj3ELh+AeOWc7APvnvhcY840X784OT3688of41lS5RgktDZESbjYcqDRIEdZGxT65YG0Wzv4OJuDWs8kKdF+HjBMsySX+R5V9t0MDveMOoGDZ+tCJISpBUzn0eEbSpRCew96VRYrLcIsUZdY102tvYe9x+7DjWRWPZD97+q7bwj3MeZJbTzrkd2/jLt57i/s+9mM6bHkIzddz4mmy+NUeIzZ/HrkMSTFRYm3cL8Z19uGE31HzknqvKc7LlIxqizaJHlo6Q8BIBdfyeyDz+uK4jY+IutK+4Oib+8F15cwVc0CGRsFD/NYM1JA2jcliDv/zkGVohKcF1GNfwNaKDiKwHeoEMkFbVF4vIHOBHwHJgPfAWVd1TrTSWhXyTaIuDxByko529y2OoE7LsxJQzjnqQf5x3B7OcdgASEuMri//Ma967AH16JTy2toIXUGGKsCRqR6tnPJigadO22cVdt8HfYQLhlmNpq/acbPmIzhTsap9SfMQds7Spw5i1TST74wg43jau7+LoNkGmBdJtQir4tAqZZiGTFNyEHyky5n0ySe9/ta85sh/DMBqXar9/GuETNNRc8T7BcjcCaavHT2l5maoeoaov9v9fCvxWVdcAv/X/1xVZVrYQqor29LLP7/bQsjk+eq+X7buTTy28m1WJ9nH7/Hz/mznmuw+z59yjkVh0mvKVZmRRh2ekSJH3GY0NCV1/3Z29cKIJa/2JtKMUfCRMNCxtmKWt5Cj+uDa/XguNWcP/r2ErW2i/0Z/+eyATG9tJQi/vCeeAM6KJlTPDqACCmFW7InheJX4lFNRrYPe/9jgDONX//V3gDuCSaiWm1Eh8gua2q7gDAzjPbWLJ7Uk2vryV4ZXDfHz1TbQ7zXl3SUiMT8x/jNQ/x3jwkUPr1+KWz0UyWOYqifueZMW2pfQcOoe+fRyG5uuYwQJo3aywq3vi4+f+j6BbZEA0RJuaaCs1eYVUgTpMdEyrTUWAWZ7VGoLY5NqGUX7KY5kwcgmsbSGx5nVQSiR7yY1RFPi1eGr7f1X1SmChqm7x128FFubbUUQuAi4CaKa1EmmdOSITj6UCT7gNDhH/85Os2LqYDWcvZu4pA0Bywt3eM/cPvPnIE5i/rhl3eLj+gpPku57QMk2l0WfW0/nCFmYlmxg8egUjnTH27B8j3abMWp/C3RWytOXmQ/j4vhiMoltkQDREG1gFVw60wG8YHZ+m/u+82xj1ReA2ZEwN9XrtVQDHColRJGbpqQwy5hWSJd7s/keZE1V1k4gsAH4jIk+GV6qqiuTvQvYF3pUAnTKnJl7Ihdwi86EZFzZvY/n3U7y17Z/45nn/w8n5jW08MDzC+5/6W0a6BHfNMpxnXsAdHCpRqiNOjoVMR0bQkRGSd/yVZhG69llE38ELaH5gHW4+wRzePxBuEZuTLR+TijYRWQZcg9frocCVqnpFoUGjIiLAFcBrgAHgHar6lwnPgVltyk6e+iscJdJcHKtLJcoZYMK8SMQVJO27WAXvJsePlmqFpaapRFmLD0J8QMi02LNSEUL1mxXP6KOqm/zv7SJyI3AMsE1EFqvqFhFZDGyvaiJLRb7gI5OgqTTu7j2sujrBu9P/wJff/m1OaxkkFjpOnzvER9edxeb18+CANIMLO1l894G03fVkYwi3CSyKipLZtIWWTVtwQ+H7s+de0/HL1I2sW2RAMZa2NPBBVf2LiHQAD4jIb4B34A0a/ayIXIo3aPQS4HRgjf85Fvgf/7sw5h5ZFaxyixTlL2cQUur1RymfZ8mAkxtK3PWWj/OBr2Pq9HEpe1mLDbnsc1ea3QclGFykuIkIvmwjmKSapz7LS0kRkTbAUdVe//ffAJ8EbgIuAD7rf/+8eqksEeE52aaIjqRg8zZW/E8/l21+F/t89Msc1uSZ3J4YGeAjG87k6ecWjW6f6szgNgnS2oKk05GbX6zkTFQP++Pdxi0rdJwIR4vMZVLR5vsYb/F/94rIE8ASCg8aPQO4RlUVuFdEuoLek4lPNM0rMIw6oFLlzDpHpkiOi7GTEtymxnlZ1WPHTqXKWmIgzYIHMwwsaGLPgQ6pTit8dU8dlpcysBC40TNgEwd+qKq/FJH7gR+LyIXABuAtVUxjSZiuYAvQVBr6+hEXPrr+TD6z/EaWxVwue/4MHn5iv6xtkztjdDy5C0RwFswDx8HdsQsdGp5RGiJLMWP3cifLnugY6kbaLTJgSmPaRGQ5cCRwH4UHjS4BXgjtttFfllXBhQeTJtpnI/U2eNIwpkm5ylls9mxrVEyRsGhRGZu8vk4tUA1HucpaMjnLs8yitG4foWlvjN5lcXpXEE2rm2FUCFVdBxyeZ/ku4BWVT1GZmIZbZD40naZtW4bHH9uXN216L4mmNEPd2YPcYv0OK27ohu27GDxiOT0rmxjYR1j2q1k4f3my/oKThMlncQtcH4Pfk+0X8eAjYYoWbSLSDtwA/JOq7vV7SYCJB40WIjyYtHXBMq3HHl3DmCrlLGfJZXVUzipwHSpjgegAL8iBf+66uY/5aBBBWs6y1tmxJBTeDOJDGbqedWnZHad7VYzhOToWPCMCVOR5dsFJg8Yp7tqDyJB1gHXyNB4ztbKN4iptv3+K/UbWsOWEZobmZbJmWJaMsOiPioyk2XrW/nQf4qLxDAhsObGNZc+04/b0liYt1SJf2P8wE1nSgn1zRVw4AmXEg4+EKUq0iUgCr3L7gar+1F9caNDoJmBZaPel/rLC2Jg2wyh/OQOLqDYVcqPS+d913wKrjbprRlSkrKmCK4ijqHgPUFN3mvkPu/Qt9qxumYmjedcFkoHYsBAb9ur5TAJSHZotyPw2gGQCa7bgxhS3qWrJLil13cljjGPCOdmmgds/SPJ3j7Dy2SVsP2URe16kuM1eo9kZFOIDGTacOZ+BfbPHsQ0uctn9qjXMvumx2naTLEaw5bO4hcVevmNEfE62fBQTPVKAbwNPqOqXQqsKDRq9CbhYRK7DG6zdM+l4NmiIhoJhFKJS5cwaD1NEQq+mYNJeu4c1TcXqtDDquUqiAmmX9s0pmrtj7N03xuDCaFndSoUA8T4hNkLWVCOxYdCYkGnV0bLlpCA2kt3oclTQhFrTwKgtipmTbZq4Gzax4Ce76Hp2NZtObmZkjktsUHj+NQ4aGx94JNPm0r9PnDkL58GW7V5wk3qkkCukIxO7SdaQW2RAMd0BJwDnA38VkYf8ZR/Fq9jyDRq9FS808lq88MjvLCol9mY2GpvylzOldstZldKt+f7U6j0sRJ0bDvNQmTotH4GBSSE+kGH2My7Ne+L0LZXx1qdqUopnXD0xJnnaRPEBQAQ3rmh8bPswkgFJAyXyMosUUclno+SUzC2yAO7gEIl7H2fFpn0YWjGHwXlxtp5Y+N3RvyxD72EL6OwfxO3ZW59RJSeythXCEe9e1IhbZEAx0SPvpvArZtygUT/C1vumkohgcL9hNCoVKWfUuJXIJcuX3ygRtfxMTINKlLVJcRVxvDkAW3akSe4VepfEGVyouKX1rKoqGgPydO4LEBsCiQsaBzeueZ9DJy1k6nFC+zq8JIOSBR+ZDM24sGkrzXt6SC5ZQPf+sxlalF+MaZOy/agY7evmIIOD9SnaIP9k2eF1uetrzC0yIDLVQ003Jg2jVqjFcqZeAANJCzheA09rqfc9GK/jjk3W7SZCFgajMfBdJDUU8ARVBMEZUTo3pGnZ5dC7r8NIZ4SsbjPAjUNMxjplVfBcjkOuxpLygikE/7OGjLrWNjBqhBnMyTYdNJVGe/twtsaYtXYWQwvJ/85QyLQog4vbaH6ihifdLjYYST6Blme/WnOLDIhOs8FezIZRfmqsnImCMyJjLlYuxDKCxnzhE/GGrSjEhiRrTA9ATD0LQtTTb5QBX4+pq56iCZapkuhz6VqrDM51GFgko9MD1OSYN/9do7Gx36OCTbK3kUyedcFvl7oQsEZ9U0nBNoqraG8fsx/pZvehs0l1ZQsRyQjOgMPqH+5FnnjWe+fUKpOlPRB1+axsuceokTnZ8hEZ0WbukdVB1HNL0an4Ahs1S031Wrt+cILcd4Mfct9Boj/nlT+OcNx9z/gNVb9BOzr3Wy02zo1pIwqKesELfEuck4LWHRmSPQ6ZJsgkhEyzF7xDHbzfDmSaQOOVEf5TfW84Kb+jRX3B6YSsaKFP2LIWPP+aI9rElRp7cc0M68ipQSrkFpkPTaWRp9fT9vwcuru8ZZIWEj0OC+/P0P7UTtx1z9e2YCuGKYxr03TtuohGQ7T5vYxG5YkPKfEBF3UEN+E1CjQmZOok1LIRIqqBSAqkKXAlzG2vabiXvlY6e/KN10kJ6oIz7EfUi0OqvcyNcGsQRpIst0C/nMZGXCQjxIYV+gUVxU2AGxdUPAtWplkY6fCEXFQQ1wtAkvXMO17nNk7IaqiMs6qNs7QFNJCSaSB9WjdUxcoWQjMui2/bhsYWMtIFC/6SovW5PbB5G+7gUH1Prh2QK9bC1xxaV6tukQHREG0QzcZkneO5bimS8T6OP2BcHcGdHWukerJhqCmLtqg/jwpZ7lXh75po4OQpR6LgDHpj9cIuYgkVUu2U78Jq4X7VC5KT8ZO9T0PPuLieKBsdC+ao974eEcRVMgnvYPEBJTYEqTZhZBaRsdSqgJP7rvGtaK5/XaNTHobFWoGyYhhRpdRzsk0X97kX2Oe7O5DZs2B4BLd/oLbnZiuWYtwiwVuvbk0GHwkTjaeNGmtM1glej6g73pLhr7Ne+TqkxpT4aMARN/SOCDfyIn49gQUlN5Wx4fyh0GPDgEK6XaJ+aUYx5IgozRVyeRAdm4x7dG63YCgG6lnfVD2Lm18+En2KkxKG5xCJID3qMGFnizqMWsmz3CLD1jcTa0bUKeOcbNPBHRxCRkYgFqtpF8CC5AtGUsCilrXeX17rVjaIiGir+VDkNYqT9ixs44iPRfIy6otazFN1fH0WLAj1yEf+enIbrbnTm+RJf2wYzw0uWea0GRVhnFArRoxrzm9fzIjiN1IEJ62oeu6SOBAbUZr2eha3agp+8dOqzvgAI+HxaqNukrlpjXqZNgwfiSeqnYRxaMaFTHWsIM6cLkSEzK495TnBZG6e+daHBVsdDMOKhGiL7FibOie3wRv8V0csP+qRGi5nGnRo5o79qRHUT7totvUtn+jUoHFeQ9dn5CHcAZ8jTDzr0sTKKnCRhCBgydixRp8Pl9GAHwDxQSXTLGQqJfgneEZV/PgquYItvF+4M8Oe98kx63t0cCJg0o4IkogjSxez6bULcUZg8R27cNeur85Yujp1iwyIhmijSi/sOlDdM0LJHqDpFzBvLEWd3Zsi3JIaghrO1iw3w1q7jrCFIWjMBkIu7Pbpu4kF64zaRmXsoQ3/Hls/2QHIsioHgl5DbpPiBmY4b7tEr+86WQmvrYme0ZA1LSsiZLH7G+MJ3y+r0qpHhedkizKxRQvYeeoyBhYJw3MUcWHHMXOYt34jOpKqXEIKuUZSH26RAdERbfUmEiKOZLzoZOFGgwQPfT1GWG50ge5TD/lak9cQSrMXoTU0bjTXGuE3dGvyOo1Rxgm20eU5G8rE4xdFFWW84PPW4VvQFVxvA2cEYkPRiCiZ77rsuS4Rdh+rhgk2iM2bw8iqRWx/UQt9+0LwQKoDA4uF1DEHkPjj4567ZiXIF4jEEagTt8iAyIg2ewFVFnHVn7R0fGMiGARv1CGVyld7fiZEHS+KnhMar6QOo8LNSUFmOj3p1vseDfIJtrCLoJNHqBWInpi1SeAmGVjcgsGeIYubiC/aojgm0t4LRq1TxTnZooIk4ux6+XL2HCR5Ax+lOpRdBzez+IEmdHCo9AnIF5AkWA5Z0STrLSBLNESb+bNXHMkz/xWAm7AgJHVNGfPVnpniUQHinqv9qAtcyA1O/HrGneI4d8uC6JAVgCRHsI0tHy/eAgtdIOLHVjBe1IUClKj/X1wvKIm4FXKRNIwGwqxsIB3tDM530FjhGmdwoTJ4/P403/1E6d0kC3nm5SzXdAXdMytENEQbWGuj0gQuNbmLHRlzxzHqCilX54iV3WkRjF0bc3HLXu+kfVfKYhve9ejWXKME1rDR/zmCzXOHzRZ1033lBoJNYNRN0kl7UUij4CJpGPVCVOZkqzZudw8LHhxk46ktZJrzVzpuArYe28TyrfvC48+WPhGTWdvqzC0yICJPoCJ1eHOjjLg6fn42GYtYZvlRp1i2RobR0Oj5okVOpwFveRsdCljWxgJyhMUbofX5x64Fx8z6Ht2J0SkxxMXzuXXFiySZtM43wygJEZuTraq4Suzex1mcfBEbT03kH2/re3P1L++g9fHypGEcIffIego+EiYiog1rcFQQCSYqzp2UMOj5tbyoW8wSEyHCbpFBq5ux/2oBSWqWTFIYmRUn0e9m9/aGxRrkRBUNibsCbcPssPmK+AcIHqVR4YYSGxJiI1N3sTWMUiAiVwGvA7ar6qH+sjnAj4DlwHrgLaq6R0QEuAJ4DTAAvENV/1KNdBciinOyVRVXaV63i/gxi0m3ZVdSkoG5jyqzf78Bd0935ZqU4WiRdWp4iIRok3DYa6O8+APWnVTODQ8m2VaxvKhn6vM9VtME1haBLMEWuE5alhWmZXs07446sHd5jESfQ7JHaerNeIGfAnJEW1awkiI688eCkKi/m4wTbg5KbMiffNswKs/VwNeAa0LLLgV+q6qfFZFL/f+XAKcDa/zPscD/+N/RwOZky4tu3say2zp4/m/aybR476LkLmHRvQMknnieTM/eylong2iRdTInWz4iIdqAirRMzOUPcCE2nF+VScZ3U63z26QNPGebWW0iSGBpc3JC/werLc/y0rpVmXvr09VORkFUYKRDSLULsSGH5j0uTb3u6PQ2uYFKIEewhQVdbkASchfpOOGGQnwQ0i1TGBdpGCVCVe8SkeU5i88ATvV/fxe4A0+0nQFco6oK3CsiXSKyWFW3VCi5hbE52QqiqTTOY88xd8nB7DokxuynXGbftwl363bcjFt5d9I6dosMiIxoK7ugsoYPEIxlKzBw1GmMUP+i2pih0fMEuzCiwejYJiF7nJPlV17iAzDvzo1kunuqnZSChMV2phn6FzmkWoTWXZnRKI9QQFDlWuDyHDP8bAigjv9eC4k8J+W5SWZaZngxhlEaFoaE2FZgof97CfBCaLuN/rJxok1ELgIuAmimtXwpDc5ngm1CdGiYWfe+QOeTHej6jWQqNaF2OLx/kJY6dosMiIxoK2vjpL7zcMqoyHiLmuAVgka5V/nCZzcCjZK/tUjYwmb5lJfYMLTsdJn9wA4ym7dVOzkTkycPU+1CatAhPpgTyjVPEJqwy2SwzWiHW55jh0WgGxvbMT6kuMmJJ/A2jEqjqioydT8CVb0SuBKgU+aU901pc7IVRWb7Tti+s7InzReIpI7dIgOiIdrKbAEw96IchDFnmqAN4E/22lD3Smm4hkxD5W8NUkv546QgPqjEB3VMMIgXhMNNQLpZ0BLUMM6IJ9Q6NgySeH4nbs9e3KFhUBctNF9PBMiblw4MzA/Gubk4wVji3AiS4WWBWJvMOhccKqPEXG+bTEKIDSuSlqjU9kZjsy1wexSRxcB2f/kmYFlou6X+suphbpE1RT3OyZaPSV/jItIM3AUk/e2vV9WPi8gK4DpgLvAAcL6qjohIEm/g6dHALuCtqrp+0vNEuPKtN1SCcQ/BKIhwD39j5UNUGsmVKmclJSL3rmGISAeDuNDUoyT3uuOCFql4lh3wREO6xSHVBpnmKUz0rF70sWS30vHCCM3Pbkd79qIZF9d10XR6RoKtImVtoo5IgeEuIdUWIz6gNPUrTrrAxn7gqNz7PFmwKHV88eZPGBcfUFIdEXmAjEbmJuAC4LP+989Dyy8WkevwApD0VHs8mwm2GiCYq03dhmm7FtP3Ngy8XFX7RCQB3C0ivwD+Bfiyql4nIt8ALsSL+HMhsEdVV4vIOcDngLdOdpKoNJ4bhbBwC7A8qCoVKWelFlr2zJSfKLlMiuuJqeReN7+LXvh3Bpr6XBL9kG4Rhmc5uE2FrdvBsds3p2je0A27eyA1grqKqoLrV8xhsabTCnUbibLmxmGkU0i3edawRL8SG/Etar7oc9JFBIYqMEeS9+3tnBiEdOsUhLNhzBARuRYv6Mg8EdkIfBxPrP1YRC4ENgBv8Te/FS/c/1q8kP/vrHiCw9icbLVBOMR/gzCpaPOj+fT5fxP+R4GXA+f5y78LXI5XwZ3h/wa4HviaiIh/nMpi1rsJCYQb+CGkG/V+OdXvga5UOSuLyGrQx6YiRMxluWmvkuxx8wYzUsk/JlYUEgNKfCjDcKfDSGfoxeN661q3Z2jb0IezdReaSo31no4TbO6M3SIrUdaE4vPNs0gKmaSQ7Ha98W7qBREJ3+eCUW8nOo94wi8+5FlA0y3Vf9cZjYGqnltg1SvybKvA+8qbouKxOdlqh0YIPhKmKC93EYnhuYusBv4beBboVtW0v0kQ6QdCUYBUNS0iPXjuJjtzjjkaASjZ3FUWgRWlxk4t0Kj3KypitdzlLNEx2wRWrRGh/BKXrJD12SsLRwAOxJxkoLnbRTIObhxad2Vo3ThAbEcPOjQMmYwv0AoLtiydND0rm5fcCJY1FUi1CfEhz10y935OFmE5r6jzAy5JxrPkZSwgiWFMiMRt8GfNoG5DBB8JU9TTqaoZ4AgR6QJuBA6c6YnDEYA6OpeqWQCMahGVNky5y1nLwmVqZcKYDqKelc3xJc24sWwTeBIFAY+ctBLvz9C+bginpx+GhlHXt5ppyPUxn2ALnyvLPXJ6D3S5y1rrwmXTqtOCibAlM8HOhdxLC4lmBHE9S5uTUtxEVN54hhExzC2ypmgkt8iAKXUpqGq3iNwOHA90iUjc75kMR/oJogBtFJE4MAtv8PYkB59KSorDJtM2ikIncD2qAuUqZ1Nx2TKMURSaepWmPncsKEb4OZIJAmMoJPrTxLuHcXoHkOGUJ8RcF3Xd7EHkuYItTGBlCwTbDKxsWckrV502USCSSfaTDBNPSTLFMW6i/tTbGSU+ACOd00iXYTQAFnykdmg0t8iASbsURGS+3xuJiLQApwFPALcDZ/mb5UYBusD/fRbwu0nH2eBVLKX+GEaxVPv5q0Q5A8Yak/axTxGf2LDSssv1BRse6n1lzRkW+oirxAcyNO8Yom1dN8n1u4jt3osMjUwu2MLksbKVgiiXtVhKiY24xe1f7HlDxIdcz4oXgWfLPjX4qWdsTrbaoQHdIgOKsbQtBr7rjwFwgB+r6i0i8jhwnYh8CngQ+La//beB74nIWmA3cE5RKan3F4JhTExFyplZ2oyicCHRrzT1u6OWtXBHRPA7LNw898c0sZ4hZHgESfs9oRlffBVwd8w+b34rW4mpTJ02DeID6lnaQhQcgzZZWQ7P84aCKzhpiA9BukUj5V0QYJ2t0yeK+Vkz2JxsNUUjukUGFBM98hHgyDzL1wHH5Fk+BJw9pVSYZcxocCpTzrDOEWNSYsNKU68SS+mYWPOfmyw3SBecjBIbzBAbGMEZTEE6A66LhIVajmDLCjZSpJUtr2Fr+uPZyl/WmIYA0bE57rKPM8luk4m60HpRbzJ0NyGoo96+UWzr23uqeIK52K0NNW1MsNUQDTQnWz6iEyan5J2phmGMo3HfdcYkiKs09SmJAfUiROYTauoFyYgPZYj3pZDhFJJ2PWuaL7SyBFswBi1XsOUjn5WtlpnipYh6Ijhvg2QCK0ohUVdIzDkZJT7oTTMggDoTCD8j+tRRkakK5hZZU2g6PflGdUx0RFs9VdaG0eBYr2/xVNutybO+eBNhB5M5By56We6PIy7xwQzOUApJZfyxUZot2FzNFmxaQISErWy5hN0hs37X1jM1ZVfkiSzhk5WnPM+QaAExpuCkQBKgMT+vHaJpcTOMMmNWttqhkd0iAyIj2mysjWGUn4qLKSvX+clxWasWThoSfUpi0PUmc04rklHvfey7RkpacVKZMaHmutmCzRdmo4ItTDHj2EoUCTJSTNMVOdMkxAcLHHIiUZV7fyfpCBD1gp6knWCKAU/AGUYjYXOy1RANHHwkTDSeWMUsbYZRCayYRYMq54MoxAc861ps2MUZ8QKOjAYYcQMB52YLNdf1XNlzx6sF5FrZCK8qMH4NJrSi1arL5HQ6IjPJqbtBBmSJOtUs4ZbPmisZcDKMzbHnYtY2o3GwOdlqB3XNyuYTDdEGVW/EGEYjYBZtw0lBcq9LfMBzh5S0ZrlBSsYXa66Ot6wFwi5rzrQCVraJmGzbCdaLI2gt1N/TtLS5CcEZnsD66ORXVgXdIf1l3ti17A0kM7aPuEw4Sbph1BPmFlk76FTrlzomOqKtBNg4GqNUVHucUVmw6JENjQRh/PsCV8iQdS2IEplRfwJtzWtZk7BlLd94tdyxbBOF68+xrtWqRa0g07gcFRjpiNGcmjhgSxYhETcq3IL3V7Aqz+tMAvdXX7XpNNNsGDWHBR+pHcwtMovIiDYTXNVHUl7B0JhTsDe3UajX59EsbY2JMwLJXs8VUjKMCTZfAIQFG8qY8MqyrPkHywkgIhMFHAm2LzXiAPUZ+jnVIsRbHOL9RTZUXB19X4ctbcFvDQu43Nd60JEjjI5jrIVIko36HquFvIk8NidbTWFukdlERrTVWmSwekPUa8ihXg+8Og7EBG1w8WaUmEYs5lUsQpKBpj4lPui5OAZCLWv82qg4w3ePdP3fIYsaZFvZguVFiqaZWNFEZNz+UXeRFGYgLASGO2M4I4qTKiJIS+47WmRs/rWQa2RW4sJ/w0JNbVhblGlUsVpKTLDVEA0+J1s+oiHa1F5G1UZSGcgEmaBIJgMZv8GUiE0ajcyoEaycVZ4q3HNxITGgxIdcnBEdfccGbo9ZLpG57o+QLdRg/DyaZahIs8SZSBFh7h0iq9xm6IrsxmF4dozkHiYWbiHBFrhFhgWbiowJt2BdePtwev3d7BVh1C3mFllTNPqcbPmIhmjDBhlWFVVI54voBt6kPgrWOVUfWDGre5y0FxUyPuhmd4iNjjcjW7AxZmUb/Q3Z7pBhquUV4cj4c4tE9pmeaUdkpkk84daNN39eAUbFV1iwjSbCt7KFrW4ysZuddaAa9YpZ2WoHc4vMT0RE2wwwsTdjJJUpOIZLRbyJVxv1PteRhXFGLltGTeCkoLk740eEJKvcSmBtyxc8JCAUEXLcunz/iySfi2NeEZaL44wPZiLO2LQBUe41L0FZyySE4a4YTb1e8Ji8919C49lCgUdUZFSwZY9rC08SSJZLpL0ejHrF5mSrISz4SEGi8xQ3qiioNi5j4bzzEROvxz3CbaOyUk/P5Qxdtozo09TvZgk2CYKK5BC2sgHVf87DAi6PUCs4rq0BxkJnEsLwLMcTbiMFNsoRa2S5QYq/PGyVI2ub0fdCDfdR1WOHlAUeKRE2J1tNYVa2wkRHtBUx3tooPaKKinh1dW5gAZGxXlnLH6MM1FNDq9oNLCeDZ42BLMGWdY9zhbuOrc8dz1Yy8o1PCy8LW84m268Y61xEKOWzrY4w0u7Q1O+G8jh8Mn9RINAccoRceJucwCOqo2KvnspjPWD5URoknqh2Eowi0Uym+p2IESYSoi2vy45RORxQV8Y6WcPBAEQsb+oIawSUjyjcW3VC3m/5BFslEZncvXHcLvncKAsfQxyBqHbKluF+p1oc4qLeGLc8og3IdokMCbbR+dvC3pGB/jP/SKNecWwcW81gbpGTEgnRNi1MSJQOEXA0W7jhV/p2n8eoh/Ftlp11iwpoTCCtfpnGiwir4M3DFR67xvTKdvi4hXAk7/ElGGMVtpRluUXmcZEMvYNGBV2w3QTir+qUqyNSIJP07qOT0fHlOdfiliPYJgpCYp1zRikRkauA1wHbVfVQf9nlwN8DO/zNPqqqt/rrPgJciNcN835V/VUJEmHBR2oIc4ucnOiINqswqksg3BBwfeuauYBnUwfPaBSsQUb5cOOCM+KV5XCACZmKS2GW62KBjptQB4Y63hQDYwsKbJ/lep0zOXZIgE1kbcu7rpFQT3S5CU+UicsEwo1xgi3fODat1/d8rT4mddA36HM18DXgmpzlX1bVL4QXiMjBwDnAIcA+wG0isr/qzOb0MMFWQ9icbEVhos0Yj+8uCVi+GEYN4cZBY744F/F+B+Hiw0XZwZuHMbTMG9saEmvh345O3Q0xEHYh98Zx1rbAcpdjbfO2y7G2hd0kw9a2qFKmV2eQZ25MEAmCzuRu5Cchj2DLcok0t8joUSd5oap3icjyIjc/A7hOVYeB50RkLXAM8MdpJ8DmZKspbE624oiOaIuol0tDY3lSf9RJg8DIjxsT3Lj4Fhjf2hbWX5N1wjgy5vroC7uA0YBFwbrQ+2GctW2cG6QvuHKtbRoWYf7/qbpJRpSyWbXDQtuRsf+5QaTIcYUsMAbOrO9GhblYRP4W+DPwQVXdAywB7g1ts9FfNg4RuQi4CKCZ1vxnMLfImkLTqWonoWaIjmgrEvO7N6qJSg37rljAn4bAjQtOWj0R5Vvb8ua9CEooWIkv7lRA8ERSllCbiuIftaAFu45Z1gTGrG1hN8kc4SYuvjgLCbdcN0knwuWxXEUtJMIhHMa/yHuREz3SMCrI/wD/jvfk/TvwReBdUzmAql4JXAnQKXPyPsEm2GoIc4ucEkXbjkUkJiIPisgt/v8VInKfiKwVkR+JSJO/POn/X+uvXz7pwVWRIj9Gg6HqNdjSGUilYSQFwyPedxWeh2Kf0+k+q2UtZ7WCRvhTA3gukoI6niui5oaAnwAVGRNCgfAKcIIAF/7HCf0W73xZ04RA9jaOM3pcyTpPKOSlI2MuTY63neTsi+OA44ytmwa1XM4Ct8axydILbztumwhrXKP+UdVtqppRVRf4Jp4LJMAmYFlo06X+sqljc7LVFOYWOTWm8mR/AHgi9P9zeANKVwN78KL+4H/v8Zd/2d9ucoI5wuxjn+DjujCSQoZTSCqNpDNIxkVcRTIuZNzqp3Giz/T4QHnLWQ18oky1702R9y/TJGNCzRdIKjJmKc51fQ6W54yFIthnVHhRvHDLEmITCLdAqOUKN3FGhZ2ExNq4Y02PspYzIVswlfoDjApyYJx4y/2fG4SknGmzT/1+ZoqILA79PRN41P99E3CO30myAlgD/Gla57A52WoGixY5dYqq9URkKfBa4Fv+fwFeDlzvb/Jd4I3+7zP8//jrXyHT7Q4NU+0GuH0q//EFWqH1kkpXL21loBLlrNqVvn0q81HHc5Oc1NoWBKkIoaMiz1s/Xqjl/p9EuOVuExJfEhZv4vjBA3LEXCw23uoWiLdpuEFVpD7TCn0gK4x/uHEdLM9yhbSPfWbymQIici1eIJEDRGSjiFwI/KeI/FVEHgFeBvwzgKo+BvwYeBz4JfC+aUWOtDnZagubk23KFDum7SvAh4EO//9coFtVA7tmeNDoEuAFAFVNi0iPv/3O8AGzBpMmOilXQ9ioUQKxNhGqnttkvAov6vI8r1+hjOWsqXV2OdJcHqL2OqhBtzI3ITgZb2ybOuKJORcQxqJEAqNj2/xxbAjePwfUVcRxQNRbFdSxufM65gYmiXnHknA5dvBOHiyLxbxvf4yadw4FYqMBRiThi7NweWtvJcsF8+kp35qvUOJyBtUva4XmXzPGUwqrUSmo5zxT1XPzLP72BNt/Gvj0VM7Ry56+2/T6p0YXZJh6lNvoM48875s6w64xm/0KrZhUtIlIMDniAyJyapEnnJTwYNJZLYs1ylHAjCozgUCSjOuFBq9xKlHO2uYu08iJoVqhBu+bCmQSgmRAVL0GouM3WHNE1qgwkmzhhi++cPHEX4yxeRx9IRf+P3ZyBVdRx1+WiGetU8fBbW/y/vvvfrcpRrotu0pKt8ay5xETz4KYxRREW7nKGWSXtfY5yzQqwsCILvaMzJinVPXF1U5EORGRP9s11j6lusZiLG0nAG8QkdcAzUAncAXQJSJxv3cyPGg0GFC6UUTiwCxg14RnUMzSZozHd7USlbH5mXLJZCDmZPe81yZlL2fBOBujcdCY4CYg5urE1jbHF2mabXGDkHgLBJiroyJWY0K6LeFNM+AvS7c4WZZJFfEmgw6nK2eMVQUpf30WYHWaYRiGUUImNVGo6kdUdamqLsebsf53qvo24HbgLH+zC4Cf+79v8v/jr/+dqtVexjQo8rGRTO1PKFeRclbt8RD2qconk/BFUzDGKRjblvv2d4BYniiQvsDyxscJGnfIJGOMzGpiYJ8WBuclGO6KMzTb+6SbHdLJsU+mSbLGV5V8jNUUsPrMyKUaY04NwzCmw0zmabsEuE5EPgU8yJiv8reB7/kz2u/GqxgnQbFeSWNSXM2elykIfBBz6vn5KWE5MxqVTEKQNOA3GtUXPOLoqJvkaGTJGN6YNMa+vT+gcU8AZpKOF+ik5g3co5S8nFnj3CiEPRsl48pqJ6AC2DXWByW5ximJNlW9A7jD/72OsTk2wtsMAWeXIG313BA3por4LlrBb59ICLYSu2aWs5xZY6FBESHTDPFBHXOPFDxLVc6YMRUh0+yQbnGyRVu+w9bw81T2+qyG741h1AL+ONK6xq6xPijVNc7E0lZaqt3wNqKLSN7f6kSkm9+e3cpQ7vtc++MiJ0QdId3sEB90s6xtuduk2mNkmqTu74dh5KNcHRF1ZJU2DKNKREa0iUWPNCYiTwPSnplpYAKzMA1wb9SBTLMDQy6O724s/nW7MSHT4gs2aIj7UTZs7JKRgz0PhmHMlMiItrxYo8GoBWrIImENB0MdL8JjLCY4KW8uNTfhWeEQe0ZKht1HwygbIvJqvMivMeBbqvrZKidpWojIVUAwFcmh/rI5wI+A5cB64C2qukdEBO+aXwMMAO9Q1b9UI93FIiLLgGuAhXhvxStV9Yo6u8Zm4C4giaerrlfVj4vICuA6vLk9HwDOV9UREUni3ZOj8aIRv1VV1xdzrmhMcKV4Ai33Yxi1QL5n157f8lPBqIN1h0AmKaTaHEY6YuPC9BuG4SGqM/oYpUdEYsB/A6cDBwPnisjB1U3VtLkaeHXOskuB36rqGuC3/n/wrneN/7kI+J8KpXEmpIEPqurBwHHA+/y8qqdrHAZerqqHA0cArxaR44DPAV9W1dXAHuBCf/sLgT3+8i/72xVFdCxt9nIzjPJjxWwMuxdj2L0oMdZgNzzsOSgLxwBr/QBCiMh1wBnA41VN1TRQ1btEZHnO4jOAU/3f38ULmHSJv/waf9qRe0WkS0QWq+qWCiV3yvhp2+L/7hWRJ4Al1Nc1KtDn/034HwVeDpznL/8ucDmeCD3D/w1wPfA1EZFippOJjmgzDKO82Dgbw6gcVtYMo1wsAV4I/d8IHFultJSDhSGRshXPtRDyX/cSfFEUdXxxeiRwH3V2jb719wFgNZ4V+FmgW1XT/ibBdUDoGlU1LSI9eC6UOyc7T0REm7mTGYZhGIZhGEaAqqpI7Xe3ikg7cAPwT6q6V8KRwOvgGlU1AxwhIl3AjcCB5ThPRERbCIsIaNQyUZmGoBBWvAyj7IiCuNVOhWHULZuAZaH/S/1l9cK2wCVQRBYD2/3lNXndIpLAE2w/UNWf+ovr6hoDVLVbRG4Hjge6RCTuW9vC1xFc40YRiQOz8AKSTEo0ApGAJ9ZMsBm1TvAc27NcUSwYgGEYFcECHEWB+4E1IrJCRJqAc4CbqpymUnITcIH/+wLg56HlfysexwE9UR7rBeBHg/w28ISqfim0qp6ucb5vYUNEWoDTgCeA24Gz/M1yrzG49rOA3xUzng2iYmkLokcahlFGGlfANOp1G1XEHrnGxPK97PjjgC4GfoUX8v8qVX2sysmaFiJyLV5AjnkishH4OPBZ4MciciGwAXiLv/mteKHw1+KFw39nxRM8dU4Azgf+KiIP+cs+Sn1d42Lgu/64Ngf4sareIiKPA9eJyKeAB/HEK/7390RkLbAbr9OhKKIh2gzDqAzWoDCMimAdBYZRPlT1VrwGfk2jqucWWPWKPNsq8L7ypqi0qOrdFJ5Qpl6u8RG8ACu5y9fhRTrNXT4EnD2dc0VHtFkFZxiGYRiGYRiGMY7oiDbDMMqKBUcwjApi/ZCGYRhGCYmIaFNwrTVpGIZhGIZhGIaRS0REm2EYFcF6/w2j/FgkQcMwDKPEREe02Zg2wzAMo06o7aliDcMwjKgRHdFmGEbZsYh2hmEYhmEYtYcUOZ9beRMh0gs8Ve10lJl5wM5qJ6LM2DVms5+qzi9nYqaCiOwA+qnvPLJnsD6o2XIGVqfVEfV+jVO9vsiVNcNoJKJiaXtKVV9c7USUExH5s11j7VPL16iq82s5/cVQ79cHdo01gtVpdUC9X2O9X59h1BtOtRNgGIZhGIZhGIZhFMZEm2EYhmEYhmEYRoSJimi7stoJqAB2jfVBrV9jrad/Mur9+sCusRao9fQXg11j7VPv12cYdUUkApEYhmEYhmEYhmEY+YmKpc0wDMMwDMMwDMPIg4k2wzAMwzAMwzCMCFN10SYirxaRp0RkrYhcWu30TAcRWSYit4vI4yLymIh8wF8+R0R+IyLP+N+z/eUiIv/lX/MjInJUda+geEQkJiIPisgt/v8VInKffy0/EpEmf3nS/7/WX7+8qgkvEhHpEpHrReRJEXlCRI6vh3ysh3IGjVPWrJzVZh5aOYt+HuViZa0+8tEwGoGqijYRiQH/DZwOHAycKyIHVzNN0yQNfFBVDwaOA97nX8elwG9VdQ3wW/8/eNe7xv9cBPxP5ZM8bT4APBH6/zngy6q6GtgDXOgvvxDY4y//sr9dLXAF8EtVPRA4HO9aazof66icQeOUNStnNZaHVs6in0cFsLJWH/loGPWPqlbtAxwP/Cr0/yPAR6qZphJd18+B04CngMX+ssV4E64C/C9wbmj70e2i/AGW4r3gXw7cAgiwE4jn5ifwK+B4/3fc306qfQ2TXN8s4LncdNZ6PtZrOfOvpe7KmpWz2sxDK2fRz6M812VlrQ7y0T72aZRPtd0jlwAvhP5v9JfVLL7LxJHAfcBCVd3ir9oKLPR/1+p1fwX4MOD6/+cC3aqa9v+Hr2P0Gv31Pf72UWYFsAP4ju8u8y0RaaP287FW0jkl6risfQUrZ7WYh7WSzilRx+UMrKxBfeSjYTQE1RZtdYWItAM3AP+kqnvD61RVgZqdX0FEXgdsV9UHqp2WMhIHjgL+R1WPBPoZcxsBaj8f64V6LWtWzjxqOQ/riXotZ2BlLaDW89EwGolqi7ZNwLLQ/6X+sppDRBJ4ldsPVPWn/uJtIrLYX78Y2O4vr8XrPgF4g4isB67Dcye5AugSkbi/Tfg6Rq/RXz8L2FXJBE+DjcBGVb3P/389XoVX6/lYK+ksijova1bOqNk8rJV0FkWdlzOwslYv+WgYDUO1Rdv9wBo/WlMTcA5wU5XTNGVERIBvA0+o6pdCq24CLvB/X4A3LiBY/rd+pKbjgJ6Qq0IkUdWPqOpSVV2Ol0+/U9W3AbcDZ/mb5V5jcO1n+dtHujdPVbcCL4jIAf6iVwCPU/v5WBflDOq/rFk5q+k8tHIW/TwaxcpafeSjYTQU1R5UB7wGeBp4Fris2umZ5jWciOde8AjwkP95DZ6/+2+BZ4DbgDn+9oIXZexZ4K/Ai6t9DVO83lOBW/zfK4E/AWuBnwBJf3mz/3+tv35ltdNd5LUdAfzZz8ufAbPrIR/roZz519EwZc3KWe3loZWz6OdRgeu1slYH+Wgf+9T7R1Qj3VFkGIZhGIZhGIbR0FTbPdIwDMMwDMMwDMOYABNthmEYhmEYhmEYEcZEm2EYhmEYhmEYRoQx0WYYhmEYhmEYhhFhTLQZhmEYhmEYhmFEGBNthmEYhmEYhmEYEcZEm2EYhmEYhmEYRoQx0WYYhmEYhmEYhhFhTLQZhmEYhmEYhmFEGBNthmEYhmEYhmEYEcZEW4URkfUiMigifSKyVUSuFpH2Mp/zahEZ8c+5W0R+IyIH5myzWES+KSKb/e3W+fsd6K9fLiLqr+sTkW0i8nURSUxw3n8Xkb+KSFpELi/nNRpGmEYpZyKyQESu9Y/XIyL3iMix5bxOwwholHLm73O7iOwQkb0i8rCInFHO6zQMw8jFRFt1eL2qtgNHAEcCH6nAOf/TP+cSYBPw7WCFiMwF/gC0AicBHcBRwJ3AaTnH6fKP8yLgeOB9E5xzLfBh4P9KdA2GMRUaoZy1A/cDRwNzgO8C/1fuhrNhhGiEcgbwAWCxqnYCFwHfF5HFpbkcwzCMyTHRVkVUdSvwK7zKDgARuVREnhWRXhF5XETODK3bICJH+7/f5vcUHuL/v1BEflbEOQeBH4fPCfwzsBc4X1WfVY9uVf2Oqn61wHG2A78BDp7gXN9V1V8AvZOlyzDKRT2XM1Vdp6pfUtUtqppR1SuBJuCAydJoGKWknsuZv80jqpoO/gIJYNlkaTQMwygVJtqqiIgsBU7Hs0gFPIvXOzgL+ATZvXl3Aqf6v08B1gEnh/7fWcQ524Bzc875SuBGVXWnkPZ9gFcB9xa7j2FUg0YqZyJyBJ5oWzvJpoZRUhqhnInILSIyBNwH3AH8udhzGIZhzBQTbdXhZyLSC7wAbAc+HqxQ1Z+o6mZVdVX1R8AzwDH+6jvxKjPwKsL/CP2frJL7kIh041m9TgTOD62bB2wN/ojIG0Sk2+8d/XXOcXb6x9kE9APXF3fJhlFxGqqciUgn8D3gE6raM9n2hlEiGqacqerr8NwtXwP8eirC0DAMY6aYaKsOb1TVDrxexgPxKhkARORvReQhv5LpBg4Nrb8TOMnvqYzhuYWcICLL8XoyH5rgnF9Q1S5gOTBItvvULmDUN19Vb/K3/We8Xvsw8/x1rcA9eO4whhFFGqaciUgLcDNwr6r+x0TbGkaJaZhy5h8v5bv9/42IvGGy7Q3DMEqFibYqoqp3AlcDXwAQkf2AbwIXA3P9yuRRQPzt1wIDwD8Cd6nqXrwexYuAu4vp9VPV5/EGVF/hN/QAfgu8UUSKfh78sQRXA8eJyLxJNjeMqlHv5UxEksDPgI3Au4s9tmGUknovZ3mIA6uKPYdhGMZMMdFWfb4CnCYihwNteAOcdwCIyDvxeibD3IlXCQauI3fk/J8UVf0NsBmvcgT4EjAb+J6IrBKPDrIHd2fhNxTPx6tkdxXYJiEizXjPWVxEmkUkVmw6DaOEfIU6LGfihSi/Hs/acIG5axlV5ivUZzk7UEROF5EWv157O974u6LTaRiGMVNMtFUZVd0BXAP8m6o+DnwR+COwDS8M8T05u9yJ51N/V4H/xfJ54MMiklTVncBxwBBwN944gYf84743Z79uEenz03c88AZV1QLn+CZeY/Jc4DL/9/kFtjWMslHH5eylwOuAvwn28T8nTTGdhjFj6ricCXA53pi9HXjWvbeq6l+mmE7DMIxpI4Xb24ZhGIZhGIZhGEa1MUubYRiGYRiGYRhGhCmLaBORV4vIUyKyVkQuLcc5DMOwsmYYlcDKmWEYhlFtSu4e6QeaeBo4DS+a2f3Aub5/u2EYJcLKmmGUHytnhmEYRhQoh6XtGGCtqq5T1RHgOuCMMpzHMBodK2uGUX6snBmGYRhVJ16GYy4BXgj93wgcm7uRiFyEH6K3ra3t6DVr1pQhKUaA67ps2LCBRCJBPD61bFdVhoeHSSaTiEjR+zU1NTF79uwp7VNPPPTQQztVdX4ZTzFpWbNyVjv09PSwadOm0f/TLTfJZJIVK1ZEstwNDg7ywgsvkE6ni95nsusYHh6uejmD7LImIkcnEokyJmncuenq6mLBggXEYqWdVWXPnj1s3boV8OqCqdLV1cXixYsj+TwahVFVXNfNyvNHH3203GXNMIwJKIdoKwpVvRK4EuDII4/U22+/vVpJaQiGh4c577zzWL9+PXPnzmXJkiXMmjWLzs7OvCJuaGiI3t5eNm/ezNatW2ltbeWVr3wljjO5cVZEaG9v54wzzmDRokUNW1nPnj17Q7XTYOWsdvjBD37A5z//eUQEx3EQkdGyU0wZChpXRx99NF/96leppGgoFlXloYce4pJLLmHbtm0Ftwtfe+69CN5Bwf+nnnqq6uUMsstaMpnUJUuW4LpueH1ZzrtgwQLe85738IY3vKEsed7d3c3FF1/MU089haqO+0D+a3MchxNPPJHLLruMRYsWlTxdRvlRVVKpFENDQ2QyGVauXBmJsmYYjUo5RNsmYFno/1J/mREB0uk027ZtG20wJRKJvEIsk8lk9Ya3tLQUfQ4RYcGCBQ0t2CqElbU64vHHvSFShcqMiEza8FdV9t9//ylb0yuFiHDEEUfw/ve/n09+8pMMDw/n3S64ThHBdd0soRb8V9VKvV+mVc7KLdji8Tgnn3wy73//+1m+fHnZ7sWsWbO44oor+NKXvsQ999zD3r17s/In37WtWrWK888/n9NPP51kMlmWdBnlR0RGvXMKlVXDMCpHOWr2+4E1IrICr2I7BzivDOcxSkAqlSpqu6k0CESEuXPnTjdJRvFYWWsApmJlExEOO+ywSHeWiAinn346PT09XHHFFUULt1gsNirUwsKtAsyonJUjjQsWLOCiiy7ida973ZQ61KZD8D7/xCc+wYYNG7j33nu57rrreP755wvu87KXvYw3vvGNkX4OjeIIrNzNzc3VTophNDwlF22qmhaRi4FfATHgKlV9rNTnMSrLVMemdXR0WIVdZqys1ReHHnoo//d//zejY4gIbW1tJUpR+YjFYpxzzjmoKv/1X/81YS9+INQymUxe4VZuplvOyiHWHMfhuOOO40Mf+hArV66s6Ds2Ho+zatUqVq5cyQEHHMDFF1/M8PBwXhfJPXv2VCxdRmWw+twwqk9ZfGhU9Vbg1nIc26gOxQYhCcbjzJ49uwKpMqys1Q8nn3wy11xzzaRjvSYSAwcffDAHH3xwOZJXcmKxGOeeey4vvPACP/rRj/JeVyDQJhJulSAK5ayzs5O3ve1tnHfeeXR2dlYtHSLCwQcfzJIlS1i/fn1WvmUyGQAee+wxUqkUTU1N1UqmYRhG3VH+bkqj4XAch/b29monwzBqin322YdPf/rTLF++fMLtCnWeLFmyhEsuuaSqDfqpEovFuPjii3nVq15V8LpyLTmBMKiQa2TVcRyHI488kq997Wu8+93vjkT+JpNJli3zhvnlC5gzPDxctOu9YRiGURwm2oyimEoY6Y6Ojkg0LAyjlhARjjzySL797W/zoQ99iIULF5JOp8lkMlnR+oIw3MHvtrY2XvGKV/D5z3+eAw88sObcmDo6OvjYxz7G3/zN30xZuNU7HR0dvPvd7+arX/1qpMYqxmIxVq1aNW55kL4tW7awffv2SifLMAyjrolmiDGj5ARui9Pdd968eUVvG4vFSj5XkGE0AiLC7NmzOeecczj88MO56KKL6O3tzVoPnqvcq1/9avbff3+OPfZYFi9eTCwWi0yjfqoEwm3r1q08/PDDebfJdZWs1Ji2arFq1Souu+wyjjzyyEhe5+GHH571P+y6G0QpXrFiRTWSZhjGNBARBdao6toSHvOjwEpV/btJtrsa2KiqHyvVuYtFRN4B/J2qnljpc0+V6NUERlmIx+OcdNJJ09o3kUhMKWxzOSZ4NYxGQkRYs2YNq1atIpFIjHaEBKLsoIMO4tJLL+Wss85i2bJlxOPxmhVsAR0dHfzbv/0bK1euLLhN2OIWWBrrjebmZt7ylrfwjW98g6OOOiqSgg1g2bJl44LeBM9gOp3mhRdeyLebYVQNEVkvIoMi0iciW0XkahFpz9nmxSJyi4jsEZFuEXlcRD4tIrP99e8QkYx/jD4RWSci7y1TepeKyA9EZJeI9IvIn0TkdeU410zIuSd7ReShIJ2q+pnJBNs0z/l2Pz/3ish9IrJ0ku0vF5GUn8ZuEfmDiBxf6nSVm2jWBkbJcRyHc845h1e+8pVTmsMpFotxxBFHMGvWrKK2F5Gig5YYhlGYYK7EwHrtOA6xWIx4PM6hhx5ad2VMRFi1ahWf//znpyTc6oXg+j/72c9yySWXsGDBgkjn8dy5c+nq6iq4fvPmzXWVP0bd8HpVbQeOAI4EPhKsEJGXAncA9wAHqmoX8GogDYRNy39U1Xb/OG8G/lNEjizm5L7QWF7EdnOAu4ER4BBgHvBl4IciclaBfarpPfdH/350Ad8GfhwI3VLjC+3vABf557sYGCpi1x/5aZyPd29/KlF+yebBRFsDsWDBAj796U/zsY99jEMOOWRCa1hLSwvHHHMMl156KWvWrJlS42Hx4sWlSK5hNDS7du1i8+bNo//DAR+6uroi3aCfLmHhMmfOnILb1ZsYSCQSnH766XzjG9/g1FNPjezk6GE6OjomdH8cGBioYGoMY2qo6la8aTyOCC3+T+A7qvofqrrN3+55Vf24qt5R4DgPAk8AB5U4if8M9AEXqupWVR1U1WuBTwNfDMSGiKiIvE9EngGe8ZddISIv+FaoB0Rk1M1KRGIi8lEReVZEev31y3JPLiJJEfmCiDwvIttE5BsiMumkkKrqAlcBLcAq38L1/dBxT/StXN1+Gt+R59wdInK7iPxXAVGleEL6OVV1VfV+Vd05WdpCaUwB3wUWAeMmFZ7u/RORA0XkNyKyW0SeEpG3FJumYjHR1mA0Nzfzxje+kW984xssXZrfmtza2sqXvvQlvva1r3HiiScW7Z4j/ri51tbWUibZMBqWYPxWuN6Kx+Psv//+VUxVeQlcQz/84Q9PGIW2XixtixYt4rLLLuPyyy+PvHUtl8MOOyyrfgjqgJe//OX8wz/8QxVTZhgT47vTnQ6s9f+3AccDN0zxOC8B9gf+XOIkngbc4IugMD8G9vXPGfBG4FggmO/lfjwxOgf4IfATEQlmR/8X4FzgNUAn8C4gXw/LZ/1zHAGsBpYA/zZZon1r39/hCc5nctbtB/wC+CqetesI4KGcbeYCvwXuUdX3a/6X/Ii/3499i+SUEJEk8A7ghQJib8r3z39+fuNvvwA4B/i6iJR0Dp7od+cZJUdEaGpqKijG2tvbOfTQQ0kmk+zevXtKDaNkMjlhD7lhGMWxceNGRkZGsoJvBN/FuivXKo7j8KpXvQpV5VOf+hT9/f15t6tl0RaLxTj11FN5//vfz3777VdTYg28euTNb34zixYtor+/n5/+9Kc8/fTTrFy5kssuu6zun1GjZvmZeAE32oHfAR/3l8/GM2RsDTYUkf/Ec8FLAP+hqp/yVx0nIt1AzD/O18gRKCVgHrAlz/ItofVP+b//Q1V3Bxuo6vdD239RRD4GHAA8jCeoPqyqwb7jIj/51q2LgMOC44rIZ/AEyUdyt/cJ7kkaTwifqao9Oe+184DbfIshwC7/E7APcCfwXVX9fIHzgCf6HgbWAb8RkdNUdbeIfApoUdUPFtjvLf5YuxHgUeDMfBtN5/6JyFuB9ar6HX/5gyJyA3A28IkJrmVKmGgzJiQcua4YgqAJhmHMjJ07d44LbS8izJ07l4ULF1YpVZXDcRxe/epXk0ql+OQnP0k6na52kkrGwoULefe7383rXvc6mpqaak6wBXR1dfHa174WVWVkZIQvfvGL7L///sydO87jyDCiwhtV9TYROQVPhMwDuoE9gAssBp4EUNUPAx/23fvC7eV7g0iDIrIQuBb4DHkEjYjsCzwSWtQJPCIigQXtH1T1h3nSudNPSy6LQ+sDsqL+iMiHgAvxRJD65wxCgC8Dns1z3DDzgVbggdC7SfBEaiFG78kETHbu1+JZ6L5RaAPfonUhsK+qbvEtbbeJyCuBE4AvTHD8H6vq2ydJ43Tv337Asb5wDYgD35vsfFPB3CONgqjqlOZDEj9ceXNz8+QbG4ZREFXNmpw47CLZ3t5OS8ukQwvqgkC4veUtb4lsFMWpICIcffTRfO1rX+NNb3pT3QRtEhGW7LsSSTTz/I697OkfqXaSDGNCVPVO4Gr8Rr6q9gP3AW+a4nG24blUvr7A+udVtSv4AM/jWbCCZfkEG8BtwJtEJPfF9xY8kfZ0+DTBD3/81Yf97Wb75+zBE134+46fZDGbncAgcEgonbP8IB4zYbJzfxP4JXCrL87y4eCJxwSAql6K5854L5474y9mksAZ3L8XgDvDee0HqylpZNHarwWNsqGqbNq0aUr7mEuMYZSGhx56KO/ywHW5UUgmk3zgAx/grW99a00Lt1mzZvG+972PK664YsrBnWqBp1PzcI65kMfjh3LjPU9UOzmGUQxfAU4TkSAy5IeBd4nIpSKyAEbHvhWMuOOPwToTeKzEafsyMAv4togsEpFmETkXuAz4fwXGegF04Lko7gDiIvJveJaigG8B/y4ia8TjMP8aRvHH0X0T+HLoPiwRkVfN8Jp+ALxSRN4iInERmSsiR+RsczGe2+fN+QKfqGovnrD7uogsFJEmPDfXlcBeZu5BON37dwuwv4icLyIJ//MSESlpgJrarQGNsjFr1qzR6GVTmQcpsLTVW2PEqD+COb5c1yWdTjMyMkIqlSKTyURmnFQ+K7fjOBxzzDENV8aam5v5p3/6J9785jdXOylTxnEcjjrqKL761a9y4YUX0tHRUXf5p6q0t7bidC5Eku3c+9RW+obM2mZEG1XdAVyDH2BDVe8GXg6cDDztu7r9Em8agK+Gdj1e/Hna8CJH7gD+scRp2wWcCDQDj+ON/foX4HxV/dEEu/7KT/PTwAa8UPhh98kv4QUz+TWeyPk2XqTHXC7BG5t2r4jsxbP8HTCDS0JVn8cL4PFBYDdeMJHDc7ZRvPF0G4GfhwKAhHk7sA1vPNlO4J14rpEOXuTKmTCt++eLyb/BC0CyGW9s5OeAkvaw2pg2YxzLly+npaWFkZGRosM2B+5bHR0dZU6dYcwMVSWdTjM4OIjrulkiTURIJBI0NTVVfcLqCy64gJNPPplvfOMbbNy4kcWLF7Nw4UKOOuqoqqWpmjQ3N/Oe97yHp59+mocfHjd2PpK0tbVxwQUX8La3vY22tra6E2sBCmzrd2nrnE0m4ZAizvBIhnbzlDcihKouz7PsvTn/78MTFoWOcTWeW2XJ0jDBts/jRSqcaBvJ+Z/Bi2j4rtDi/8xZ/yn/U/BYqjoEfNT/TJbOqylwT1T18pz/v8eLdJm73TtCv13gbyc43268MWe5nDDBPpdPsO5q/PTP8P49hTcur2yYaGtgJmtADA8P09fXV/TxHMeZcLJVw6g2qsrg4CAjIyN5LWpBQIVUKkUymaS5ubkqDW0RYfXq1SxevJivf/3rALzvfe+rmTm8ysXcuXP57Gc/y6WXXhp54bZ69Wo+8IEPTGnalFrFEeHdJy3hzUfMQ9VlXkeStmTjPqeGYRjloL5rEqMgTU1NvPe9782K8uU4Dp2dneyzzz7A1MNpB41cw4gqrusWFGxhVJWhoaFxlrhK88LuQbqHXJLtXSxatpJEIlG31ppiEBEWL17M5z73OVavXl3t5BRkzpw5/O///i8nnXRS3Qu2gLZkjP3mtbJ8fjvtzY39nBrTQ0Re7U9KvFZELq12egwjajRGbWKMw3EcTjvtNL7xjW+wevVqEokEl156Kddeey3/+I+ea/aOHTuyIthNRktLS8NEtTNqk6mKsMHBwSmLtmDS50AgDgwMMDAwMGURqKr85tHtDBxyLqmXXMSDO20qDfCE26JFi/jQhz4U2dDyCxcuZN68eSZcDKNIRCQG/DfehNcHA+eWemJiw6h1TLQ1MCLCmjVrOOKII4jFYhx77LEsXbp0tDc/lUoV3cAUEebNm2dztBmRJh6PT8m9MJ1OF10OgrFyQ0ND9PX10dvbS39/P8PDwwwPDzM4OEhfX19Rlj6AbT2D3PHoRmidDW1zEWp7MulSIiIcd9xxfO5zn6t2UvJiYs0wpswxwFpVXaeqI8B1wBlVTpNhRApzOm9w9g6meGqPQ6ZzKT3D2et27tyZf6cCNDc3N4wrkFG7NDc309/fX7QQGxoaIh6P4zhOVmM82D+YUy2IQjkRmUxmNLjPZJMq9w6mkeZZJDKKE4vx2LZh0q6SiJkgAE8YveQlL6l2MgzDKA1LyI7St5E8AStE5CK86IIAR1cgXZHBcZyGmu6l1gnaBdNgp6rOz7fCRFuDs2lXL0+NLCC96jTW7k5zmOpoQ7K3t7fo44gICxcuLFcyDaMkiAjxeJxkMsnQ0FBR+7iuS19fHy0tLSQSCWBs4vmhoaEpT0IPMDAwgOM4eSNUptNpNm/ezNL58/mfdxzJ87uHcByH1fObiTsm2Iz6IHjO58+fb271RtGo6pXAlQAi0jCuByLCvvvuy7x586Y0bMWoDn19faxfv366u28otMJEWwOjqvz6oRfI4CAxh9uf2sPrXuySTMSm3BAVEat4jZpARGhqaiKTyRRd+bmuOzq+bSoukxPR399Pa2vrqDtyMJn9VVddxX333ceKFSs4++yzOfbYY0kmk+ZyZ9QFmUyGp556iuuvv5777ruP5cuX8853vpMjjjiioSOjGmwCloX+L/WXGXjz53Z0dJhgqwHS6TRbt26dcmduMUz6hhSRq4DXAdtV9VB/2RzgR8ByYD3wFlXdI16r4gq8OS4GgHeo6l9KnmqjZJx57EpaEw4jGTjxkKU0xT33xlQqxbZt24o6RtAInj17djmTWvdYWascsViMZDI5pQrQdd2i5y0shrDrZV9fH7fccgvXX389u3btAuDRRx/liSee4OCDD+ass87i2GOPteisJcDKWXXIZDI8/fTTXH/99fzhD39gcHAQgMcee4yPfexjnHrqqVxwwQUsWrTIOigak/uBNSKyAk+snQOcV90kRYNEIsH8+fNt+EkNoKrs3r27pG2FMMV0a10NfA1v1viAS4Hfqupn/bCsl+LNnn46sMb/HAv8D3l8ko1oICLst6CTi159WN71rusWfaxEImG+1jPnaqysVYx4PE5TU9Ok49DKydDQEI8++ihXX301a9euHVfmMpkMf/3rX3n88cc5+OCDOfvssznmmGPM8jYzrsbKWcUIxNoNN9zAPffcMyrWwgwNDfHLX/6S+++/n/PPP59XvvKVdT0ZuTEeVU2LyMXAr4AYcJWqPlblZEWCuXPnmidTDaCqDA8Ps3PnzrIFDZtUtKnqXSKyPGfxGcCp/u/vAnfgVXBnANeol9p7RaRLRBar6paSpdioCMEExMUgIsyePdteKjPEylrlaW1tJZPJlMWNYSJc12Xjxo3ceOON/P73vyedTk/YQA2Lt0MOOYSzzjqLY445xixv08DKWWUoRqzlsmvXLq644gpuu+023vGOd3DYYYfR1NRUgdQaUUBVbwVurXY6okR7eztz5861DowawHVdtm7dWlYX1uk6kC8MVVpbgSACRb7oP0uAcRVcOALQ0qVLp5kMo1wMDAwwPDw8+YY+k0XCM6bNjMqalbPCBOPImpqaimpQlgJVpaenh1/+8pfcfPPN9Pb2TsnlJZPJ8Mgjj2RZ3l7ykpeY5W3mWJ1WIjKZDM888ww33HADd99995TLlqry6KOP8tGPfpQTTzyRv/3bv2Xfffc11zCj4YjFYixYsMCmUqoBgrp97969ZT3PjEf9qqpOJ4JPOALQkUce2TARgGqFqUwCLCIsXry4zCkyplPWrJxNjIiQTCZxXXdKnRTTYWRkhD//+c9ce+21bNhQMDhUUaTTaR555BEee+wxDj30UM4++2xe/OIXm+WtBFidNj0ymQxr164dFWszHdMxMjLC7373Ox544AHOPfdcTj/9dGbNmlWi1BpG9JkzZw7t7e3WIVcDjIyMsH379rLPpTpd0bYtcBERkcXAdn+5Rf+pE7Zs2TIl0WaWtrJhZa3MBMKt2Emvp4qq8txzz/HTn/6Ue+65Z5wrpuu6OI6DhqbbKJZMJsPDDz/MY489xiGHHDJqebPyOGWsnE2TUou1gObmJM3JZnr27uV///d/ufPOO3nb297G8ccfb5YHo+5pa2tjwYIF1U6GUQSqyo4dO8re8QvTF203ARcAn/W/fx5afrGIXIc3WLvHfP9rk2J9ckUEx3FsjrbyYWWtAgRzppXSF11V6evr4+abb+YXv/gFPT09BbediXADz/L28MMP8+ijj/KiF72Is846a9Rt0igKK2dTJBBrP/3pT/n9739f0mhpC+fP47SXncI+Cxfy7Pr1PPrEUzz99NN88pOf5OSTT+b8889n3333tY4Joy4REZYuXUpLS4uF+I84qkpvby/d3d0VOV8xIf+vxRugPU9ENgIfx6vYfiwiF+JNAvcWf/Nb8UIjr8ULj/zOMqTZmAKqysjICDt27ODpp5/m4YcfHn0JLF++nIMPPphVq1bR3Nw8WgGq6pQq4KamJnPLKgFW1qpLa2sr/f39pNPpGR2nqakJ13W5++67ue6663juueeKsuDNVLiB15B+6KGHePTRR7PcJs3yNoaVs5mRyWR49tlnueGGG0ou1gBWr1jO6a98ObO7ZiEiHHLgASxZtIjevj42bdnKbbfdxkMPPcQb3/hG3vjGN1qUSaPu6OzsJJFImGCrATKZDNu3b69YMLNiokeeW2DVK/Jsq8D7ZpooozSkUin+9Kc/8ZOf/ISnn346b+Xa1NTEUUcdxSWXXDI6z9qOHTvYtGkTjuMUFfa/tbWV9vb2kqe/0bCyVj1EZNTNdyaiLRaLsXPnTr797W/zxz/+kaGhoSntHwi3mZJOp0fF24te9CLOPvtsjj76aLO8YeVsugRiLbCs9ff3l+U88XiMgYEB4jGH1tY2enp6eOqZtbQ0N4+Wjx07dnDVVVdx5513ctFFF3HEEUeQSCTKkh7DqCSJRIKFCxda4J0aQFXZuXNn2d6F+ZhxIBIjmnR3d/ODH/yAG2+8MSt0f9CLH/T8Dw4Ocs8993DFFVfwr//6rziOwyOPPILruixcuJB0Os3IyAipVGo0NHrYahCPx2ltbSWdTluladQ8iURiWnO3qSp79+7lt7/9LTfddBM9PT3jylqxhIXbTC0I6XSaBx98kL/+9a8cdthhXHjhhRx44IE2JsgommC8xne/+13uuOOOsjdQhodHGBgYYGBgwDv3rt1kMpnRsuC6LiIyGqXyYx/7GKeccgpvf/vbWbZsmVndjJpFRJg7d651rtUAqsrg4CC7du2q6HlNtNUh6XSa73znO9xwww151+c2IlWVBx98kGuvvZb999+fdevW0dPTMzr5cFNTE+3t7TiOQywWw3Gc0U88HieZTLJ582ZWrVpVicszjLIhIrS0tOC6btEWt1QqxV/+8heuvfZa1q9fP3qccONxusJtJq6SYdLpNH/5y1946qmnOPnkk3nTm97EypUrTbwZBQl6kX/xi19wyy23sH379sl3KkMaVHVcdNdwmRgeHubXv/41999/P+eddx6vetWr6OzsrHhaDWOmtLa2Mnv27JK9943y4bou27Ztm/Fwiqlioq0OefbZZ/nZz3422iNZqPAHDclMJsO2bdu49tprec973jNqZUin06TTaQYHB3Ech0wmg+M4WccMApEsXrzYXjRGzRM8v+3t7aTTaYaGhnBdN6+bsKqyYcMGfvrTn3L33XeTTqdHy0fu8YLtp0KpXCXD9Pf384tf/IK77rqLU045hTe96U2sWLHCxJsxSiDWfvnLX3LLLbewbdu2ip5/x67dPPHMWpYvWzo6PnTDxk28sHnLaN2Tr57Zs2cPX//610ejTB5zzDH2XBs1QywWY/78+aPv/HB9Ye2q6NHd3U1vb2/Fz2uirQ7p7u6mr6+PWCw2ahnLJei9TKfTZDIZkskkCxcuJBaL0dLSkjUhqqqODrLMbbyKCM3NzaMN3JaWlvJenGGUmaCCjMfjtLe347oug4ODo4PCg0A9t9xyC7feeit79uwZ7byY6HjBvlMhEG6lrrT7+/u59dZbufPOO0fF28qVK20cRQOjquzatWvUslZpsRbQ19/PQ48+zjPr1rNw/jx6+/rYuXvPaKfgRGUhmJj78ssv59RTT+X8889nyZIlddvozX2f1Ot11juBW2Q4qI51gs+chx5+GIAjDj+8ZMcMB/cr95xs+TDRVqcE488CN8Zw4Q8mzg4EWDweR1VZtGgRQ0NDNDc3E4vFioqGE4/HWbZsGSMjI/T29ppoM+qGoMw4jkNTUxOpVIpUKsU999zDTTfdxLp160bXh7ef6FjTecmXcoxbLoF4y7W8mXhrHAKxFljWtm7dWu0kAdA/MMC6Dc+P/p/Ksz88PMyvfvUrHnzwQV73utfxhje8gVmzZtV0Izh4d6jqqBdMUJcHBJ20QbTYWr7eRqK9vZ3Vq1ezd+/erOXl8LYwZk6l5mTLh4m2OiawkOUTX0GAhPALoa2tjaGhIVSVpqamLGtbPpqamli8eDGtra0MDw/T0dFR8mswjGojIsTjcbZs2cI111zDfffdRzqdntBVqxDFRmTNpdRj3HLp6+vj//7v/7jzzjs59dRTOfPMM0281Tmqyu7du/nlL3/JzTffHBmxVkq2b9/OVVddxd133815553HiSeeSDxeW82eQJSNjIxkibV8BONrhoeHSSaTOI5DIpEw8RZhRITZs2dXxdXOmBpBwLFKzcmWj9p6exkzJhBrQUUQFm8tLS2jIcrj8fiEke8SiQSzZs2iqamJgYEBZs+ebXO1GXVH8JL+0Y9+xK233kp3d3dWjzcUbwEIW+6iKNzAE2+33HILd9xxB6eeeipvetObWL58uYm3OiIs1m655Ra2bKn/ucKffvppPvOZz3Dcccdx4YUXsmzZskg/0+Hx5sPDwxMKtXwELt0Azc3NWfOwGtFi1qxZtLe3V8XVzpga6XS6onOy5cNEW50SFlzhRmLuiyFY3tbWRiwWY2hoaHTfQqItHo+PulAODAwgIqxZs6bMV2QYlWV4eJh7772Xq666iueff37cwPCZVLIzFW7lJizeXvayl3HmmWeaeKtxArH2q1/9iptvvrkhxFqYkZER7rrrLh555BHe9KY3ccYZZ9DZ2RkpMaOqpFKp0bHmuVPsTIehoaEsl0kjOiQSiazgI7m0Nzex7/xZPLu1m/Q06gujdARu5PnmO64kJtrqkI6ODhKJxKjP7USNw3CFMDIyMjofzkQv96C3P4gymUgkWLx4sVUIRl3gui7r1q3ju9/9Lvfee+9oAJKAcOTU6VAqi9tM0lAsfX193HzzzVnibb/99jPxVkOoKnv27BkVa5s3b652kqpKd3c33/nOd7jzzjv5u7/7O44++miampqqkpbwlAaZTGZ03GypCSJA21yq0UFEmD9/fsFnr7UpwWuOXs0B+8zle3f+lY27ejFbXGEcEZYtXcLwyAg7du4quTVsYGCg4nOy5cNEWx2yZMkSOjo6pjRQcu7cuVlRIicik8mMTrgtIsybN8/Gsxl1we7du7nlllv48Y9/TF9fX8HtSiWWpmuxq/QA9d7eXm666SZuv/12E281gom1wqgqzz77LP/6r//KqaeeynnnnceKFSsq0vEYdq8eGRlheHh4Wp03Uz1nJpMZHfZgVJ+2traCwXHakgne8bLDaY4JG7fu5OWH7MtvH3uBjbv25jmSAdDR0c4Jx7yEBfPn8uxzG3ju+efBjx45E4JOle3bt1d8TrZ8mGgzAKYUVjwc3EREWLp0qc2HY9Q0qVSK3//+9/zwhz9k7dq1FTnnTKYCgMpa3ALC4u3lL385Z555Jvvuu6+JtwgRiLVf//rX3HTTTWzZssXGyxQgnU5z22238cADD3DWWWfx+te/no6OjrKUp2CKnVQqRSaTGRf5sZw4jlNzAVjqmVgsxoIFC/K2m2a3NXPqIfuxuKuNTTu6iTkODsopB+zDrx4ZYWffUBVSHG1EhDUrV7JsyT4kk00cedihHLXPIrjuxwD815YtfGTxYvqnefw9e/ZEJlCMleI6Q1VHI0BWiqVLl1rvnVGTqCrr1q3jmmuu4Z577ql4T1ophVsl6e3t5ec//zl33nknl112GS95yUsqngYjP7t27eIjH/kIzzzzTLWTUlFiDrS2CP2DylQNV3v27OGb3/wmv//973nb297G8ccfP2ORE7g+Bp2cwVi1ShMEGYvFYlZPR4Q5c+bknR6pNZngzGMPZJ9ZrazfsnN0yiaApniMYw89iNsffY6Bvu4KpzjaJBIJFi+cT1PTWKTUo2742ej6fRYu4F9SKf59iu7Bqsrw8DA7d+6MTMeXibY6Y2RkhOeee65ilUNHRwcLFy6syLkMo1SoKr29vdxwww3cdNNN7Nmzp2ppqUWLG3hTfrziFa/gRS96UcXOaUzOnDlzOPvss/n6179e1dDUlaCzTThkVYJDViVYMj/GwrkxrvhhL09tmF799+STT/KpT32KE044gXe9613Tmpg7cKcaHh5mZGSkao09x3GIxWK0tLRMyZPGKC8tLS3MmTNnXH7Mak1y8sH7smxOO9t29+TUC7CdLh6Pr6Rz//0Yfuw3ZIanazeqP5YsXsR+y5Zl3bOWUJ2+84D96fzT/TB7zpSOq6pVnZMtHyba6oy9e/fS19dXsQHH++67r02obdQUIyMj3H///XzrW99i/fr1kehBqzWLW2trKx/4wAd4xSteYW5XEcNxHF75yleyfPly/v3f/50XXnih2kkqG6efkOQtp7UhMjY+9PUnt7D2B71kpjlMbHh4mN/97nc89NBDnH322bz2ta8t2mVSVRkYGBgN0lVJAktaLBajqakpy/XOBFs0iMViLFu2bFz7rCke46zjDmLZnHZe2LEHxw8GpwAK3dLBemc5wzILmdVJ10F/w/CGP9C/Z1vDBydJJBKsWr4fs2Z1Zi3fcMpJLHj0MQBedOsvuWTlCqD4qXqCjt2odXzZQIQ6QlVJp9PE43GOO+64ohtTBx98MEcddRRdXV10dHTQ0dFBMpks6kVvrpFGrRBEhfzMZz7D5ZdfznPPPRcJwRZmJmUpcMcq9zW1tLTwgQ98gNNOO80EW0RxHIc1a9bw8Y9/nKVLl1Y7OWVjcCjN0IibVW6OOjDBoaviuK47o3Fju3fv5sorr+SDH/wg999//6RWs8CVqpKCLRBpyWSS9vZ22tvbaWlpGQ04Mlkk6GohIutF5K8i8pCI/NlfNkdEfiMiz/jfs6udzlLT1dVFW1vbuOVz2ltYvnA2bp73925m8Vddwx7pAhFAaJq9D/NWvphka+e4YzUa7W2tLF64kFiONXnjS48f/f2fL38ZdyJZAX8mqytTqRTbtm0re5CgqWI1bh0RzMPT19fHwQcfTG9vLw888AD9/f15H85kMsnq1as59dRTWbJkCe3t7Rx66KHEYjEGBwcZHh5m79699PT00NPTQ3d3NwMDAwwMDOA4DitWrLD52YzIEwRmCCbIjsqA4lzCUwFMR3wF+5RzAu7AwnbaaadZ8JGIIyKsWrWKyy+/nMsvv5yNGzdWO0klRxV294yQySTY25diV3eK3d0jDA5lCIpPUCamUx5UlWeeeYbLLruMF7/4xfz93/99wfkKA9FWKeLxOK2trVVxjS4RL1PVnaH/lwK/VdXPisil/v9LqpO00pNMJpkzZ07eKR129PTz64ee5bVHrWb+rHZ6+ofIuMpubecZZz/6nE5EwvksDHcsp2VNHHfdHxjp3VHhq4kOba2tgNI/MEBrS0vejop7+j1X0nC9OlGHRjAnWzBBfZQw0VYHBIOdBwYGiMfj9PT0kEwmOeigg1i2bBl79+5l8+bNWQ/gvHnzWLJkCc3NzXR2dtLX1zcaflZVaW5uprm5ma6urqyHO5hHJhaLMXv2bOtpNyLN0NAQf/zjH/n2t79dM43WoKxN10JQLlfJWbNm8d73vtcEWw0RCLePf/zjfOYzn+G5556rdpJKSioNe3pSPPLkXkZSY+XF6xwvnYhJpVL88Y9/5NFHHx2NMjl79uysOdZGRkYq0iufSCSIx+Mkk0mgJsVaIc4ATvV/fxe4gzoRbcHUSIXmZMuocu8zm+gbGuG0w1Yyb1Y7G3YP8gQr2BubS95nWYTm2UuJLT+a3rV3MzxYeIoa13HJJDIkhutvnr7NW7dx869+w9J99uFFBx3AgvnzaWlOZt3r7r3jp0oI16+5wxP6+/sjMSdbPqzFXeMEgq2/vx/XdZk3bx6rV69m/fr1LFy4kBUrVpDJZOjr68t6SFtaWmhubmZkZIS+vj7mzJnDmjVrxlUAuT3+juOMVhaBeKujSsOoE1SVJ598ku9///vce++9JZ9osxJMd/JtKN5vv1iWLFnCv/7rv7L//vubYKsxRITVq1fzxS9+kS984Qv84Q9/qHaSSsZf12Y48gAhlS7cwVHK+qm3t3d0Yu4LLriAww47rGTHLpZkMjnaWVrDda8CvxYRBf5XVa8EFqrqFn/9ViBvhDMRuQi4qDLJLA3t7e10dnYWzC/Hcci4yiPP72Dznj7OP+lQ5s5fwMjOJlAQZ/x+3pg3ZdaCeRzetIKn161jU3c/bqgojLSMsGvFLnYt30XH9g72+/N+5brEquG6Lt09e+nZ28sz655j8YL5LJg/j5ccefjoNsPDxbssB3OyRbXNYKKtxnFdd1SwgVf4V69ejeu6PPfcc/T29pLJZGhvbx8d/Bq4i6kqiUSCefPmcfjhhxfsBSpEVB9qo3EJXIRvvPFGfv7zn8/YFTIeg1ntDksWxNiz1+WFbZV55gOL93SFW9hVMjjedFmyZAkf//jH83bqGLWBiDBnzhwuueQSPve5z9WNcNu8U3l8XYauNhhJwdAI7Nor7NorOHkaujMl6Ax59tln+eQnP8lxxx3Hm970popNzA3Q39+P4zi0tbXVclTIE1V1k4gsAH4jIk+GV6qq+oJuHL7AuxKg0DZRIpFIFJyTDcbezcH3rt5BfnLvE5z90kM5gqf4a2olg8l5aI61TXHJsJVM56PEkiMcEZ9P5inY1N1H37w+dq7ayd7Fe0eNdCNtI6goojX5vExK4J68/oWNPPf8C6x9bj1/P8H2+dwjg7ZxX19hq2W1mVS0icgy4Bq8Xg8FrlTVK0RkDvAjYDmwHniLqu4R7y5cAbwGGADeoap/KU/yG5vgIc1t1DmOwwEHHMC+++7Lli1b2Lp16zhTb3t7O4sWLWLJkiWjbpFTpUYri0hi5WzmpFIpbr/9dq699toZuYGpKvstjvHyY1o4aHmcfebHaE4Kj65N8Zmr9pKq0FRLMxVuMHNXyXoUbJUoa8FY4Il616vBrFmz6kq4qcKdD7p0tTvs2Sv0D0Ha71cp9X0PBFtQFkdGRrjrrrt4+OGHefWrX80b3vAG2tvby57fYe+a9vZ2oPbqYlXd5H9vF5EbgWOAbSKyWFW3iMhiYHtVE1kili5dSnt7e95O7rzCAdi4u49fP/wsrz1yJUsTPdy+p5ld2oaIM+ZFkdxDYvbjbHSe4A5nPYl9HPbtmss63UJfmz8UxoWujV3MWzeP1t2tSAldhqNIuIzu2Fmce2P4/g8NDbF9+/bIBSgLU4ylLQ18UFX/IiIdwAMi8hvgHeQfNHo6sMb/HAv8j/9tlIDgYQrGluUb1Areg9ja2sqqVatYuXLluIewFFGlzDWypFg5myZBVMhvfetbPPDAAwXLxFSY26m87sRmYrGx5/vA5QmWLojx3ObKWZhLIdwK+e5PRj0KNp+yl7WBgQF++MMfcvzxx3PQQQfR1NQUmXsYCLfPfvaz3HvvvZFuoBRDdy9091bGZTdcBoP83Lt3Lz/5yU/405/+xDnnnMOLX/zi0eiN5SSTybB3716SySTNzc2Reb4mQ0TaAEdVe/3ffwN8ErgJuAD4rP/98+qlsjS0traSTCaLFmxhHnthB919gxy5fD77JDfS13Ygw+4CENDEVpzZTzKU3MjDPU8wN9HFDnc3O1t7AGgaSjB73Rzmrp9bl+PYJiOoNydan3vvM5kM27dvL0n7oZxMKtp8H+Mt/u9eEXkCWELhQaNnANeod8fuFZGuoPek9MlvLFzXJZVKMTIyMqXJs8sV9tfGtpQOK2dTR1Xp7u4enSB7b57BxtNlV0+a3XtTzJ895jLc1ASnHJ3kuc0DJTtPMQQV0GQVUSGmE1XyuOOO4+KLL57W5MJRp1Jlra+vj9tuu40nnniCU045hcWLF0fmXs6aNYuPfvSj/OhHP+K6666bUn3SyExUhtavX88XvvAFXvziF/P2t7+dJUuWlL2OVFWGhoYQEeLxeK10pC4EbvTTGQd+qKq/FJH7gR+LyIXABuAtVUzjjHEch/nz5+d1iwy3ySaKYLhpTx+7+wY44Ki5MDeFu+twIEZ8zhPsiD/OM33PkSbNtpQXhFNcOHnj/rQ/nsQdibEro0RnWujyE76XxZSDcNCv3t5eenp6ypa2UjGlMW0ishw4EriPwoNGlwDh2Tw3+suyKrjwYNJ6nkemFKgqqVSKoaGhyIwjE39+GKP0WDmbnOHhYe677z6+9a1v8cILL5TcWrCzBzZsGqKzLc7wSIaBoQwjKZe5nRkScSrmIhmQG91qOhTrKnncccdx6aWX0tXVNa3z1BLlKmudnd78SarKxo0bueGGGzjkkEM47rjjaPHDUlebjo4OLrjgAuLxON///vdNuE3CZI1syI4yeeaZZ3LaaadVxEU2iAzd3t5eESvfTFDVdcDheZbvAl5R+RSVh66urnHusrkd6BPlU9BJN5jK8OTm3XQsGYZFu9mY2sTzI88xMDQWDbxVWxjZnEIclxWPNXNkayeHzWrjO7t38/DwsD+/W+MQ3LvD/UBBMsEUOqrKyMgI27Ztqwmvg6JFm4i0AzcA/6Sqe3MaEQUHjRYiPJj0yCOPjP6dqgJBOOFUKhW5+SJisZhZ2sqAlbOJUVWefvppfvjDH3L33XeXpRNDREilhQ1bhulsc9CsddW7heFGY7miSjaYYCtbWVu8eHHWvkNDQzzwwAM8//zzvPSlL2XVqlWR6PRKJBK87W1vQ1X5wQ9+UFbh5jS10tS1hFTvNjKDha3iAjSL0CRCT8Qmtp2MoE7s7e3le9/7HnfffTfnnHMORx999GggsHIyMDAwbnJto/Ikk0nmzp1bcH2x+RJst6e3l7Vb1rNnYTeueGWi1WlhefNSZmdm8djjT9O1Bf6hew2vnb+URU1JkiK8pm2IJ4f6GSbekMIt/Duo+wJLeXj9rl27GBoaqngap0NRok1EEniV2w9U9af+4kKDRjcBy0K7L/WXGVNAVUmn0wUnxq42laiAGg0rZ4UJXCF/9rOfceONN5bUFTIfqbSwbhMcsNwlEY9O50QpXSVzK7Vjjz22kQRbVcrajh07uPnmmznwwAM58cQTIxGoJJFI8Pa3vx2gDMJNiLfNoXn+KloXH0SsuYPh3RvY8+gv0czY2JEFsTiHtTRzcLKZObE4a5JNPDk8zKe3b6NWZFs4HwPx9txzz/H5z3+eE044gTe/+c2sXr0aESGdTpdlTrcgmnQsFqv16JI1i4TmZMv3TEyVdFOap095Bvzdu/o6Wd6yhH1mLfKmYHpukPl/7OLvFy/nuKXzskKNHNHWwQkjA9zdP8SI0nDCLUy47gwI5mTbvXt3FVM2NYqJHinAt4EnVPVLoVWFBo3eBFwsItfhDdbuaaRxNqUgcIccGBiIpGADC0JSaqycFWZoaIg77riD66+/nrVr11borMLajUo641nXegdg2y6Xp593yVS5FVkKV8mwxS0Wi3HOOedwzjnn0NHRUZI0RplqlzXXdXn88cd5/vnnOfbYYzn00EOnPN1KqQmE23777ccVV1xRmrEdTpzO1SfQuvAAJD7WgE3O2Y/2fY+i97k/gW/HfmV7O38/x7NMBNvFRehwnJqztkF2Gc1kMtx111089thjvP71r+f1r389c+bMoa+vr2zDHYK5WWOxGK2treYVU0E6OjrGdcbMZOqV+EicORvm4GQc5j43l7mpVlau6qR3aYa+pmFOWgdv2v8w5jQ1ITnnmJdo4rjmNh4f7GdHxiGDQyknna818gUf2bp1a2SGHRVDMZa2E4Dzgb+KyEP+so/iVWz/v703j5Oiuvf+36d67+melWFmgBl2QXBDEEQM7mKUaOIWCe4ajLjkQXHNjcTkucm9Se4Tc+99bu4vebyJ0RijGNEYxURQUQTCIoqyw7ANDLOvPb2f3x/d1fYMM8MsvVT3nPfr1TPd1dVV51TVOVWf8/2e77e7SaNvEQmNvJdIeOQ7ElngbEefWGxEU60eoUqf8KxIKKqddSEcDvPFF1/w4osvsmHDhqSMTPdGQ4vgjTVBGlsltY0SjzcSYtwIxE+gHgj678xmMwsXLuSWW24ZStZzQ7S1trY2Vq9eTWVlJbNmzUp70BeLxcJFF12E3W7nX/7lXwZvzRYCzVnUSbDp5JSfRZgaNGc1rTs6aA+FkIAW/8BpMnOqzc46T3s3m86MB8/4cjY0NPDcc8+xZs0aFixYwLnnnpvUfYfDYcLhMD6fL6OiS2YyZrOZ4uLiTiK5L3MhT0b51oihX0pJqwyyZ1czp3hykfhpOB6GYZwg2ABMQjDd4eaTjja2BLw0BCWBLM3T1l/0nGweT2oDiw2WvkSP/IiepfkJk0ajEbbuG2S5hizBYNCQgk0IgdVqVe4WSUK1sy/RO9MXX3yRlStX0tbWhqZpjB87mqqj1Xh9qYmHFQzBJ7tCJHpk0i4EfikH7fYlhBhUKgBN07j22mu56aabMJv7FZMqozFSW5NSsn//fqqqqpg+fTrTpk1La6ASIQSzZ8+O5XIblHALBfA2VmHJKcRsjYgGGQ7jbW+mvfE45iIn+afnYB1uZtcmP95QCIfJhASCUtIWCFBhMvFxOBybV5qp87TiB1f27dvHT37yE6ZPn85NN93E2LFjk2oJ058n1P07+RQVFXUSyMm6Xtv9QbZX1mOVATSrhf2+Dgot1m47tQKLhfm5RextPEYzEmMHtE8dXq+X2tpaw3qz9cTQuVMbHD3oiNECjuhomqY6fEVS0aM4vf/++7zwwgscPhwJ2Ge1Wjhv5gzmzDyHqmPVfLT+H+w7cDBFne3Ar3fdf36YycQEm41yi5XJNhvjrTZebW7iL62JmZc3EOFmMpm47rrruOGGGwgGgycNUKJILj6fj48//phdu3Yxd+5cxo0blzaXNiEE5513XkKEW6i9Hl97M0IIvG1NdLTU4/e2gZRoLgmawDnOhslhYeP2duwNAbyhMK3BAN5QCG+4s9tSX1NWGIX4Piq+jQaDQTZs2MCOHTu47LLL+MY3voHb7U5a3bxeLz6fD4fDYaicgdlEQUEBxcXFsc99Ces/EPRt+cIQwEJdMMQGTyunOnJwmzt7S8jo4GC+2YJDWvCGA0PZOzJGKBTi+PHjhs/J1h1KtBkEIwcdAYbUSLwi9YRCIfbt28evf/1rtm7dGguG4Mpx8rV5lzFhXGQ0enT5KMpKhvP5zl289+HHtLWf6DplBPSAH2Ypeap0BFMdjshsgugNd57bzd/bWvEOsr3r2+uPcHO5XCxYsIArrrgCi8VCKBSitbWVnJwc1c7TTH19PX/5y1+YNGkSs2fPxuVyoWlayt3RdeH2/e9/n1/96lfs37+/39swmzTKbD5aQj6ajx/C29Z5sn+gxYoMCYRJ0lgk+cOwFi46GsYU575lERKHFsYTFmTS02ZPYq0rLS0t/PnPf2bDhg3cfPPNzJgxI2nzG/WpFyaTSc1JTzAmkymWky0UCiVtwCU+mIYeRbglKNjU0c4cXwdnmcyxVhJCUuv3s8PbwQtNTezz+5FCdOtGOZSQUtLS0pL0YGbJQt2hDYCUEp/PlxbB1uELEgiFyLFb0UTPjVmNzimSgZSSuro6VqxYwYoVK2iPijApJcMKC7nx61+jeFhRp2vPYrFQMWIE40ZX8Nn2Hekqep8wSUlDRwdaF5e3U6w2JlhtfO6LuC6ZHHmEgz5kYOCu0X0Rbi6XiwcffJBZs2Z1Ko9u5bfb7YbP85TtBINBvvjiCw4cOEBJSQmFhYW43W7MZjPhcJiCggI0TaOwsJCcnJykuWAJIZgxYwZPP/00Tz/9dJ+DAFlMGqeWFTCmOJfCHDvbfY185nXErSFBgDknCFLS0eij7Xg7vkCYsLBgit4GpZRIwEaQDmlGGvya7Hr/7usgip7LT0/MfdNNNzFmzJikPPiHw2E8Hg85OTlqXnoCyc/Pjw2AJcO6Fk/8fOZIsnfBgaDk47Z2TrE5cZpMNIaCbPe283ZrEzs7AtSFIi7Gxm5BqSEQCGRMTrbuUKItzeguYekw00opWbf9KKs+OUyB20ZZYQ75Lhujit2cNmYYJu3LUXwVfUqRaHw+H2vWrOH3v/99zBUynsgNKRxLCi2EIBQK0dDYSGNjEyPLSti5Zy9+A7o46DfWHAQtPi8BGcYqIg9JUko04CKXi102B47hp+EoPYVAaw1N298lHOi/i3RfUgH0JNh0gsEgHo8Hl8ulHugMQHt7O/v376e6upqSkpJYUAmr1Robzc/Pz2fy5MmMGjUqKVZSIQSjRo1i2bJlfRZuTquZ00cVYrdGXLVOtTdQ5yhmXxtoQmLOa8Ne0khOaZCWo214myNzVCXQgaRZhmklTEMwwP5AkBZhQphMCIM+cvbVqnYygsEg69evZ9u2bVxzzTXMmzeP/Pz8hD/8q/t5YrHb7RQVFcUGT1I14NV5P4L3PO1U2OyYCLHB28qeDg+1QYEvLmJk/LU6FAfmwuEwtbW1+FI0Lz4ZDGnRps8jSyehUIj29va0lCMclmw/1EBTu4+mdh+V1RFzsdsG31s4G6c94qZht9uRUmZUWFQdNQ/PeITDYXbu3MmLL77Ixx9/3O21L4SgsbmJfZUH8Xg6GDasCLvNzvGamli0p1yXi2GFBRw9XnPC742AECA0aAkFqfX5KLTa8ASDtAWDVFuc7CifxrD80SAiE9e1wtHkn3opTTsGLtz0/12Pqcvl4oEHHuhRsOnowiCdwTAUnfF4PLS0tOB2u/F4PLS2tsbOz5EjR6isrKSiooILLrggZnlLJPHC7Qc/+AH79u3rdf12X4Ca5nbKh+VFUkogmZbTQLgd3DRQO6oZvy2EpyEUE2wAQYvGRzY/jU1eOkIykhDYZAWDBh9JlFjrSnt7Oy+++CLr1q3juuuu47zzzkuYIDeZTGlPL5FNaJrWKSdbsq1s3aEP1NWGwvxHQy050k9YQFtIIyC1E8xr8YN7RmxXyULPydbY2JjuogyKIS3a9u3bx3/+53+mdTJiKBQakBgaaHLdTpjtdJTOAdF58mrd8aM8/fQP0KIxuywWS8Y27vnz53PFFVdkbPmzCSklzc3NvPTSS7z11lsn9SkPBkPsqawkPy+XqqPHsFossbluUkq8Ph9GdnAwIbGYwzQQZk9rGyX2IB2hIDW58OeCybTkT0TTvrRoCSGwFY0mb/JFNH7xDoQT0y+UlJRw5513cu655/apHfh8PiwWi3KTNBAejwe3243FYsHr9eLxeDCbzVgsFqSU7N27l/r6+lhAi0QTL9x+/vOfs23bth7vP6GwpNHjo9DrI8duo8MXoLGxjgKPh1A4THmtg0MjfATll9f3sLCdCk8OlcFGGkMmpGY27ICbXm993mqyqKys5JlnnuHjjz/mpptuYvTo0YM6HkII7HZ7Rt/PjUZ8TrZ0CLZ4hBAEJDRjgVCYIKLHZNpDTbBBZuZk644hK9pCoRAvv/wyW7duTcv+9c5+MOG6u26r6/uTYcktozDfh2YOYzJb0IdkQq3H2bNrZ6cJr5nawFtaWpg9ezb5+fnpLsqQRU8W/+GHH/L8889z4MCBPv/2WHUNBw4doXzUiIhbYfQ6bG1rZ+3GTTQ1G3cycUgKmv0hjllDnCUgLCVH8iVvzTZxZEcLpoZqXEUjTmhb1oJybEVj8dUOLJF4fCqAMWPG8Oijj/Y7B1h7ezs2m03ldzIIwWAw5iYMX7apQCAQc2Wtq6tjzZo1zJs3L2mukuXl5fz4xz/mmWeeYdWqVd3ev0YNy+XC08ex48BRjje20tzuIRT+8r6U77VQ0OJku70Jv/RT0GbBXqextaEeTyAMJkunfG1GIV6sxf9PJsFgkI8//pjt27czb948rr76alwuV7/apBACp9MZG4RR7TkxWK3WWPARIwg2nbDUBp1OZqA47WCzCBpbjTWcKqWkvr4+43KydceQFW179+7lo48+SmsZBtrpd+0g4iemdu00ervBBPxe6g5uByEieXQ0E1ZHDt7mxk4hwDO5kz9y5Ajvvvsu1113XUbXI1MJh8NUVlbym9/8hs2bN/fbqu3z+9n06WccPFLFmVNPpbAgn8bGJv6x9VNDCzYAhMArrewLBpGaRgjJPluA6sYgIa8Jv7cek8WGI7cIABkO4fe2E/C2Ew4PrG+Ib+8DFWz67/X5U2p+W/rRrU7xwk0nfuR49+7dTJo0ifHjxyctOElOTg5LliwhFAqxevXqE4RbVX0LzR0Byory+Gz/kU6CzaRpjCjKw2W30XLYQ1udRmNzgGOBiNXciPebVFnWeqOpqYmXX36ZdevWccsttzBt2rReLWZCCGw2GyaTKRZ91GjHNZPRNI3hw4djs9li14WR5gn25ImVLHEpBOS7BfPPt1NeauL/rfBQ05DeqUc6Uko6Ojqor69Pd1ESwpAUbaFQiFdffTVtqltv5AO5AfRm+eppWU+mcKlBOByJdhTwRqL2+T0tnF2sccyXy7Gmtn6Xz2hIKXn99de59NJLlbUthegjW88//zyrVq2irW3g15KUkpq6Otas28CoEWUcPnoUv994wUe6RQhqw4I/tzfispg5GJQEvQ5Cfg2kpK3haCSSnsWBz9OCDIeQMkyoo39+910fLEePHj1gwRa/zY6OjqTMk1L0D6vVipSSYDAYcxHujmAwyNq1axk5ciQOh6PH9fpL/D1L3/+3v/1t/H4/H374Yad7WTAU5rUNO/nO5Wez72gtXn8Qs0ljbOkwTikvIRCSrN5WyWeH6wmEovcmYbwwI0YQazp6+zt06BA//elPOe2007jllls65fPTLewmkwmLxaLcIJOI2+1m+PDh+P3+TsuN4nbYVbT1NNCfCHIcgrMnW5h1mpWzJkWikJ93hp+/rPETMoBuC4fDGZuTrTuGpGirqalhw4YNadn3YF0rBtLYuvuNc5wN0/A8Wg52Xq4JmFziYn9l0BCdTyI4cuQIW7Zs4aKLLsqaOhkVPRfQ+++/z3PPPUd1dXXCtu3z+9l34ODJVzQYEo1tgTBTCm2UlebQbGrBUniQQN1YkGY8TbVYHV/OQzIhsQhJX+JbdX2wFEIwZcoU7rvvvkEJNh3dBU89AKYXPRhUXx48amtr2b17N2ecccagzln8tRUMBuno6OhkVXM4HNx7773Y7Xbee++9TmU73tzORzuPcNb4co7UNjJlTBmN7V7e/aySL6oa8PgDgHGtavp7I4i1+GMkhCAYDPLJJ5+wZ88eLr/8cm644QZKSko6RYU02nHNJkwmE4WFhT0KtnQLN70MPVn+ElU2mwVKh5m4/Fwb555upSD3S4+Mr0yzs21PgP1HJelsQlJKmpqaaG1tTV8hEsyQE21SSt555x2am5vTVoaBzmNL5CiJyaHhGCEJhdoI+0wE2iyEAiasYUnQ66W1w3/yjWQIUkreeOMN5syZg81mS3dxspZQKMTOnTv5n//5H7Zu3ZrxE34ThhCEpcZxT5AZAQdFVh917npkwEGwaSQC/UYPbs3PRFsT1jFuPtrdgjfQ8zHsTrCdf/75LF68GKfTmbCbc0dHhwpKkkYsFgs2mw2/39+niMdSSnbu3MnUqVN7ndsWf/3EiwN9eTAYxOv1EgqFehQvOTk53HvvvZSXl/P73/++k3Bbu/MwU8uLKcrP5ZV1O9h7vJlAbOjdWNeSkaxq0Fmsxbe7+HPl8Xh4/fXX2bRpE3fddRfnnXcedrs9XUUeMuTn53drxY6fUmIE4da1TInEZILzp9m4+gI7ZcPMmE2d9zu6zMLXLnDyq1fa8frTcyz0Qa7a2lpDtOlEMeREW21tLW+99VZa9j3YCyfR/v72Qh/2Qh8yDDIskK0mij8FGc4jnEUXOcDnn3/Opk2bOO+889TDZ4KRUlJTU8Nrr73G66+/TkdH/8PVZz1CUNvmZ+fees4+ZTi7bM0czK/C7rHi18aQa/IzwdbEaGsrVhECWw7njB3O2j3H6Dq9racHTF2w5eTkJLTo4XCYQCCgBjzSRG5uLpqm0dbWFgtI0pWuD2l1dXX4fL4TRFv8tePz+WJC0Gw2Y7PZMJvNBAKBmFjrCyaTifnz5wN0Em7eQJBnV32CPxgiGJYJv38NFqNZ1YCTWsq6W15VVcU///M/c+6557Jw4UJOPfVUQx3nbMJutzNs2LAerVjJEkkDJVnlsJrh9AlmRhSbMZtO3IemwTlTbGw5zc+HW/1psbZJKTl+/HhG52TrjiEl2qSUvP3229TUpC+v02CtbIlAIsEad9MSIEwSZ67GnEmlbNxlzLxXgyEYDLJ8+XJmzJihHj4ThJ4Y/t133+WPf/wjR44cSXeRDM+BhnZch5o4a0wxDs2ET1YTDJuZ5pLYNRmL0CwljBnm5lB9Kwfr26LLuhdr8Ra2RAs2nXA4nPbR46GI0+nE6XTi8/kIhUInuEcKIbBYLLFgMfo6HR0dHDx4kClTpgCdrWc+n49gMNjpGgoEAjGRNpB7VE/Crd13ojunEa4ho1nW4kXAQI5PKBRi7dq1fPrpp1x99dVcd911FBYWGuJYZwuaplFcXHzSyKxDIYG11wdbdgQYN9LMyOFmItNSO7vxOuwwf66DvYeDHK0Lk0rrupSS1tZWmpqaUrbPVDGkRFtdXR1//etf07Lvwd4cEj1KafEGmP1ZmC9sXg6ZIskYHVio87vYWVWXsP0YiW3btrFlyxZmz56d7qJkPHqC7N/97nds3rxZuUL2ESnhi6ONOC0mzqwYzt5wDV7/Ydrb8rDn5iBlpK9o6/DR5vXR5vX3+oCpaRpz5sxJqmCDzrnbFKnBYrGQn5+PlJKWlpYTApBYLBYcDkdsvmEwGMTv98dyf+7cuZNJkyYB4Pf7CQQCMfHdHYNNEK0LNyklL774Il6vt9P36Rb9RrOsdTdfbbC0tbXFEnMvWLCAiy66SLk2J4jc3NxYTraTYZT5bclCAh9u9RMMSW6/2smw/BMt+sFQJPx/vltQVZtaC2QwGOT48eMJTXpvFIbMHVhKyaZNm9JiZRtMtEhI/IVuEYJvefL5hjcPjxZmjbWN921tHLNKPMEgnm5GR7OBYDDIu+++y8yZM1UY8wEipaSxsZEXX3yRd955J6sm+KaKsIStRxo4bewIcuxWvP4AxxtbEALsVgvtHT4CutUj2mV0d/OxWCwsXLiQK6+8MulzWXR3OhU6vG/k5+czcuRIjh07NqAHB4vFwrBhwzCbzTQ1NREKhWKiTc+7lZeXh9lsjs3dCIfDsRDvoVCIgwcPUllZSXFxccoeXkwmE1/72tcYPXo0v/zlL2loaOj0fbrcx+Lvv0YSa8k6DpWVlfzsZz9j9erVfPvb32bs2LEpD0kvhPgfYD5QI6U8LbqsEPgTMAY4ANwopWwUkQPxS+BKwAPcLqXcktIC94LeHvtD1gs3CTsPBDlwNERhrhn9kSosJU0tYd752MOqjV7qmsKEw6BpqTkWUkrq6uqydpqGcRJLJJmOjg5ef/31lO83ETeKhFrZpGSOI4drtDxMQuCWJq7y5fHPLWU80lJCa6uP4ABzRGUC69evZ//+/ekuRkYSDod5//33eeCBB3j11VeVYBsEvmCYv27Zy5Sxo8jLcURuNM1tNLV58Edd1/JyHFwxbQJaN03fYrFw8803c80116Qs+IDRRi2llFRVVaW7GN1it9u5/vrr+drXvsakSZOwWCx9/q2etNdqtdLS0hKznAGYzWZKSkoYOXIkbrc7ZmXTw73H5+QKBoPs3r075VZwk8nEWWedxUMPPURBQUGn71IpnHShFg6HYxbGdAq2+HQ9qZjfFwgEWL9+PUuWLOG3v/1tOh5ifwdc0WXZ48AqKeVEYFX0M8BXgYnR1yLgVykq40kRQsRysvXnnPUlaFCm09QqWfGel2N1QcJS4g9Itu7084s/NLPi/UiuNv0QpKrNt7e3nzBYlE0MCdEmpWTt2rXs3r07bfsfKAmdyyYlk202HhhWfIKJ1SwF/gYPxxozPzdbb7S3t/PnP/9ZufMNkN27d3P06NG0j1ZnA9XNHlZs2sPk0WVYzKaYx7/FbGJ0SRGTR5fhCwRPmAmgC7arr746pRbjk+UISyVSSg4fPswbb7yR7qL0iNVq5ZRTTmH+/PnceOONTJ48udf5tEII3G43JSUlWCwWWltb8Xg8tLe3EwqFcLvdjB8/npKSEoQQ1NXVsX37dtavX8/KlSv529/+xnvvvcfu3bupq6sjEAhQWVmZlhFnIQSnn346Dz/8MIWFhZ2+S/aDbLw4680dNFXogjr+fyppaWlh69atKRcQUso1QNen52uA56LvnwO+Hrf89zLCeiBfCFGWkoKehPz8fMrKygadPiPd12EyCEvYfSjI39Z1sP+IjxffbuU//9TCZ3sCdHSJ/5GKAZtwOExNTY1h7lPJYEi4R3Z0dPDqq6+mo9My3Fw2dzhMjacdq8OJO+rrLqXkkMdDZVsbTWS/mFmzZg3XXXcdEyZMSHdRMgpN07j11ls5evQoH3zwQbqLkxXsq25kU66T808bz6d7D1NSkMvEUSUcqmvht+99yoGaZuJ7D5vNxoIFC1Iu2HSM8OAhpaStrY3Vq1cPKml7qtA0jREjRlBaWsq+fftYv349dXV1sYEjk8mEw+HA7XZjs9kIBALU19cTDocJhUJomkZpaSklJSUcO3aMDz/8kL1799LQ0BCL/qiP6ksp2bVrF0IIXC4Xp556KmeccQZTp05NuVjQhdtDDz3EM888Q11d57nSiXSVMtp8NUiNG2RfGDlyJI8//nhS57z2gxIp5bHo+2qgJPp+JHA4br0j0WXH6IIQYhERa1zSMZlMFBQUnDA/sz+Ew2HDRU5NJMEQfPSpn627/Rw+HqK38fBwOJw0N1196kYm3BMGw5AQbevWrUu5lS1Rgi3RdARDfN7czK6WFkY5nUx0udGEYFtzE+3BIM2h7JzPFo9ubVu6dGnK/fwzHYfDwT333MOBAwc4eDDzEl0bDQms31NFQY6dC86cRF1rB7//4DP2HGs4wU25oKCABx98kLPOOittczK7DnzFiwWdZM17k1LS0dFBVVUVGzdupLa2NuH7SCaapjF69Gja2tqoqanB5/PFHuhMJhOhUIjm5mba2tpiER4dDgejR4+mqamJP/7xj+zbt69T5Meu/Vc4HCYYDBIKhWhqamL9+vW4XK5+u2gmCl24/ehHP+Lf//3f2bFjR6ycieh7jRYFEgYfCTKROBwO7r//fkaMGJHWcnSHlFIKIfp90qSUvwZ+DTCQ3/cVIQQVFRU4nc5BbyvbI0o2NEvqm9KXSFufc51tOdm6I+tFWzAYZPXq1RnpW5yU0Zno5gJSUtnezhFPBzaThicUZK8M4cvu6z3Gxo0bqa+vp7i4ON1FyTjKyspYvHgxP/rRj7J+VCsVhCW8u+0AB2qb2HOsgQ7/ia4d+fn5PPTQQ5xxxhlpvenriZbD4TAHDx5k3bp1rF27Fq/XG+uvpk6dytixY5kxYwalpaWDEnHxQm3v3r0cOnSI1tbWjL0xW61WTj/9dCorKzl48CDt7e2Ew2FaWloIBAL4fL5YeP+SkhLKy8v56KOPWLNmTSc3x56Op6ZpWK3WmHtQKBTio48+4oYbbuCUU05JSR27IoRg5MiRLF26lJ/97Gfs3LkTGFxQEiXWTo6madx4443MnDnTEOWJclwIUSalPBZ1f9Qjw1UB5XHrjYouSxt2uz0p1slsDEzSNU9kqpFSUltbi9/vT8v+U8lJRZsQwg6sAWzR9ZdLKZcJIcYCLwFFwGbgFimlXwhhA34PTAfqgW9KKQ8kqfy9IqVk+/btbN68OeX7HezNJFkWIA9hWmUYF5EHrIAM4w+GqCfMFzKIlAKyrEPpjtraWlauXMnNN99siA40k9qZEIJzzjmHG2+8kd///vdZ7T+eKryBINsOdT9KWFBQwJIlS9Iu2CBiRWtvb+f555/nlVdeoaOj44TBpR07dsTc86ZPn868efM455xzcDgcfdqHHg2xtraWPXv2sGvXroQKtXS3NbPZzIQJEyguLqaqqoovvviCuro6pJSEQiEsFgsTJ06kuLiYP/zhD2zfvv2E/Gy91A0pZSw1gy56X3vtNR555JG0ehYMGzaMRx55JCbcBiLajCjWUhVYpL/MnDmTG2+80WiRkt8AbgP+Jfr/9bjl9wshXgJmAc1xbpQpR8/JlmgXXiklmqZlpXCDL/ufnr5LVp1bWlqyMidbd/SlB/cBF0spzwTOAq4QQpwL/CvwCynlBKARuCu6/l1AY3T5L6LrpYVQKMTy5csH5Y/cXxI52TLRQUgEcDzsY1W4nU+lj1YZcWsKAJ+LAB2SISHYdP76179SX1+f7mLoZFQ70zQtlgdIkRi66zPy8/NZsmQJZ555piFu8i0tLfzHf/wHv/3tb/F6vbHACt1FxWttbeX999/nn/7pn1i2bBmff/55j/2ilJJgMEhVVRUffvghL774In/605/YuHEjLS0tiX5AT3tbE0KQn5/PlClTuPLKK5k3bx4lJSXk5eUxefJkioqKeOGFF9i+fXvMhbI/24aIwDabzWiaFptHl06EEBQXF7N06VImT54cW94XARY/EGqE4CLwZYCRdAQXORkVFRUsXbo0rfPYhBB/BNYBk4QQR4QQdxERa5cJIfYAl0Y/A7wF7Af2Ar8BFqehyDHy8vJwuVxJOa+615cRruFE050w6xqAJ9HPtX6/n9ra2oz0phsIJxVt0Wg+ug+UJfqSwMXA8ujyrlGA9OhAy4FLRJp6tB07drBhw4aU73ewF0+yRkPDUtIagKPeMP/welnhaWa938M/Ah5qQyEC0lg3nmRTXV3NypUrDdF5ZmI7M5vN3HvvvYwfPz6Vu81KurvZTZo0ie9///uGEWxSSl5++WWWL1/ep5uwXqdQKMTatWt5+OGH+d3vfhdzYdEtS/X19WzZsoXly5fzpz/9iQ0bNlBbW5u0CK9GamvxAUOuu+46zjzzTPLy8nj77bfZsWNHbER+IPeE+IellpYW3n//fUP0dcXFxTz22GNcdtllvY7Mxws1I4m1eKFmhHbZFafTycMPP0xRUVFayyGlXCClLJNSWqSUo6SUz0op66WUl0gpJ0opL5VSNkTXlVLK+6SU46WUp0spN6Wr3FarlWHDhiXVKp2Nwi3+ftBdtNRktZf6+no8Hk/Ct2tU+nRVCiFMQoitRPyP/w7sA5qklLpflB7pB+KiAEW/bybibtJ1m4uEEJuEEJuSMQIYDAZZvnw5Pp/v5CsniEQ2wESPRsReJgshkxWfsNKk2dgUhk+DUOv/MpHvUOLNN980jLUt09qZECLmuud2uxO67aFIfJufNGkSjzzyCBMnTjTMg2FbWxsrV67sdDPuS9n09drb23nuuef4zW9+Q1VVFZ988gmvvvoqf/jDH1i9ejWHDx9OWSoOo7U1IQR2u51JkyZx+PBhNm7cmJAgVrrACIfDfPDBB4ZIOCuEoKioiEWLFjFv3rxO82G6Ti2Iz7GWTuKtavpno7TLeIQQ3HHHHZx++umGLJ/REUIwbty4PrtyD4ZEemUZhfh2kWyxJodATrbu6JNok1KGpJRnEZkcOhOY3Psv+rTNX0spZ0gpZ/Q303xfOHLkCBs3bkz4dnsi/iYzGFI250BE564JDUxmMFmGlGukTnV1NR9//LEhOs5MbGdCCKZMmcKdd95ptLkTGYkQIibYjBQkx+/388ILL3Ds2LEB34RtNhulpaXs3buXP/7xj6xatYqDBw+mdGBNx6htzeVyUVVV1e2E+oG2L92atXfvXvbs2WOIvg4i18Mdd9zBvHnzYst0i5r+SjddByiMKtZ0Lr74YubPn6+iIg+Q3NxczGZzSq49I83LTDSpaCuhUCjrc7J1R79atpSyCXgPmE0k+aEeyCQ+0k8sClD0+zwik7dThpSSlStXptxkOtgGmAyfX317PTUgI/rjp5q33347pfMeT0amtDMdTdP46le/yuWXXz7kr6XBIKKRFx9//HGGDx9uiGMpo4Esnn32WZYvXx6zevT1gcNsNlNaWsr06dO54IILOPvssxkxYkTCUqEM9hgZra2FQiH27t17wvLB1lMIgd/v5+233x7UdhKN3W7vJNyM8hDbnVgzOhMnTuTuu+9OiZUoG7FarUyYMCFpKUu6I97aZpRrPxOQ0Zxsra2t6S5KyjmpaBNCFAsh8qPvHcBlwA4iN7rro6t1jQJ0W/T99cBqmeKr8dixY7zzzjsp21+izNzJ7Cj0G0+yJoRmMrt372bdunVp7TQzsZ3FY7fbuffeezsFGFD0HU3TOP/881m2bBnjxo0zxEh5KBRiy5YtPPzww7z66qt9Fmxms5mioiKmTp3K3LlzOeeccxg5ciQOhyMhfU4C+lnDtrWqqioOHDjQ4/f9tbZ1dTnctGkTR48eHWQpE4su3K677jrsdntay5KJYg0iFqIlS5ZQWlqa7qJkJEJEcrLp7tmpPPdGcP/NNLxeb8bl6UwUfcnTVgY8J4QwERF5L0sp3xRCbAdeEkL8b+AT4Nno+s8Czwsh9gINwE1JKHePSCn5y1/+QmNjYyp3O2hzeiqEVHcj1JlyU0omepTR2bNnp3OUMqPaWXe43W4efvhhli5dOmTC7yYCIQRXXXUVixcvxuFwIKXEarWmzforpaS1tZWXXnqJFStWxKJE6gmgdRe2eGGpaRo5OTmMGDGCESNGkJOTkzR3WT1k9iAwZFuTUrJly5YePUR6C9rR3bb0//G/aWhoYNWqVdxyyy2xbRoBu93OwoULKS0t5Te/+U3Kr/3u5uJkCiaTibvvvptTTz0148puFJxOJ7m5uZ3m1OrXRKpcJdW56xvhcJja2to+p0HJNk4q2qSUnwHTulm+n8hcgK7LvcANCSndAEiHlS0RoySpbLCqcziRXbt2sX79ei688MK0HJ9Ma2fdIYRg/Pjx3Hvvvfz0pz9NWVCJTEYIwfz582OCTV9ms9nw+XwpH4ENBoNs3bqVF154gcrKSgKBwAmWh1AoFAtD73a7KSkpobS0NDYfJNntZ7DHxKhtTUrJZ599dtL19MAiJ6O7IB5CCN58803OOOMMysvLycvLS6k7WG9omsYll1wCkBLh1p01xQjHob/MmzePr371qxlZdiNgNpsZM2bMCYINUuuu23Vf6nyeiJSS5uZmmpub012UtNEXS1vGIKVk7dq1KYsm05+5Hb2RqSN82UQoFOKdd95h7ty5KqDGIBBCcPHFF3PgwAFeeukl5fbRC10tbF2/s1qt+P3+lBxDKSUej4fNmzezf/9+pk+fzowZM6irq6OyspLKykr8fj9CCHJychg2bBjjx48nPz8/6UItftv6sdCtTtnUZ+rzNHqjL/XV70m6YOsq8Gpqanj++ee59dZbaWhoYMSIETidTkMcy3jh9utf/zopQWoy2arWlWnTpnH33XdjsVjSXZSMpbCwEKfT2W3wH+ifhXsw6O3WCK7xRiUQCFBTU2OIIEXpIqtEW1tbG2+99VZK95koK1um3zyyga1bt7Jz506mTp2a7qJkNBaLhW9961vs2bOHTZvSlm7H0Njtdq699lpuueWWbl1yhRA4HA40TUt6mPZgMMjevXv5/PPP8fl8uFwuLBYLmqZRVFTE5MmTOXbsGJ988glFRUUUFRVhs9mS1mf1tt1seuAeDD1Z27qGyu9qadPfb9u2je3btzN16lQOHz5MeXm54YSbxWLh+eefT9jclfjrxgj1HCzDhw9n8eLFFBQUpLsoGYvNZqOwsLBHwQapt7rpg1HxA1SKyHGpr683VNC4dJA1kl5Kyfvvv9/rJO5E7y/T3CIVveP1enn11VeHXAjZZOB2u7nvvvsYPnx4uotiOOx2O/fdd99JI73p1rZk9RFSSpqamvjggw/YuHEjQkTyZ+Xm5uJ0OrFYLAQCgdj8xDPOOIORI0dit9sTWqZ418vuttv1+3gLWzb1n5qm9SmQT0/HqjvB1tOItN/v529/+xter5dAIEBVVZWh3Jk1TeOCCy7gscceo6SkJCHb6xqIK5Ox2WzceeedTJgwISvqkw6EEJSUlGC1WntdR/+fyoiSKjjJibS1tZGMnM6ZRtaItra2Nl577bWUmrGVlS37WLduHbt37053MbKCMWPGcO+992Kz2dJdFMOgC7arrrqqT24wQgicTmfCXXb9fj+ffvopb731FocOHSInJweXy4XZbMbv91NXV0d1dTU1NTW0tbUl/IG+PyKtp/Wzrd8UQnDGGWf0+Z6gB4eJtwR0FWxd57PFf66srGTt2rVIKfH5fFRXVxvqQVEIwcSJE3nkkUcGJNx0kRafEDsbEELw1a9+lcsuuyxr6pQOioqKyMvLO+kx7K2vSgVGapPpQrlFfklWiDYpJR988AGVlZUp3edgUR2u8ejo6GD58uWGGnXOVIQQzJ07l69//evqWicyOt4fwQaRY2ixWBImfKWU1NbW8ve//53Nmzfj9XpxuVy43W5MJhMNDQ0cP36c9vb2hFqce7Omdf2uv69s4owzzmDUqFHd3l96GiyMF22hUCgWKKbrA053c3O2bt0acw2rra1NeW7TkxEv3Ppqte9qUcu262TKlCncfffdau71ILBarVRUVPR7/lgqLbUqd1sEfa5vW1tbuotiCLJCtAWDQVatWpVSK1siMNrNxEBFSSubN2/m+PHj6S5GVmAymbj11lv5yle+ku6ipJW8vDzuv//+fgk2Hd1NcjDpKPQk2Rs2bGDlypUcO3YMgPz8fPLz8zGZTNTX19PS0pJQD4KBiLST/V4P+Z+NE/Zzc3P5+te/DpyYYy2e+GAj8VY1fVnXQaeegik0NDTQ2tqKlBK/32/Ifk8Xbo8++igVFRW9rtc10qmR7q+JYOTIkTz22GPk5OSkuygZzdixY/vdz6XDJTtROYAzFf2+pdwivyTj73pSSj799FM+//zzlOwrUaLNaA8cQsCCK5zceJmD0iJjlS3VNDc389Zbbw3ZjjLR5OTksGjRIkaPHp3uoqSFkpISfvzjHw9IsOkIEUkDYDb3L3aU/hB/9OhRVq5cGQs2AsQseBaLhdbWVlpbWwdUtvgyJsqS1tv24+uVbQ/luuvbtGnTYhaz+PtOvEjTrWpdX925EPUk2trb2zly5Ajt7e3U19cbNr+iLtx++MMfMm3atBO+090gs1Go6TgcDu655x7Ky8uzto6poLCwELvdPiBXu3QIt/g+YKghpaSmpmbI5mTrjox/Og8EArzyyispO6mJbDhG6nhHl5m4co6dm+Y5+ckD+Tx4k4sp48zYe56jm9W8/fbbhhx1zlRGjhzJd77znSE3QlxaWsqyZcuYMmXKoAZqukto3ZfftLe389FHH7F69epO4eSFiITut9vthEKhk4aa746ehFai3Bt7235fc5VlIna7ncWLF5Ofn9+tIIv/H/8KBoOx5Ofx9HasXC4Xdrud1tZWRowYYejQ8UIICgoKWLJkCdOmTTvBspbNaJrGtddey5w5c7K+rslE0zRKS0sH5VqqX2+pdpWM/z8U0HOyDXYwMdvIeNG2bds2Pvnkk6Tvp7t8NwPFiFa2K8934LRHOqN8t8ZF59j5wT15/OCePMaOHHq+8/X19bz55ptDqpNMJkIIZs2axfXXXz9k5mKUlJTw/e9/n1NPPTUhN3efz9draGodPbDEli1beOeddzh06BAmkwmLxRIrhx7EwmKx0NHR0af5a8kUaQNZT69HtiGEYMKECfzTP/0TeXl5MTHW0ysYDBIMBru9P+mupD2h5wIcNmxYj9Y4I6ELtyeeeIKZM2cOCcEGcPbZZ7Nw4cIh03cmAyEEZWVlmM3mQc9ZT8c1N9SEm9/vp7q6OmsH5wZKRt/xdCtbXx5kBkuiG4qRbjQVpSbOO+PE0OIWs6C8VMNpG5qNZuXKldTU1KS7GFmDpmksXLiQiy++ON1FSTqjR4/mqaeeYsqUKQlp61LKPgkrPYz/mjVr2LFjB6FQCKfTGXOD1LHZbLHQ/T3lgeuLSOtuDtFARFpP9FYGk8mUtW4z+iDHsmXLKCwsjAkz/RUIBGLvu7Ou6cfnZKHDx4wZg9vtpqamBp/PZ7hAJPFomobL5SI3N5fS0lKefPJJzjvvvKwXMhUVFTz22GODmtOqiFiwy8rKEjbQ05f+K5HEGw6yXbiFw2Fqa2tT8myfaWS0aKuqqkrZXLZEYSSxBp2tbF2RUlJ5pIOjtUNTtNXX17Nx48as7yBTidVq5Z577mH8+PHpLkrSmD59Ov/2b/+WMMGmc7LrUErJsWPHWLlyJbW1tTgcDhwOB06nk5ycnE6WNl1wAbEbYzJEWl+FXG/rdbc8fg5TtqILtx//+MdMnToVODEHW3diTT8uJ7Mm5OTkcOqpp+J2uykrK4t4WeTnJ6s6A8ZiseBwOMjJycFsNsfqV1hYyFNPPcU3v/nNrBVuTqeTJUuWxCyhioGhaRrFxcX4/f6kDMCn0uKf7RY33a1/IC77Q4GMFW3hcJgVK1YkPQxoMnKyGanztZjBovlobgueUMdgSHK42oc/OwezT4qUktdee4329vZ0FyWrKCoq4sEHHyQ3NzfdRUk406dP58knn6SoqCih7VyISCCS3rYZDof55JNP8Hq9WCwW7HY7VqsVm82G0+mM5WGDyAO7nrg7Plm1vq++irTuyjlYC1pfXSyFEIaeg5UIhBCcddZZ/OQnP+G0007DbDbHXiaTKebmqr+EEL0m1Y5n2rRpVFRUkJ+fjxCChoaGPofVTzZCCMxmc+zatdvtmM3mE64pu93O7bffnpXCTdM0br311ljuPsXAyc3N7VNOtv7Ste9KBdkelCQYDHL8+HHlFtkDGSvaDh8+zOrVq5O6j0SPaBhNsAHk2CUNjR18tLmBzV80U13rJRSKdArVtT52VIbweNNdyvRRWVnJhx9+mNWdZKoRQnD66adzyy23ZNWD1tlnn50UwQZfijan09njOm1tbbHQyHpkRavVislkwmq1xgKP6GLHZrPFhFt3Iu1k4dMHakHrSQj2tPxkxyXbESIyF+fJJ59k9OjRMQubfny65mfrC6eccgpnnnkmxcXFmEwmPB4PNpstrZY2k8mEw+HA7XaTm5uLy+XC6XSe1IphtVq5/fbbufHGG7OqP5k7dy5XX311VtUpHVitVoYPH560vqI//VWi6C0dSCYjpaShoUENlPdCRoq2cDjMa6+9RktLS0r2lc2I6J9gUHKk2suGz5pYu6WBfYc87DnkYdchgST7H4x6IhwO8+qrr6pOJMFomsbVV1/NRRddlPEP3kIIpk+fzve+9z0KCwuTuh/dwtIdJpMJs9lMOBzG6/XS0dGBlDIm0HRrm6ZpNDc3EwqFYt91DZk+kLlq/bWS9Vec9fc32YbNZmPRokWMHTsWGLgXyKmnnspll13GmDFj0DSN9vZ2Dh48yKRJk/p0HhIpIvTtWa3WTha1/rq+Wq1W7rjjDm688cZ+p8UwImPHjmXRokW9DtIYHSHE/wghaoQQn8ct+4EQokoIsTX6ujLuuyeEEHuFELuEEPMSVAaKi4tjg1XJJNV9UnzbzwbhpnKy9Y2MFG3Hjh1LqpUtPidOojBilDMpJe1eyf4q8AdAysiroTnAF3ta2XsoSF1TukuZfvbv38+6deuyomM0EjabjQcffJBTTjkl3UUZMCaTiRtuuIFly5YlxcLWFSEEdru92/5En78GEAqFYrnX9ETUNpuNgoKCWNRIPYWA0+ns1eLVdf5YX61kvQm/nuqmxFnPWK1Whg0bxj333MPYsWP7fYw0TeOUU05h7ty5lJWVYbPZCAaDHDhwgJEjR1JSUtLrdjRNIycnB5fLhcvlwmq1xiy1Pd3f9IEG/VrQNC02R023qLndbpxO56DFli7cHn30UVwu16C2lU7cbjePPPIIZWVl6S7KYPkdcEU3y38hpTwr+noLQAgxBbgJmBr9zX8JIQY9OpCXl0dFRUVK+xAl3AaGlJLjx4/3KeDWUMZ4SuIkSClZu3Zt0q1syRBsRnz48HglH30Gb62DT/dCe0dEuIXC8Pl+QSi7DY19IhwOs3r1atWZJAG3283SpUvJy8tLd1H6jS7Y7r777pTNzxNR18bu8t1pmhZ70NNd5pqammhqaooFpcjPz8ftduPz+fB6vQghYta3ngRbvFjrTdD1VUQMVJgNZUEnRCSvnqZpMeE2ZsyYPh+LnJwcLrroIubNm8f48ePJzc0lHA5z6NAhnE4nM2bM6HVg0Waz4Xa7YwLMbDbjdDpxOp243W5cLhc5OTkxt0a73R6L9KiLPF2k5eTkxBLFJzrPmtVq5bLLLuORRx7JSOGmaRrf/va3mTx5csZf21LKNUBDH1e/BnhJSumTUlYCe4GZg9m/xWKhvLw85RFRU33essFFUo96rHKynZyME21NTU288cYb6S5GvzFaB9x5hEbQ0CLYsgve/Bj+sQN2H4aq2oiAU8DmzZvZtm1buouRdQghGD9+PN/5zncyau6Gpmlcf/313HnnnVitqc1Ar7uUdT1eQghGjx7d6eE7GAxSX19PS0sL4XAYm81GaWkpUko8Hg/hcBi32x1zkezJUqaLs/igF3ogjN4sZImwsg0lcdYb+hwvIQTDhg3jrrvuYvLkySfMS9TRNI2CggKmTZvGddddx6xZsxg3bhwOh4NQKMSBAwcIBAKcf/752Gy2Tr8zm82dhJfD4ejR2qpfC/r8Sd2SZrFYYr+JD5SS7PMphGDu3LksXbo044TbvHnzmDdvniE9cxLI/UKIz0TEfbIgumwkcDhunSPRZQNm9OjR2O32wWyi3+jXdaoj22a6aPP7/dTU1GR8PVJBRjl/Syl59913qaqqSuo+sjnEfzwn1lPQ3gFf7JeAQNPUw5KO3+/nlVde4fTTT8/6iHWpRtM0LrnkEvbv38/y5csN33FrmsYNN9zAXXfdlXLBFo/dbqejo6OTV0BxcTHDhg3rlF8wEAhw9OhRnE4nDoeDkpISqqur0TSNjo4OXC4XBQUFsd/0tc3HP8DHE3/+4t8Ppi9R/VDkGJSWllJZWUk4HKasrIwHHniA/fv38+mnn9Lc3Bw73larldLSUkpLSyksLCQ3NzcmzD0eD3v27MFsNnPJJZfEgpHoUSnjRVWmHnchBBdccAEAP//5z5MeZToRnHHGGdx1112dBHQW8ivgR4CM/v834M7+bEAIsQhY1Ns6usttOvJ8xbeZVN7LEtXXpppwOBzLE6k4ORkl2pqbm3n99deT1hCSEY0nM0eJjVtmk8mEzWbFbrPR0NiU0n1v3ryZzz//nLPOOsuwxydTsVqt3HbbbezZs4etW7emuzg94nK5uO2227jmmmvSKth0N0lN02hra4v1WSaTifHjx3cSbVJKvF4vVVVVOJ1OcnNzGTVqFAcPHqStrY2cnByKiopobm4+4SGnP9d5dw/6PQm4gWx/qKO7sg4fPpzq6mogItxPPfVUiouLqampQdO0mIgfMWIEubm5sWWBQIBjx45x+PBhysvLufDCCzuFQc+2c6Fb3BwOB//1X//FwYMH012kHikqKuL+++9n2LBh6S5KUpFSHtffCyF+A7wZ/VgFlMetOiq6rLtt/Br4dXQbJ3QqmqZRXl6OxWJJ+5QGTdNS5r6o70OIE9O4GBUpJW1tbTQ1NaW7KBlDn23wQgiTEOITIcSb0c9jhRAbRCTaz5+EENboclv0897o92MSUVApJWvWrEmqlU3fT6LIhEbTE0YRmwV5eYwfO4ZZ08/mhmvmc+s3r+feO27l2vlfxZbih2a/38+KFSuS2gGnu52lk5ycHL773e8a9sElJyeHhx56iOuuuy6tgk1HdzuLt/wKIRg3btwJodv1OQPV1dUEg0FKS0txOBwEg8FYuPeSkpIT3B277q+v7os9uVgaxeUxU9uZEILhw4eTl5dHMBgkEAhw5MgR/H4/kyZNYvjw4RQWFjJlyhTy8/PRNA2/38/hw4fZtGkTtbW1zJkzh6uuuoqCgoKEzykzGpqmMXPmTJ5++mnGjBmT7uJ0i9Vq5e6772bixInpLkrSEULER1f5BqBHlnwDuCna3sYCE4F/DGQfZWVlsb4tnaRjMCTT5rcFg0Gqq6uzPkp7IumP4/R3gR1xn/+VSBSgCUAjcFd0+V1AY3T5L6LrDRq/38+qVauSbmVLJEa/GfYW8csIOB0O5s+7lAXXXsMVl1zIlEmnUDFqJK6cHMpKSpg0cXzKy7R169ZkDxyktZ2lEyEEY8aM4b777jOcC6ou2C688EJDzTcRQuBwOHA4HLFlNput28ASUkqqq6tpbGwEYNy4cXg8HpqbmwkEAhQUFMQiYPYmyk4m3rr2H/p8uPhXT7/pqe/p6776Qca2M03TGDNmDCNHjqShoSE2T1HPtzZ69Ggg8kDU2NjIp59+Sm1tLTNmzOBb3/oWZ599tiEGHVKFEJG5nsuWLYsdGyNx5ZVXcvnllxvmvpsohBB/BNYBk4QQR4QQdwE/FUJsE0J8BlwELAGQUn4BvAxsB1YC90kpQ/3dZ05ODvn5+bHAS+kmncIt1QIuPup6X/YvpaS+vp6Ojo6UlTEb6NPThxBiFHAV8P+inwVwMbA8uspzwNej76+Jfib6/SUiAVfspk2b+Pzzz0++4gBIxgVupAe77tBPif4Q1fW7dN9AhBCMrSintbW127Jomsas6al/+GhubmbFihVJGRkyQjtLNyLq0nTttdem/RrU0QXbRRddZMh2rWlaLBgFRI7hyJEjT3hAlVLi9/s5cuQIHR0d5OfnU1JSgtfrjbmnlJaW9irc+mt9680C1/WlBzY5WbCTwZIN7UyISMLtCy+8MBZpsK6uDovFwoEDB6iqqorNW7z00ku5+eabmTVrFi6XyzDtKpXoA0I/+MEPDCXcpk6dyl133ZVRQZj6ipRygZSyTEppkVKOklI+K6W8RUp5upTyDCnl1VLKY3Hr/7OUcryUcpKU8u3+7k8IQUlJCQ6Hw3DWplQ/U6XL4tZVuOnLusPr9VJfX5/K4mUFfZ3T9gzwKOCOfi4CmqSUuv05PtJPLAqQlDIohGiOrt8pY56Im0w6atSoXnfu8/l45ZVXkjp6kowL3Og3R718eh4nI5HrclFYkE9zSyvNzS3k5+edcDxLhxczecJ4Ptu+o4etJId3332Xr3/965SXl5985f7xDGlsZ0bBZDJx8803U1VVxUcffZTWslRUVLB48WJmzpxpuDYSjxCRkPBer5dgMIjZbGb69OnU1NSckBi+sbGR2tparFYro0ePjs0psFgs5OfnU1ZWhsVioa6urt+DE4nu8+LFKEQEqu72NwieIcHtDFLf1oQQ2Gw2KioqKC8vj90f9XuZLoAVEXTh9sMf/pD//u//Zt26dWktz4gRI3j44YczLsKlwWgDdkHkut+/fz/79+9Pc5ESzjC66W+yDFXHzvQ4snRS0SaEmA/USCk3CyEu7OMOT0r8ZNJp06b1qJiklCmxsiUSo4u1rhiyvAIEkfNz5OhR8vJyux21P23KZD7fuSulPtF6QJzFixcn7EE+3e3MaLjdbu655x4OHjzI4cOHT/6DJKC7VOmJjI2MEJHAJFLK2FyO3Nxczj77bD766KNOo57hcJijR49SWFiIy+Vi/Pjx7Nixg8bGRqSU5OXlMXz4cFwuFzU1NXg8nn71kV3bhJ6UOf4YWq3WE5KRO51OCgsLO9WpoKCgU9JlXagM9Hwkq51BetuaEGLQyamHArqr5JNPPsnPf/5z1qxZkxaLhMPhYNGiRRnRtxicXVLKGekuRDIRQmxSdcx8ElXHvvTyc4CrhRBXAnYgF/glkC+EMEdHJ+Mj/ehRgI4IIcxAHjBgG2g4HOYvf/lLUiaVJsvv1wjuhZlOS2sbTc0tFBbk09TcgqejA1eXhMJCCMaUj2LUiDIOHUlugJqurF69mptuuimRQTPS2s6MyKhRo7jnnnv48Y9/nPIEqRUVFTz11FMZ91BlsViwWq34/X6EiOTAO3z4MAcOHOi0XltbGzU1NZjNZmw2G2PHjuXAgQPU1dXh8/koKCjA6XQyZswYPB4Pra2teL3e2OCIfkxcLhdlZWWxfEhCCIqLizsJCE3TcLvdJ8yLS3UuoyiqnSlwu90sXbo0FuAslWiaxjXXXMPcuXMzqm9RKBTp56SiTUr5BPAEQHRkcqmUcqEQ4hXgeuAl4Dbg9ehP3oh+Xhf9frUchCrat29fUpMaJ1qwGdmFKpOQUlJTV09Bfl7EMnCsmonjx8W+8/sD+Pw+2trayU2De0ljYyNr1qzhG9/4RkJuvOluZ0ZECMF5553H9ddfzwsvvJAya2pFRQU/+MEPMk6w6TidTiASvEnTNGbPnk17ezu1tbXAl31eTU0NRUVFMUvY6NGjqaqqoqmpiXA4jNPpxOl0YrfbY0md41ML5OXlMXLkyBMEmZFR7Uyh43a7eeSRRwBSKtzOOussbr31VvWsoFAo+s1g/CkeA14SQvxv4BPg2ejyZ4HnhRB7gQbgpoHuIBQK8ec///mEORmJIJlukZnyAGN06hoaKO8oI8fppLa2DiEEgUAQT4cHv99PMBiita2NPfsrU142KSUrVqzgkksuIS8vL5m7Sno7MzKaprFw4UKOHj3Ku+++m/T9nXbaaSxZsiRjBZteZofDgaZpeL1enE4ns2bN4r333uvUl3o8HpqamjCbzbFgJiUlJTQ3N9PW1obP58Nut8cSLhcUFGCxWHA6nbjd7tg+soQh3c6GKrpwczqdrFq1ikAgkNT9VVRU8Nhjj8UGVhSD5tfpLkAKUHXMDhJSx36JNinl+8D70ff7gZndrOMFbkhA2di3bx8ffvhhIjbViZNFtRkMmfigZ0SklPgDAWrr6nGWOwiGQhw9Vt1pnUAwyJ7KA/i6JAROFYcPH2bVqlUJs7bppLqdGR2bzcaiRYuorKxk3759SdvP3LlzWbp0aUZZjnpC0zRsNhvhcBghBKWlpVx55ZWsXLmS1tZWIDIo1tjYGEvArLssOp1ORo0axYQJE8jPz+92Lmk2oNqZAr50layoqOC3v/1t0oRbTk4ODz74IMOHD0/K9oci0XmkWY2qY3aQqDoadpg0FArx2muvJdzKpgu1RLtaqXlsiSN+ruHR4zX4AwGklAQCAdo9HdQ1NLL/4CG279pNc0trWsu5YsUKWlpa0laGoUJxcTGLFy8mNzc3Kds///zzWbp0Kbm5Jwa8yVSEEDidThwOB263m4qKCq666ircbnesH/T5fHg8HkKhED6fDyklU6ZMYfr06RQWFhoqIbZCkSzMZjM33ngjt99+e1ICumiaxoIFC5g2bZpqQwqFYsAYVrTV1NSwdu3apGw7STm2VGecQPSHyg6vl5179rH18+2s37KVjVs/5dMvtnPwSBWNzekXS4cPH2bz5s3pLkbWI4Rg2rRpfOtb30r4Q9X555/Po48+mjRBmC7ihZbJZIrlb5s/fz5lZWUIIQiFQng8Hnw+Xywkenl5uQoVrxhymM1mvvnNb3LHHXdgsVgSuu05c+Zw7bXXqnalUCgGhSFFm5SSd999N+EWjGTNH0+GYEtHRnsjET9Xpq6hgYamJvx+f6dcREY4NlJK3nzzTXw+X7qLkvVomsY3vvEN5syZk5DtmUwmvvKVr2SlYOsJXbhde+21XHjhhYwcORKHw0FFRQVz5sxhzJgxavBJMWTRhdttt90Wi4g6WMrLy7n33nvVPLYEI4S4QgixSwixVwjxeLrLM1CEEP8jhKgRQnwet6xQCPF3IcSe6P+C6HIhhPj3aJ0/E0Kcnb6S9w0hRLkQ4j0hxHYhxBdCiO9Gl2dTHe1CiH8IIT6N1vHp6PKxQogN0br8SQhhjS63RT/vjX4/pq/7MqRoq6ur480330zKtpNlZUsU8WJNL6sRxEkq6cvxNNIx2bZtG1u2bDFUmbIVm83GQw89xKRJkwa1HbPZzO233873vve9ISPY4nE4HEybNo2rrrqKyy67jMmTJ2Oz2bIpsIhCMSDMZjMLFizg6aefpqCgYFDbcrlcPPHEE5SVlSWodAoAIYQJ+L/AV4EpwAIhxJT0lmrA/A64osuyx4FVUsqJwKroZ4jUd2L0tQj4VYrKOBiCwMNSyinAucB90XOVTXX0ARdLKc8EzgKuEEKcC/wr8Asp5QSgEbgruv5dQGN0+S+i6/UJw92hpZSsXLmS48ePJ3Sb8SIokSRjVDocDg95AdAX66VRrG2BQIBXXnkl6ZHHFBFyc3N56KGHBhy102w2c+utt7JgwYKEjaZnInrgEYvFoqxrCkUcJpOJmTNn8sQTT5Cfnz+gbWiaxt13383kyZNV+0o8M4G9Usr9Uko/kVQd16S5TANCSrmGSGTaeK4Bnou+fw74etzy38sI64nklzT0iICU8piUckv0fSuwAxhJdtVRSinboh8t0ZcELgaWR5d3raNe9+XAJaKPnYThRFt9fX1SrGzJyvGUSNfI7kSIEURJuuiLaDMKn332GVu2bEl3MYYEQggmTpzIokWL+j2/TRdsyZgbp1AosgchBOeccw5PPvnkgITb5ZdfzhVXXKGs18lhJHA47vOR6LJsoURKeSz6vhooib7P6HpH3QCnARvIsjoKIUxCiK1ADfB3YB/QJKUMRleJr0esjtHvm4GivuzHUL2JlJKNGzcm1MqWTJIxetZViAz1EbpMEW7BYJC///3vKUsAPdTRNI1LL72Uq666qs9txG63K8GmUCj6jC7cnnjiCYYNG9bn302ZMoU777xzSFvyFYlBRh5yjPGgMwiEEC7gVeB/SSk7BazIhjpKKUNSyrOAUUQswZOTsR9DibaOjg7eeOONhG4zmS50qRBURhEl6aCrFbNr6HGjRezcsGED+/fvT3cxhgw2m41vf/vbnHbaaSddNz8/n2XLlrFw4UIl2BQKRZ8RQjBz5kx+8YtfMHnyyZ/DCgoKWLJkCcXFxSko3ZClCiiP+zwquixbOK67BEb/10SXZ2S9hRAWIoLtD1LKP0cXZ1UddaSUTcB7wGwirp36A0d8PWJ1jH6fB9T3ZfuGEW1SStatW8fu3bsHvZ3uXolGFws9iYaeytFdmXoqZ3yOpKFMd7mijCja2traeO2115S1LYXk5OTw0EMPUVhY2OM6eXl5PP7445x77rkq5LZCoeg3QghGjRrFU0891WsQJLPZzKJFi5gwYYKh7k1ZyEZgYjQ6nxW4CUjsiH96eQO4Lfr+NuD1uOW3RiMsngs0x7kYGpLoXK1ngR1Syv8T91U21bFYCJEffe8ALiMyd+894Proal3rqNf9emC17KNQMYxo83q9LF++PBbSvb90FT564JFkCbaeBEN8GcLhcKwM+nv91d268WVVgi1CvDg2smDT+eCDD6isrEx3MYYMIppb7P777+82t1JeXh5PPPEEs2bNMuT1olAoMgMhBGVlZSxbtqxH4aZHY1V9TXKJzgO6H3iHyMPxy1LKL9JbqoEhhPgjsA6YJIQ4IoS4C/gX4DIhxB7g0uhngLeA/cBe4DfA4jQUub/MAW4BLhZCbI2+riS76lgGvCeE+IzIgMLfpZRvAo8BDwkh9hKZs/ZsdP1ngaLo8of4MnLmSTGMn9C6devYtWvXgH7bnWBLBz1ZzLorTzgcRgjR7Xeqw+9MJh0P3dr20EMPpbsoQwYhBHPnzmXnzp28/PLLseW6hU0JNoVCkQh04fbUU0/xwx/+sNMzy9SpU7nzzjuV+3WKkFK+ReQBP6ORUi7o4atLullXAvclt0SJRUr5EdDTDThb6vgZkQArXZfvJzK/retyL3DDQPZlCEublJJVq1YNyK0sHUmou1p5BioYh/J8tWxm/fr1NDR0jeCrSCZms5mFCxcye/ZsAE455RR+8pOfcO655yrBplAoEoYQghEjRvCjH/2IefPmxYTcd7/73SGZ81GhUKQOQwwJhUIhLBYLs2bN6vdvg8HggF0qB4LFYjkhhG84HCYYjET1TJQQ624/isxACMHBgwfTXYwhR15eHosXL8ZkMnHvvfcyYsQIJdgUCkXCEUIwfPhwvvvd72K32znzzDOZOHGi6m8UCkVSMYRoM5vNLFu2rN+/8/v9eDyelFmszGYzLpfrBCtbe3t7QhMrCyFwu90qaIJC0U9GjRrFsmXLMJvN6gFKoVAkFafTyQMPPBCbg65QKBTJxBCiDfo3b0lKSSAQoKOjo9+/HShCCJxOZyfXSCklfr+fYDCY8DK0t7fjcrmUcFMo+oEQotuAJAqFQpEM1Bw2hUKRKjLO/04XbKm0sOn79Xg8hEKhWBkCgQBerzcp+xNCKPdIhUKhUCgUCoVCYRxLW19Jh2DTCYVCtLW1Ybfb8fl8Sc3HZbfbk7ZthUKhUCgUCoVCkTkII0QwFEK0AgOL9585DAPq0l2IJKPq2JnRUsriZBamPwghaoF2svscqWswO8jYdgbqnpZFZHsd+1s/w7U1hWIoYRRL2y4p5Yx0FyKZCCE2qTpmPplcRyllcSaXvy9ke/1A1TFDUPe0LCDb65jt9VMosg01aUqhUCgUCoVCoVAoDIwSbQqFQqFQKBQKhUJhYIwi2n6d7gKkAFXH7CDT65jp5T8Z2V4/UHXMBDK9/H1B1THzyfb6KRRZhSECkSgUCoVCoVAoFAqFonuMYmlTKBQKhUKhUCgUCkU3KNGmUCgUCoVCoVAoFAYm7aJNCHGFEGKXEGKvEOLxdJdnIAghyoUQ7wkhtgshvhBCfDe6vFAI8XchxJ7o/4LociGE+PdonT8TQpyd3hr0HSGESQjxiRDizejnsUKIDdG6/EkIYY0ut0U/741+PyatBe8jQoh8IcRyIcROIcQOIcTsbDiP2dDOYOi0NdXOMvMcqnZm/HPUFdXWsuM8KhRDgbSKNiGECfi/wFeBKcACIcSUdJZpgASBh6WUU4Bzgfui9XgcWCWlnAisin6GSH0nRl+LgF+lvsgD5rvAjrjP/wr8Qko5AWgE7oouvwtojC7/RXS9TOCXwEop5WTgTCJ1zejzmEXtDIZOW1PtLMPOoWpnxj9HPaDaWnacR4Ui+5FSpu0FzAbeifv8BPBEOsuUoHq9DlwG7ALKosvKiCRcBfj/gAVx68fWM/ILGEWkg78YeBMQQB1g7no+gXeA2dH35uh6It11OEn98oDKruXM9POYre0sWpesa2uqnWXmOVTtzPjnqJt6qbaWBedRvdRrqLzS7R45Ejgc9/lIdFnGEnWZmAZsAEqklMeiX1UDJdH3mVrvZ4BHgXD0cxHQJKUMRj/H1yNWx+j3zdH1jcxYoBb4bdRd5v8JIXLI/POYKeXsF1nc1p5BtbNMPIeZUs5+kcXtDFRbg+w4jwrFkCDdoi2rEEK4gFeB/yWlbIn/TkopgYzNryCEmA/USCk3p7ssScQMnA38Sko5DWjnS7cRIPPPY7aQrW1NtbMImXwOs4lsbWeg2ppOpp9HhWIokW7RVgWUx30eFV2WcQghLERubn+QUv45uvi4EKIs+n0ZUBNdnon1ngNcLYQ4ALxExJ3kl0C+EMIcXSe+HrE6Rr/PA+pTWeABcAQ4IqXcEP28nMgNL9PPY6aUs09keVtT7YyMPYeZUs4+keXtDFRby5bzqFAMGdIt2jYCE6PRmqzATcAbaS5TvxFCCOBZYIeU8v/EffUGcFv0/W1E5gXoy2+NRmo6F2iOc1UwJFLKJ6SUo6SUY4icp9VSyoXAe8D10dW61lGv+/XR9Q09mielrAYOCyEmRRddAmwn889jVrQzyP62ptpZRp9D1c6Mf45iqLaWHedRoRhSpHtSHXAlsBvYB3wv3eUZYB3OJ+Je8BmwNfq6koi/+ypgD/AuUBhdXxCJMrYP2AbMSHcd+lnfC4E3o+/HAf8A9gKvALbocnv0897o9+PSXe4+1u0sYFP0XK4ACrLhPGZDO4vWY8i0NdXOMu8cqnZm/HPUQ31VW8uC86he6pXtLyGloQeKFAqFQqFQKBQKhWJIk273SIVCoVAoFAqFQqFQ9IISbQqFQqFQKBQKhUJhYJRoUygUCoVCoVAoFAoDo0SbQqFQKBQKhUKhUBgYJdoUCoVCoVAoFAqFwsAo0aZQKBQKhUKhUCgUBkaJNoVCoVAoFAqFQqEwMP8/YvsSPZBbx8kAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Language Goal: put the gray block on the green and red blocks\n", + "Step Reward: 0.16666666666666663\n", + "Total Reward: 0.6666666666666666\n", + "\n", + "Step: 7/8\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Language Goal: put the yellow block on the blue and gray blocks\n", + "Step Reward: 0.16666666666666663\n", + "Total Reward: 0.8333333333333333\n", + "\n", + "Step: 8/8\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Language Goal: done stacking block pyramid.\n", + "Step Reward: 0.16666666666666663\n", + "Total Reward: 0.9999999999999999\n", + "Done, Total Reward: 0.9999999999999999\n", + "\n", + "\n", + "Dataset Statistics: \n", + "Color Mean: [0.61303802 0.61201705 0.61082985], Std: [0.00764521 0.0077408 0.00799113]\n", + "Depth Mean: 1.142882227897644, Std: 0.013715719804167747\n", + "Total Image-Action Pairs: 7\n" + ] + } + ], + "source": [ + "color_sums = []\n", + "depth_sums = []\n", + "\n", + "total_images = 0\n", + "\n", + "for i in range(0, min(max_episodes, ds.n_episodes)):\n", + " print(f'\\n\\nEpisode: {i + 1}/{ds.n_episodes}')\n", + " episode, seed = ds.load(i)\n", + " \n", + " total_images += len(episode)-1\n", + " \n", + " total_reward = 0\n", + " for step in range(min(max_steps, len(episode))):\n", + " print(f\"\\nStep: {step+1}/{len(episode)}\")\n", + " obs, act, reward, info = episode[step]\n", + " \n", + " total_reward += reward\n", + " batch = ds[i]\n", + " \n", + " num_images = len(obs['color'])\n", + " fig, axs = plt.subplots(2, num_images+1, figsize=(15, 6))\n", + " for n in range(num_images):\n", + " axs[1, n].imshow(obs['color'][n])\n", + " axs[1, n].set_title(f'Raw RGB {n+1}')\n", + " \n", + " axs[0, n].imshow(obs['depth'][n])\n", + " axs[0, n].set_title(f'Raw Depth {n+1}')\n", + " \n", + " color_sums.append(np.mean(obs['color'][0], axis=(0,1)) / 255.0)\n", + " depth_sums.append(np.mean(obs['depth'][0], axis=(0,1)))\n", + " \n", + " cam_config = None\n", + " if b'camera_info' in info:\n", + " cam_config = ds.get_cam_config(info[b'camera_info'])\n", + " \n", + " img_depth = ds.get_image(obs, cam_config=cam_config)\n", + " img_tensor = torch.from_numpy(img_depth)\n", + " img = np.uint8(img_tensor.detach().cpu().numpy())\n", + " img = img.transpose(1,0,2)\n", + " \n", + " if step < len(episode)-1 and episode[step]:\n", + " batch = ds.process_sample(episode[step], augment=augment)\n", + " else:\n", + " batch = ds.process_goal(episode[step], perturb_params=None)\n", + " \n", + " img_sample = batch['img']\n", + " img_sample = torch.from_numpy(img_sample)\n", + " color = np.uint8(img_sample.detach().cpu().numpy())[:,:,:3]\n", + " color = color.transpose(1,0,2)\n", + " depth = np.array(img_sample.detach().cpu().numpy())[:,:,3]\n", + " depth = depth.transpose(1,0)\n", + " \n", + " axs[0, num_images].imshow(depth)\n", + " axs[0, num_images].set_title('Depth')\n", + " \n", + " axs[1,num_images].imshow(color)\n", + " axs[1,num_images].set_title('RGB + Oracle Pick & Place')\n", + " \n", + " if act and step < len(episode)-1:\n", + " p0 = batch['p0']\n", + " p1 = batch['p1']\n", + " p0_theta = batch['p0_theta']\n", + " p1_theta = batch['p1_theta'] + p0_theta\n", + " \n", + " pick = p0\n", + " place = p1\n", + " \n", + " line_len = 30\n", + " pick0 = (pick[0] + line_len/2.0 * np.sin(p0_theta), pick[1] + line_len/2.0 * np.cos(p0_theta))\n", + " pick1 = (pick[0] - line_len/2.0 * np.sin(p0_theta), pick[1] - line_len/2.0 * np.cos(p0_theta))\n", + " axs[1,num_images].plot((pick1[0], pick0[0]), (pick1[1], pick0[1]), color='r', linewidth=2)\n", + " \n", + " place0 = (place[0] + line_len/2.0 * np.sin(p1_theta), place[1] + line_len/2.0 * np.cos(p1_theta))\n", + " place1 = (place[0] - line_len/2.0 * np.sin(p1_theta), place[1] - line_len/2.0 * np.cos(p1_theta))\n", + " axs[1,num_images].plot((place1[0], place0[0]), (place1[1], place0[1]), color='g', linewidth=2)\n", + " \n", + " c_pick = plt.Circle(pick, 3, color='r', fill=False)\n", + " c_place = plt.Circle(place, 3, color='g', fill=False)\n", + "\n", + " axs[1,num_images].add_patch(c_pick)\n", + " axs[1,num_images].add_patch(c_place)\n", + " \n", + " plt.show()\n", + " \n", + " print(f\"Language Goal: {batch['lang_goal']}\")\n", + " print(f\"Step Reward: {reward}\")\n", + " print(f\"Total Reward: {total_reward}\")\n", + "\n", + " print(f\"Done, Total Reward: {total_reward}\")\n", + "\n", + "print(\"\\n\\nDataset Statistics: \")\n", + "print(f\"Color Mean: {np.mean(color_sums, axis=0)}, Std: {np.std(color_sums, axis=0)}\")\n", + "print(f\"Depth Mean: {np.mean(depth_sums, axis=0)}, Std: {np.std(depth_sums, axis=0)}\")\n", + "print(f\"Total Image-Action Pairs: {total_images}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/dataset_test.py b/notebooks/dataset_test.py new file mode 100644 index 0000000000000000000000000000000000000000..191963b2a50622700a123b6b945b88ade1ac1ab2 --- /dev/null +++ b/notebooks/dataset_test.py @@ -0,0 +1,166 @@ +import os +import sys +import numpy as np +import hydra + +from cliport.dataset import RavensDataset +from cliport.utils import utils +from cliport import tasks +from cliport.environments.environment import Environment + +import torch + + +import matplotlib +import matplotlib.pyplot as plt + + + +mode = 'train' +augment = True + +### Uncomment the task you want to generate ### +# task = 'align-rope' +# task = 'assembling-kits-seq-seen-colors' +# task = 'assembling-kits-seq-unseen-colors' +# task = 'assembling-kits-seq-full' +# task = 'packing-shapes' +# task = 'packing-boxes-pairs-seen-colors' +# task = 'packing-boxes-pairs-unseen-colors' +# task = 'packing-boxes-pairs-full' +# task = 'packing-seen-google-objects-seq' +# task = 'packing-unseen-google-objects-seq' +# task = 'packing-seen-google-objects-group' +# task = 'packing-unseen-google-objects-group' +# task = 'put-block-in-bowl-seen-colors' +# task = 'put-block-in-bowl-unseen-colors' +# task = 'put-block-in-bowl-full' +task = 'align-box-corner' +# task = 'stack-block-pyramid-seq-unseen-colors' +# task = 'stack-block-pyramid-seq-full' +# task = 'separating-piles-seen-colors' +# task = 'separating-piles-unseen-colors' +# task = 'separating-piles-full' +# task = 'towers-of-hanoi-seq-seen-colors' +# task = 'towers-of-hanoi-seq-unseen-colors' +# task = 'towers-of-hanoi-seq-full' + +### visualization settings +max_episodes = 1 +max_steps = 100 + + + +root_dir = os.environ['CLIPORT_ROOT'] +config_file = 'train.yaml' +cfg = utils.load_hydra_config(os.path.join(root_dir, f'cliport/cfg/{config_file}')) + +# Override defaults +cfg['task'] = task +cfg['mode'] = mode +cfg['train']['data_augmentation'] = True +data_dir = os.path.join(root_dir, 'data') + + + +task = tasks.names[cfg['task']]() +task.mode = mode + +ds = RavensDataset(os.path.join(data_dir, f'{cfg["task"]}-{cfg["mode"]}'), cfg, n_demos=10, augment=augment) + + + +color_sums = [] +depth_sums = [] + +total_images = 0 + +for i in range(0, min(max_episodes, ds.n_episodes)): + print(f'\n\nEpisode: {i + 1}/{ds.n_episodes}') + episode, seed = ds.load(i) + + total_images += len(episode)-1 + + total_reward = 0 + for step in range(min(max_steps, len(episode))): + print(f"\nStep: {step+1}/{len(episode)}") + obs, act, reward, info = episode[step] + + total_reward += reward + batch = ds[i] + + num_images = len(obs['color']) + fig, axs = plt.subplots(2, num_images+1, figsize=(15, 6)) + for n in range(num_images): + axs[1, n].imshow(obs['color'][n]) + axs[1, n].set_title(f'Raw RGB {n+1}') + + axs[0, n].imshow(obs['depth'][n]) + axs[0, n].set_title(f'Raw Depth {n+1}') + + color_sums.append(np.mean(obs['color'][0], axis=(0,1)) / 255.0) + depth_sums.append(np.mean(obs['depth'][0], axis=(0,1))) + + cam_config = None + if b'camera_info' in info: + cam_config = ds.get_cam_config(info[b'camera_info']) + + img_depth = ds.get_image(obs, cam_config=cam_config) + img_tensor = torch.from_numpy(img_depth) + img = np.uint8(img_tensor.detach().cpu().numpy()) + img = img.transpose(1,0,2) + + if step < len(episode)-1 and episode[step]: + batch = ds.process_sample(episode[step], augment=augment) + else: + batch = ds.process_goal(episode[step], perturb_params=None) + + img_sample = batch['img'] + img_sample = torch.from_numpy(img_sample) + color = np.uint8(img_sample.detach().cpu().numpy())[:,:,:3] + color = color.transpose(1,0,2) + depth = np.array(img_sample.detach().cpu().numpy())[:,:,3] + depth = depth.transpose(1,0) + + axs[0, num_images].imshow(depth) + axs[0, num_images].set_title('Depth') + + axs[1,num_images].imshow(color) + axs[1,num_images].set_title('RGB + Oracle Pick & Place') + + if act and step < len(episode)-1: + p0 = batch['p0'] + p1 = batch['p1'] + p0_theta = batch['p0_theta'] + p1_theta = batch['p1_theta'] + p0_theta + + pick = p0 + place = p1 + + line_len = 30 + pick0 = (pick[0] + line_len/2.0 * np.sin(p0_theta), pick[1] + line_len/2.0 * np.cos(p0_theta)) + pick1 = (pick[0] - line_len/2.0 * np.sin(p0_theta), pick[1] - line_len/2.0 * np.cos(p0_theta)) + axs[1,num_images].plot((pick1[0], pick0[0]), (pick1[1], pick0[1]), color='r', linewidth=2) + + place0 = (place[0] + line_len/2.0 * np.sin(p1_theta), place[1] + line_len/2.0 * np.cos(p1_theta)) + place1 = (place[0] - line_len/2.0 * np.sin(p1_theta), place[1] - line_len/2.0 * np.cos(p1_theta)) + axs[1,num_images].plot((place1[0], place0[0]), (place1[1], place0[1]), color='g', linewidth=2) + + c_pick = plt.Circle(pick, 3, color='r', fill=False) + c_place = plt.Circle(place, 3, color='g', fill=False) + + axs[1,num_images].add_patch(c_pick) + axs[1,num_images].add_patch(c_place) + + plt.show() + + print(f"Language Goal: {batch['lang_goal']}") + print(f"Step Reward: {reward}") + print(f"Total Reward: {total_reward}") + + print(f"Done, Total Reward: {total_reward}") + +print("\n\nDataset Statistics: ") +print(f"Color Mean: {np.mean(color_sums, axis=0)}, Std: {np.std(color_sums, axis=0)}") +print(f"Depth Mean: {np.mean(depth_sums, axis=0)}, Std: {np.std(depth_sums, axis=0)}") +print(f"Total Image-Action Pairs: {total_images}") \ No newline at end of file diff --git a/notebooks/print_results.py b/notebooks/print_results.py new file mode 100644 index 0000000000000000000000000000000000000000..6ea11091fc06280f613b073f4ffe09382c1141c9 --- /dev/null +++ b/notebooks/print_results.py @@ -0,0 +1,145 @@ +import os +import sys +import json + +from cliport import agents +from cliport import tasks +import argparse +import datetime +import matplotlib as mpl + +mpl.use("Agg") +import argparse +import os +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +import matplotlib +import IPython +import numpy as np +font = { + "size": 22, +} +matplotlib.rc("font", **font) +sns.set_context("paper", font_scale=2.0) + + +def mkdir_if_missing(dst_dir): + if not os.path.exists(dst_dir): + os.makedirs(dst_dir) + + +def save_figure(name, title=""): + print(f"output/output_figures/{name}.png") + if len(title) > 0: + plt.title(title) + plt.tight_layout() + mkdir_if_missing(f"output/output_figures/{name}") + plt.savefig(f"output/output_figures/{name}/output.png") + plt.clf() + + +def print_and_write(file_handle, text): + print(text) + if file_handle is not None: + file_handle.write(text + "\n") + return text + +parser = argparse.ArgumentParser() + +# federated arguments (Notation for the arguments followed from paper) +parser.add_argument( + "--results", "-r", type=str, default="exps/exps-singletask" +) +parser.add_argument( + "--single", "-s", action="store_true", default=False +) +args = parser.parse_args() + +root_folder = os.environ['GENSIM_ROOT'] +exp_folder = os.path.join(root_folder, args.results) # replace 'cliport_quickstart' with your exps folder + + +mkdir_if_missing('output/output_figures') +mkdir_if_missing('output/cliport_output') +mkdir_if_missing('output/output_stat') + + + +output_stat_file = os.path.join('output/', 'cliport_output/', 'cliport-training.txt') +mkdir_if_missing('output/cliport_output/') +file_handle = open(output_stat_file, 'a+') + +tasks_list = list(tasks.names.keys()) +agents_list = list(agents.names.keys()) +demos_list = [1, 5, 10, 20, 30, 50, 100, 200, 1000] # 100, + +results = {} +for t in tasks_list: + for a in agents_list: + for d in demos_list: + task_folder = f'{t}-{a}-n{d}-train' + task_folder_path = os.path.join(exp_folder, task_folder, 'checkpoints') + + if os.path.exists(task_folder_path): + print(f"train {task_folder_path}") + + jsons = [f for f in os.listdir(task_folder_path) if '.json' in f] + for j in jsons: + model_type = 'multi' if 'multi' in j else 'single' + eval_type = 'val' if 'val' in j else 'test' + + with open(os.path.join(task_folder_path, j)) as f: + res = json.load(f) + + results[f'{t}-{a}-n{d}-{model_type}-{eval_type}'] = res + +dt_string = datetime.datetime.now().strftime("%d_%m_%Y_%H:%M:%S") +print_and_write(file_handle, f"==========================={dt_string}=========================\n") +print_and_write(file_handle, f'Experiments folder: {exp_folder}\n') + +data = {'task': [], 'success': []} + +for eval_type in ['val', 'test']: + print_and_write(file_handle, f'----- {eval_type.upper()} -----\n') + for t in tasks_list: + for a in agents_list: + for d in demos_list: + for model_type in ['single', 'multi']: + eval_key = f'{t}-{a}-n{d}-{model_type}-{eval_type}' + + if eval_key in results: + print_and_write(file_handle, f'{eval_key} {t} | Train Demos: {d}') + res = results[eval_key] + best_score, best_ckpt = max(zip([v['mean_reward'] for v in list(res.values())], res.keys())) + # TODO: test that this works for full results folder + + print_and_write(file_handle, f'\t{best_score*100:1.1f} : {a} | {model_type}\n') + data['task'].append(t) + data['success'].append(best_score) + +data['task'].append("Average") +data['success'].append(np.mean(data["success"])) + + +# make figure as well for sinle expeirment results +dfs = [] +suffix = "" +run_num = 0 +df = pd.DataFrame.from_dict(data) +title = args.results + "_res" + +# rewards +fig, ax = plt.subplots(figsize=(16, 8)) +sns_plot = sns.barplot( + data=df, x="task", y="success", errorbar=("sd", 1), palette="deep" +) + +# label texts +for container in ax.containers: + ax.bar_label(container, label_type="center", fontsize="x-large", fmt="%.2f") +# ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha="right") +ax.set_xticklabels(['\n'.join(str(xlabel.get_text()).split("-")) for xlabel in ax.get_xticklabels()]) + +# save plot +save_figure(f"{title}", title) diff --git a/notebooks/real_affordance.py b/notebooks/real_affordance.py new file mode 100644 index 0000000000000000000000000000000000000000..6a02cd5300dc6a194f96efedf423aa33a35a9f7e --- /dev/null +++ b/notebooks/real_affordance.py @@ -0,0 +1,257 @@ +import os +import sys +import json + +import numpy as np +from cliport import tasks +from cliport import agents +from cliport.utils import utils + +import torch +import cv2 +from cliport.dataset import RavensDataset +from cliport.environments.environment import Environment +from torch.utils.data import DataLoader +import IPython + +import matplotlib +import numpy as np +import matplotlib.pyplot as plt + + +train_demos = 50 # number training demonstrations used to train agent +n_eval = 1 # number of evaluation instances +mode = 'test' # val or test + +agent_name = 'cliport' +model_task = 'place-red-in-green' # multi-task agent conditioned with language goals +task_type = 'cliport3_task_indomain' # cliport3_task_indomain, gpt5_mixcliport2 +# model_folder = f'exps/exp-{task_type}_demo{train_demos}_2023-07-27_13-30-52-small' # path to pre-trained checkpoint + +# Lirui +model_folder = f'exps-singletask/debug_checkpoints' # path to pre-trained checkpoint +ckpt_name = 'last.ckpt' # name of checkpoint to load + +draw_grasp_lines = True +affordance_heatmap_scale = 30 + +### Uncomment the task you want to evaluate on ### +# eval_task = 'align-rope' +# eval_task = 'assembling-kits-seq-seen-colors' +# eval_task = 'assembling-kits-seq-unseen-colors' +# eval_task = 'packing-shapes' +# eval_task = 'packing-boxes-pairs-seen-colors' +# eval_task = 'packing-boxes-pairs-unseen-colors' +# eval_task = 'packing-seen-google-objects-seq' +# eval_task = 'packing-unseen-google-objects-seq' +# eval_task = 'packing-seen-google-objects-group' +# eval_task = 'packing-unseen-google-objects-group' +# eval_task = 'put-block-in-bowl-seen-colors' +# eval_task = 'put-block-in-bowl-unseen-colors' +eval_task = 'place-red-in-green' +# eval_task = 'stack-block-pyramid-seq-unseen-colors' +# eval_task = 'separating-piles-seen-colors' +# eval_task = 'separating-piles-unseen-colors' +# eval_task = 'towers-of-hanoi-seq-seen-colors' +# eval_task = 'towers-of-hanoi-seq-unseen-colors' + + + +def crop_img(img, height_range=[200, 340], width_range=[180, 460]): + img = img[height_range[0]:height_range[1], width_range[0]:width_range[1], :] + return img + +def read_rgb_image(path): + img = cv2.imread(path) + img = crop_img(img) + img = cv2.resize(img, (320, 160)) + img = img.transpose(1, 0, 2) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + return img + +def read_depth_image(path): + # TODO: why the depth image has 4 channels ? + img = plt.imread(path, cv2.IMREAD_UNCHANGED) # TODO: need correct + img = crop_img(img) + img = cv2.resize(img, (320, 160))[:, :, 0][:, :, None] + img = img.transpose(1, 0, 2) + return img + +def process_real_sample(cmap, dmap, info, aug_theta_sigma=60, augment=False): + """Process the sample like the dataset method.""" + print(cmap.shape, dmap.shape) + img = np.concatenate((cmap, dmap, dmap, dmap), axis=2) + p0, p1 = np.zeros(1), np.zeros(1) + p0_theta, p1_theta = np.zeros(1), np.zeros(1) + perturb_params = np.zeros(5) + if augment: + img, _, (p0, p1), perturb_params = utils.perturb(img, [p0, p1], theta_sigma=aug_theta_sigma) + + sample = { + 'img': img.copy(), + 'p0': np.array(p0).copy(), 'p0_theta': np.array(p0_theta).copy(), + 'p1': np.array(p1).copy(), 'p1_theta': np.array(p1_theta).copy() , + 'perturb_params': np.array(perturb_params).copy() + } + + if info and 'lang_goal' in info: + sample['lang_goal'] = info['lang_goal'] + + return sample + + +def plot_affordance(batch, obs, agent, info, draw_grasp_lines=True, affordance_heatmap_scale=30): + + fig, axs = plt.subplots(2, 2, figsize=(13, 7)) + + # Get color and depth inputs + img = batch['img'] # (320, 160, 6) + img = torch.from_numpy(img) + color = np.uint8(img.detach().cpu().numpy())[:,:,:3] + color = color.transpose(1,0,2) + depth = np.array(img.detach().cpu().numpy())[:,:,3] + depth = depth.transpose(1,0) + + # Display input color + axs[0,0].imshow(color) + axs[0,0].axes.xaxis.set_visible(False) + axs[0,0].axes.yaxis.set_visible(False) + axs[0,0].set_title('Input RGB') + + # Display input depth + axs[0,1].imshow(depth) + axs[0,1].axes.xaxis.set_visible(False) + axs[0,1].axes.yaxis.set_visible(False) + axs[0,1].set_title('Input Depth') + + # Display predicted pick affordance + axs[1,0].imshow(color) + axs[1,0].axes.xaxis.set_visible(False) + axs[1,0].axes.yaxis.set_visible(False) + axs[1,0].set_title('Pick Affordance') + + # Display predicted place affordance + axs[1,1].imshow(color) + axs[1,1].axes.xaxis.set_visible(False) + axs[1,1].axes.yaxis.set_visible(False) + axs[1,1].set_title('Place Affordance') + + # Get action predictions + l = str(info['lang_goal']) + act = agent.real_act(obs, info, goal=None) + pick, place = act['pick'], act['place'] + + # Visualize pick affordance + pick_inp = {'inp_img': batch['img'], 'lang_goal': l} + pick_conf = agent.attn_forward(pick_inp)[0] + print("pick_conf:", pick_conf.shape, pick, place) + # IPython.embed() + logits = pick_conf.detach().cpu().numpy() + + pick_conf = pick_conf.detach().cpu().numpy() + argmax = np.argmax(pick_conf) + argmax = np.unravel_index(argmax, shape=pick_conf.shape) + p0 = argmax[:2] + + p0_theta = (argmax[2] * (2 * np.pi / pick_conf.shape[2])) * -1.0 + + line_len = 30 + pick0 = (pick[0] + line_len/2.0 * np.sin(p0_theta), pick[1] + line_len/2.0 * np.cos(p0_theta)) + pick1 = (pick[0] - line_len/2.0 * np.sin(p0_theta), pick[1] - line_len/2.0 * np.cos(p0_theta)) + + if draw_grasp_lines: + axs[1,0].plot((pick1[0], pick0[0]), (pick1[1], pick0[1]), color='r', linewidth=1) + + # Visualize place affordance + place_inp = {'inp_img': batch['img'], 'p0': pick, 'lang_goal': l} + place_conf = agent.trans_forward(place_inp)[0] + + place_conf = place_conf.permute(1, 2, 0) + place_conf = place_conf.detach().cpu().numpy() + argmax = np.argmax(place_conf) + argmax = np.unravel_index(argmax, shape=place_conf.shape) + p1_pix = argmax[:2] + p1_theta = (argmax[2] * (2 * np.pi / place_conf.shape[2]) + p0_theta) * -1.0 + + line_len = 30 + place0 = (place[0] + line_len/2.0 * np.sin(p1_theta), place[1] + line_len/2.0 * np.cos(p1_theta)) + place1 = (place[0] - line_len/2.0 * np.sin(p1_theta), place[1] - line_len/2.0 * np.cos(p1_theta)) + + if draw_grasp_lines: + axs[1,1].plot((place1[0], place0[0]), (place1[1], place0[1]), color='g', linewidth=1) + + # Overlay affordances on RGB input + pick_logits_disp = np.uint8(logits * 255 * affordance_heatmap_scale).transpose(2,1,0) + place_logits_disp = np.uint8(np.sum(place_conf, axis=2)[:,:,None] * 255 * affordance_heatmap_scale).transpose(1,0,2)# .transpose(1,2,0) + + pick_logits_disp_masked = np.ma.masked_where(pick_logits_disp < 0, pick_logits_disp) + place_logits_disp_masked = np.ma.masked_where(place_logits_disp < 0, place_logits_disp) + # IPython.embed() + + axs[1][0].imshow(pick_logits_disp_masked, alpha=0.75) + axs[1][1].imshow(place_logits_disp_masked, cmap='viridis', alpha=0.75) + + print(f"Lang Goal: {str(info['lang_goal'])}") + plt.savefig(f'{root_dir}/data/real_output/test_real_affordance2.png') + + +if __name__ == '__main__': + os.environ['GENSIM_ROOT'] = f'{os.path.abspath(__file__)}/../..' + root_dir = os.environ['GENSIM_ROOT'] + print("root_dir:", root_dir) + assets_root = os.path.join(root_dir, 'cliport/environments/assets/') + config_file = 'eval.yaml' + + vcfg = utils.load_hydra_config(os.path.join(root_dir, f'cliport/cfg/{config_file}')) + vcfg['data_dir'] = os.path.join(root_dir, 'data') + vcfg['mode'] = mode + + vcfg['model_task'] = model_task + vcfg['eval_task'] = eval_task + vcfg['agent'] = agent_name + + # Model and training config paths + model_path = os.path.join(root_dir, model_folder) + if model_folder[-7:] == 'smaller': + vcfg['train_config'] = f"{model_path}/{model_folder[9:-8]}-{vcfg['agent']}-n{train_demos}-train/.hydra/config.yaml" + vcfg['model_path'] = f"{model_path}/{model_folder[9:-8]}-{vcfg['agent']}-n{train_demos}-train/checkpoints/" + else: + vcfg['train_config'] = f"{model_path}/{model_folder[9:-6]}-{vcfg['agent']}-n{train_demos}-train/.hydra/config.yaml" + vcfg['model_path'] = f"{model_path}/{model_folder[9:-6]}-{vcfg['agent']}-n{train_demos}-train/checkpoints/" + tcfg = utils.load_hydra_config(vcfg['train_config']) + + # Load dataset + ds = RavensDataset(os.path.join(vcfg['data_dir'], f'{vcfg["eval_task"]}-{vcfg["mode"]}'), + tcfg, + n_demos=n_eval, + augment=False) + + eval_run = 0 + name = '{}-{}-{}-{}'.format(vcfg['eval_task'], vcfg['agent'], n_eval, eval_run) + print(f'\nEval ID: {name}\n') + + # Initialize agent + utils.set_seed(eval_run, torch=True) + agent = agents.names[vcfg['agent']](name, tcfg, DataLoader(ds), DataLoader(ds)) + + # Load checkpoint + ckpt_path = os.path.join(vcfg['model_path'], ckpt_name) + print(f'\nLoading checkpoint: {ckpt_path}') + agent.load(ckpt_path) + + os.makedirs(f'{root_dir}/data/real_output', exist_ok=True) + real_rgb_img = read_rgb_image(f'{root_dir}/data/real_imgs/rgb0.png') + plt.imshow(real_rgb_img[:, :, :3]) + plt.axis('off') + plt.savefig(f'{root_dir}/data/real_output/real_show.png') + real_depth_img = read_depth_image(f'{root_dir}/data/real_imgs/depth0.png') + print(real_depth_img.shape, real_rgb_img.shape) + plt.imshow(real_depth_img, cmap='gray') + plt.savefig(f'{root_dir}/data/real_output/real_depth.png') + info = {} + info['lang_goal'] = 'place red block in green bowl' + + batch = process_real_sample(real_rgb_img, real_depth_img, info, augment=False) + + obs = batch['img'] + plot_affordance(batch, obs, agent, info, draw_grasp_lines=draw_grasp_lines, affordance_heatmap_scale=affordance_heatmap_scale) \ No newline at end of file diff --git a/notebooks/results.ipynb b/notebooks/results.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..fe5484746d51576167573a8b32ad166c59d1f077 --- /dev/null +++ b/notebooks/results.ipynb @@ -0,0 +1,175 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8f4d6efb", + "metadata": {}, + "source": [ + "# Results\n", + "\n", + "This notebook gathers results from evaluation JSON files and prints them as a list. \n", + "\n", + "### Setup\n", + "\n", + "- Set the root folder environment variable with `export CLIPORT_ROOT=`\n", + "- Train and evaluate agents by following the [README guide](https://github.com/cliport/cliport#single-task-training--evaluation)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d072ae18", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pybullet build time: Aug 16 2021 17:58:31\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "import json\n", + "\n", + "from cliport import agents\n", + "from cliport import tasks" + ] + }, + { + "cell_type": "markdown", + "id": "e2ee3b65", + "metadata": {}, + "source": [ + "### Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "95c14026", + "metadata": {}, + "outputs": [], + "source": [ + "root_folder = os.environ['CLIPORT_ROOT']\n", + "exp_folder = os.path.join(root_folder, 'cliport_quickstart') # replace 'cliport_quickstart' with your exps folder" + ] + }, + { + "cell_type": "markdown", + "id": "2627285a", + "metadata": {}, + "source": [ + "### Gather JSON Results" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6f5186e1", + "metadata": {}, + "outputs": [], + "source": [ + "tasks_list = list(tasks.names.keys())\n", + "agents_list = list(agents.names.keys())\n", + "demos_list = [1, 10, 100, 1000]\n", + "\n", + "results = {}\n", + "for t in tasks_list:\n", + " for a in agents_list:\n", + " for d in demos_list:\n", + " task_folder = f'{t}-{a}-n{d}-train'\n", + " task_folder_path = os.path.join(exp_folder, task_folder, 'checkpoints')\n", + "\n", + " if os.path.exists(task_folder_path):\n", + " jsons = [f for f in os.listdir(task_folder_path) if '.json' in f]\n", + " for j in jsons:\n", + " model_type = 'multi' if 'multi' in j else 'single'\n", + " eval_type = 'val' if 'val' in j else 'test'\n", + " \n", + " with open(os.path.join(task_folder_path, j)) as f:\n", + " res = json.load(f)\n", + " \n", + " results[f'{t}-{a}-n{d}-{model_type}-{eval_type}'] = res" + ] + }, + { + "cell_type": "markdown", + "id": "0b6fcfa9", + "metadata": {}, + "source": [ + "### Print Results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2554998c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Experiments folder: /home/mshr/cliport/cliport_quickstart\n", + "\n", + "----- VAL -----\n", + "\n", + "stack-block-pyramid-seq-seen-colors | Train Demos: 1000\n", + "\t97.3 : cliport | multi\n", + "\n", + "----- TEST -----\n", + "\n", + "stack-block-pyramid-seq-seen-colors | Train Demos: 1000\n", + "\t96.5 : cliport | multi\n", + "\n" + ] + } + ], + "source": [ + "print(f'Experiments folder: {exp_folder}\\n')\n", + "\n", + "for eval_type in ['val', 'test']:\n", + " print(f'----- {eval_type.upper()} -----\\n')\n", + " for t in tasks_list:\n", + " for a in agents_list:\n", + " for d in demos_list:\n", + " for model_type in ['single', 'multi']:\n", + " eval_key = f'{t}-{a}-n{d}-{model_type}-{eval_type}'\n", + " \n", + " if eval_key in results: \n", + " print(f'{t} | Train Demos: {d}')\n", + " \n", + " res = results[eval_key]\n", + " best_score, best_ckpt = max(zip([v['mean_reward'] for v in list(res.values())], \n", + " res.keys())) # TODO: test that this works for full results folder\n", + " \n", + " print(f'\\t{best_score*100:1.1f} : {a} | {model_type}\\n')\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/prompts/bottomup_task_generation_prompt/cliport_prompt_api_template.txt b/prompts/bottomup_task_generation_prompt/cliport_prompt_api_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..109662809a0913cb94f00960b2206f2961c82850 --- /dev/null +++ b/prompts/bottomup_task_generation_prompt/cliport_prompt_api_template.txt @@ -0,0 +1,356 @@ +Before writing the code for the task "TASK_NAME_TEMPLATE". Here are some APIs that are defined. Please confirm that you understand these APIs. + +""" +class Task(): + """Base Task class.""" + + def __init__(self): + self.ee = Suction + self.mode = 'train' + self.sixdof = False + self.primitive = primitives.PickPlace() + self.oracle_cams = cameras.Oracle.CONFIG + + # Evaluation epsilons (for pose evaluation metric). + self.pos_eps = 0.01 + self.rot_eps = np.deg2rad(15) + + # Workspace bounds. + self.pix_size = 0.003125 + self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]]) + self.zone_bounds = np.copy(self.bounds) + + self.goals = [] + self.lang_goals = [] + self.task_completed_desc = "task completed." + self.progress = 0 + self._rewards = 0 + self.assets_root = None + + def reset(self, env): + if not self.assets_root: + raise ValueError('assets_root must be set for task, ' + 'call set_assets_root().') + self.goals = [] + self.lang_goals = [] + self.progress = 0 # Task progression metric in range [0, 1]. + self._rewards = 0 # Cumulative returned rewards. + + # ------------------------------------------------------------------------- + # Oracle Agent + # ------------------------------------------------------------------------- + + def oracle(self, env): + """Oracle agent.""" + OracleAgent = collections.namedtuple('OracleAgent', ['act']) + + def act(obs, info): + """Calculate action.""" + + # Oracle uses perfect RGB-D orthographic images and segmentation masks. + _, hmap, obj_mask = self.get_true_image(env) + + # Unpack next goal step. + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] + + # Match objects to targets without replacement. + if not replace: + + # Modify a copy of the match matrix. + matches = matches.copy() + + # Ignore already matched objects. + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + if self.is_match(pose, targs[j], symmetry): + matches[i, :] = 0 + matches[:, j] = 0 + + # Get objects to be picked (prioritize farthest from nearest neighbor). + nn_dists = [] + nn_targets = [] + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + xyz, _ = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + if len(targets_i) > 0: + targets_xyz = np.float32([targs[j][0] for j in targets_i]) + dists = np.linalg.norm( + targets_xyz - np.float32(xyz).reshape(1, 3), axis=1) + nn = np.argmin(dists) + nn_dists.append(dists[nn]) + nn_targets.append(targets_i[nn]) + + # Handle ignored objects. + else: + nn_dists.append(0) + nn_targets.append(-1) + order = np.argsort(nn_dists)[::-1] + + # Filter out matched objects. + order = [i for i in order if nn_dists[i] > 0] + + pick_mask = None + for pick_i in order: + pick_mask = np.uint8(obj_mask == objs[pick_i][0]) + + # Erode to avoid picking on edges. + # pick_mask = cv2.erode(pick_mask, np.ones((3, 3), np.uint8)) + + if np.sum(pick_mask) > 0: + break + + # Trigger task reset if no object is visible. + if pick_mask is None or np.sum(pick_mask) == 0: + self.goals = [] + self.lang_goals = [] + print('Object for pick is not visible. Skipping demonstration.') + return + + # Get picking pose. + pick_prob = np.float32(pick_mask) + pick_pix = utils.sample_distribution(pick_prob) + # For "deterministic" demonstrations on insertion-easy, use this: + # pick_pix = (160,80) + pick_pos = utils.pix_to_xyz(pick_pix, hmap, + self.bounds, self.pix_size) + pick_pose = (np.asarray(pick_pos), np.asarray((0, 0, 0, 1))) + + # Get placing pose. + targ_pose = targs[nn_targets[pick_i]] + obj_pose = p.getBasePositionAndOrientation(objs[pick_i][0]) + if not self.sixdof: + obj_euler = utils.quatXYZW_to_eulerXYZ(obj_pose[1]) + obj_quat = utils.eulerXYZ_to_quatXYZW((0, 0, obj_euler[2])) + obj_pose = (obj_pose[0], obj_quat) + world_to_pick = utils.invert(pick_pose) + obj_to_pick = utils.multiply(world_to_pick, obj_pose) + pick_to_obj = utils.invert(obj_to_pick) + place_pose = utils.multiply(targ_pose, pick_to_obj) + + # Rotate end effector? + if not rotations: + place_pose = (place_pose[0], (0, 0, 0, 1)) + + place_pose = (np.asarray(place_pose[0]), np.asarray(place_pose[1])) + + return {'pose0': pick_pose, 'pose1': place_pose} + + return OracleAgent(act) + + # ------------------------------------------------------------------------- + # Reward Function and Task Completion Metrics + # ------------------------------------------------------------------------- + + def reward(self): + """Get delta rewards for current timestep. + + Returns: + A tuple consisting of the scalar (delta) reward, plus `extras` + dict which has extra task-dependent info from the process of + computing rewards that gives us finer-grained details. Use + `extras` for further data analysis. + """ + reward, info = 0, {} + + # Unpack next goal step. + objs, matches, targs, _, _, metric, params, max_reward = self.goals[0] + + # Evaluate by matching object poses. + if metric == 'pose': + step_reward = 0 + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + target_pose = targs[j] + if self.is_match(pose, target_pose, symmetry): + step_reward += max_reward / len(objs) + print(f"object {i} match with target {j} rew: {step_reward}") + break + + # Evaluate by measuring object intersection with zone. + elif metric == 'zone': + zone_pts, total_pts = 0, 0 + obj_pts, zones = params + for zone_idx, (zone_pose, zone_size) in enumerate(zones): + + # Count valid points in zone. + for obj_idx, obj_id in enumerate(obj_pts): + pts = obj_pts[obj_id] + obj_pose = p.getBasePositionAndOrientation(obj_id) + world_to_zone = utils.invert(zone_pose) + obj_to_zone = utils.multiply(world_to_zone, obj_pose) + pts = np.float32(utils.apply(obj_to_zone, pts)) + if len(zone_size) > 1: + valid_pts = np.logical_and.reduce([ + pts[0, :] > -zone_size[0] / 2, pts[0, :] < zone_size[0] / 2, + pts[1, :] > -zone_size[1] / 2, pts[1, :] < zone_size[1] / 2, + pts[2, :] < self.zone_bounds[2, 1]]) + + # if zone_idx == matches[obj_idx].argmax(): + zone_pts += np.sum(np.float32(valid_pts)) + total_pts += pts.shape[1] + step_reward = max_reward * (zone_pts / total_pts) + + # Get cumulative rewards and return delta. + reward = self.progress + step_reward - self._rewards + self._rewards = self.progress + step_reward + + # Move to next goal step if current goal step is complete. + if np.abs(max_reward - step_reward) < 0.01: + self.progress += max_reward # Update task progress. + self.goals.pop(0) + if len(self.lang_goals) > 0: + self.lang_goals.pop(0) + + return reward, info + + def done(self): + """Check if the task is done or has failed. + + Returns: + True if the episode should be considered a success, which we + use for measuring successes, which is particularly helpful for tasks + where one may get successes on the very last time step, e.g., getting + the cloth coverage threshold on the last alllowed action. + However, for bag-items-easy and bag-items-hard (which use the + 'bag-items' metric), it may be necessary to filter out demos that did + not attain sufficiently high reward in external code. Currently, this + is done in `main.py` and its ignore_this_demo() method. + """ + return (len(self.goals) == 0) or (self._rewards > 0.99) + # return zone_done or defs_done or goal_done + + # ------------------------------------------------------------------------- + # Environment Helper Functions + # ------------------------------------------------------------------------- + + def is_match(self, pose0, pose1, symmetry): + """Check if pose0 and pose1 match within a threshold.""" + + # Get translational error. + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) + dist_pos = np.linalg.norm(diff_pos) + + # Get rotational error around z-axis (account for symmetries). + diff_rot = 0 + if symmetry > 0: + rot0 = np.array(utils.quatXYZW_to_eulerXYZ(pose0[1]))[2] + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + diff_rot = np.abs(rot0 - rot1) % symmetry + if diff_rot > (symmetry / 2): + diff_rot = symmetry - diff_rot + + return (dist_pos < self.pos_eps) and (diff_rot < self.rot_eps) + + def get_random_pose(self, env, obj_size): + """Get random collision-free object pose within workspace bounds.""" + + # Get erosion size of object in pixels. + max_size = np.sqrt(obj_size[0] ** 2 + obj_size[1] ** 2) + erode_size = int(np.round(max_size / self.pix_size)) + + _, hmap, obj_mask = self.get_true_image(env) + + # Randomly sample an object pose within free-space pixels. + free = np.ones(obj_mask.shape, dtype=np.uint8) + for obj_ids in env.obj_ids.values(): + for obj_id in obj_ids: + free[obj_mask == obj_id] = 0 + free[0, :], free[:, 0], free[-1, :], free[:, -1] = 0, 0, 0, 0 + free = cv2.erode(free, np.ones((erode_size, erode_size), np.uint8)) + + # if np.sum(free) == 0: + # return None, None + + if np.sum(free) == 0: + # avoid returning None, None + # return None, None + pix = (obj_mask.shape[0] // 2, obj_mask.shape[1] // 2) + else: + pix = utils.sample_distribution(np.float32(free)) + pos = utils.pix_to_xyz(pix, hmap, self.bounds, self.pix_size) + pos = (pos[0], pos[1], obj_size[2] / 2) + theta = np.random.rand() * 2 * np.pi + rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + return pos, rot + + def get_lang_goal(self): + if len(self.lang_goals) == 0: + return self.task_completed_desc + else: + return self.lang_goals[0] + + def get_reward(self): + return float(self._rewards) + + # ------------------------------------------------------------------------- + # Helper Functions + # ------------------------------------------------------------------------- + + def fill_template(self, template, replace): + """Read a file and replace key strings.""" + full_template_path = os.path.join(self.assets_root, template) + with open(full_template_path, 'r') as file: + fdata = file.read() + for field in replace: + for i in range(len(replace[field])): + fdata = fdata.replace(f'{field}{i}', str(replace[field][i])) + alphabet = string.ascii_lowercase + string.digits + rname = ''.join(random.choices(alphabet, k=16)) + tmpdir = tempfile.gettempdir() + template_filename = os.path.split(template)[-1] + fname = os.path.join(tmpdir, f'{template_filename}.{rname}') + with open(fname, 'w') as file: + file.write(fdata) + return fname + + def get_random_size(self, min_x, max_x, min_y, max_y, min_z, max_z): + """Get random box size.""" + size = np.random.rand(3) + size[0] = size[0] * (max_x - min_x) + min_x + size[1] = size[1] * (max_y - min_y) + min_y + size[2] = size[2] * (max_z - min_z) + min_z + return tuple(size) + + def color_random_brown(self, obj): + shade = np.random.rand() + 0.5 + color = np.float32([shade * 156, shade * 117, shade * 95, 255]) / 255 + p.changeVisualShape(obj, -1, rgbaColor=color) + + """"" + + # Environment Class + def add_object(self, urdf, pose, category='rigid'): + """List of (fixed, rigid, or deformable) objects in env.""" + fixed_base = 1 if category == 'fixed' else 0 + obj_id = pybullet_utils.load_urdf( + p, + os.path.join(self.assets_root, urdf), + pose[0], + pose[1], + useFixedBase=fixed_base) + self.obj_ids[category].append(obj_id) + return obj_id +""" + +========= +Note that the objects need to obey physics and not collide with each other, and the object goal poses need to be above the table with lower bound x=0.25, y=-0.5 and upper bound x=0.75, y=0.5. When there are multiple objects for a multi-step pick-and-place task, there are often multiple subgoals. Once the task and environment are generated, an agent with a pick and place primitive will follow the defined goal to accomplish the tasks. + +Additionally, make sure you understand and summarize the ``self.goals`` variables, which has a list of 8-tuple with (objs, matches, targ_poses, replace, rotations, metric, params, step_max_reward, symmetries). +- objs (List of obj_id): object ID. +- matches (Binary Matrix): a binary matrix that denotes which object is matched with which target. This matrix has dimension len(objs) x len(targs). +- targ_poses (List of Poses [(translation, rotation)] ): a list of target poses of tuple (translation, rotation). +- replace (Boolean): whether each object can match with one unique target. This is important if we have one target and multiple objects. If it's set to be false, then any object matching with the target will satisfy. +- rotations (Boolean): whether the placement action has a rotation degree of freedom. +- metric (`pose` or `zone`): `pose` or `zone` that the object needs to be transported to. Example: `pose`. +- params (List of (zone_target, zone_size)): a list of (zone_target, zone_size) for each zone if the metric is `zone`. +- step_max_reward (float): the total reward of matching all the objects with all the target poses. It is not dependent on the number of objects but dependent on the number of goals. +- symmetries: the radians that the object is symmetric around z axis. +- language_goal: the low-level language instructions that denote the goal of this step. + diff --git a/prompts/bottomup_task_generation_prompt/cliport_prompt_code_candidate_template.txt b/prompts/bottomup_task_generation_prompt/cliport_prompt_code_candidate_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..ff2bce8375fea33af61f0beb0279439572dfa9c4 --- /dev/null +++ b/prompts/bottomup_task_generation_prompt/cliport_prompt_code_candidate_template.txt @@ -0,0 +1,16 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + +""" +TASK_CODE_REFERENCE_TEMPLATE +""" + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. +Note: +1. Use `make_piles` to create piles of small blocks. +2. Use `make_ropes` to create cables. +3. Us `self.primitive = primitives.push` and `self.ee = Spatula` to use spatula. +4. Do not use random pose or the initial pose for the target poses. Come up with the specified pose. Especially for building tasks, use a consistent `anchor_pose` and use `self.add_corner_anchor_for_pose(env, anchor_pose)` to add an anchor there. +5. Do not use target poses that are not in the workspace bound `bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]])` + + +For each object, try to describe its color, size, category in the task first before you write the code. Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/bottomup_task_generation_prompt/cliport_prompt_code_reference_selection_template.txt b/prompts/bottomup_task_generation_prompt/cliport_prompt_code_reference_selection_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..ebcf4a9a238b088952a88973335b664791c25100 --- /dev/null +++ b/prompts/bottomup_task_generation_prompt/cliport_prompt_code_reference_selection_template.txt @@ -0,0 +1,7 @@ +Now I will provide you some reference code that might help you can write the code for the task "TASK_STRING_TEMPLATE". + + +TASK_CODE_LIST_TEMPLATE + + +Please pick 4 task python files that you would like to use as reference. Format them in a python list. diff --git a/prompts/bottomup_task_generation_prompt/cliport_prompt_code_split_template.txt b/prompts/bottomup_task_generation_prompt/cliport_prompt_code_split_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf5c20cbf04444d9b7296fadf58bb6f49531b364 --- /dev/null +++ b/prompts/bottomup_task_generation_prompt/cliport_prompt_code_split_template.txt @@ -0,0 +1,247 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Use functions `make_piles` and `make_ropes` for creating piles and cables. Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/bottomup_task_generation_prompt/cliport_prompt_common_errors_template.txt b/prompts/bottomup_task_generation_prompt/cliport_prompt_common_errors_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..098bbead4261558ef07f44ef550e42cbb01ed7e2 --- /dev/null +++ b/prompts/bottomup_task_generation_prompt/cliport_prompt_common_errors_template.txt @@ -0,0 +1,102 @@ +Before writing the code for the task "TASK_NAME_TEMPLATE". Here are some runtime errors that you do not want to make. Please confirm that you understand these runtime errors. + +""" +- environment.py, line 338, in info + pos, rot = p.getBasePositionAndOrientation(obj_id) +TypeError: an integer is required (got type NoneType) + +- task.py, line 118, in act + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] +IndexError: list index out of range + +- task.py, line 308, in is_match + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) +TypeError: 'float' object is not subscriptable + +- task.py", line 315, in is_match + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + +- utils.py", line 280, in quatXYZW_to_eulerXYZ + quaternion_wxyz = np.array([q[3], q[0], q[1], q[2]]) +IndexError: tuple index out of range + +- pallet_pose = self.get_random_pose(env, pallet_size) +pallet_surface_height = pallet_pose[0][2] +TypeError: 'NoneType' object is not subscriptable + +- No such file or directory: './cliport/environments/assets/circle/circle-template.urdf' + +- No such file or directory: './cliport/environments/assets/block/block-template.urdf' + +- task.py", line 308, in is_match + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) +IndexError: invalid index to scalar variable. + +-TypeError: get_random_size() missing 4 required positional arguments: 'min_y', 'max_y', 'min_z', and 'max_z' + +- task.py", line 195, in reward + obj_pts, zones = params +TypeError: cannot unpack non-iterable NoneType object + +- environment.py", line 230, in step + reward, info = self.task.reward() if action is not None else (0, {}) + File "task.py", line 200, in reward + pts = obj_pts[obj_id] +IndexError: arrays used as indices must be of integer (or boolean) type + +- generated_task.py", line 41, in reset + utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['light blue'], +KeyError: 'light blue' + +- environment.py", line 195, in reset + self.task.reset(self) + File "", line 38, in reset +TypeError: can only concatenate str (not "list") to str + +- environment.py", line 195, in reset + object_shape = np.random.choice(object_shapes) + in numpy.random.mtrand.RandomState.choice +ValueError: a must be 1-dimensional + +- No such file or directory: 'assets/box-template/box-template.urdf' + +- line 38, in reset.py +{'HALF': box_size / 2} +TypeError: unsupported operand type(s) for /: 'tuple' and 'int'. box_size is a tuple not a float. + +- line 38, in reset.py +IndexError: tuple index out of range +box_pose = (pallet_pose[0], pallet_pose[1], pallet_pose[2] + np.sum(box_sizes[:i+1])) + +- task.py", line 338, in fill_template + for i in range(len(replace[field])): +TypeError: object of type 'float' has no len(). + +- task.py", line 325, in get_random_pose + pos = (pos[0], pos[1], obj_size[2] / 2) +IndexError: tuple index out of range + +- task.py", line 206, in reward + for zone_idx, (zone_pose, zone_size) in enumerate(zones): +TypeError: 'NoneType' object is not iterable + +- task.py", +ball_pose = self.get_random_pose(env, ball_size) +ball_pose[0][2] += 0.02 +TypeError: 'tuple' object does not support item assignment +""" + + +You do not want to make mistakes such as +- using assets (urdfs) that do not exist +- use ambiguous language descriptions as goals. For instance, "place the colored blocks into the matching colored bowls" with one goal and sparse reward as the task instead of adding subgoal "place blue block into blue bowl" and give continuous reward. +- `matches` in the goal has wrong dimensions. It should have the same dimensions as number of objects (N) multiplied by the number of goal poses (M). Usually it is N by M. +- have vector dimension problem such as `np.random.choice(box_size)` or `box_size / 2` where `box_size` is a tuple and not an int +- make too large an object for stacking or make the task objects invisible for picking. +- accessing index out of bound `pallet_pose[2]` for `pallet_pose`. `pallet_pose=get_random_pose` returns a tuple (translation, rotation). It does not have 3rd component. Similarly accessing `container_pose[2]` or `box_pose[2]` would cause errors as well. Since it's a tuple, try to modify it in-place will also trigger errors. +- forget to replace str using `fill_template()` for urdfs with template such as `cylinder-template.urdf`. `ball-template.urdf`, `line-template.urf`. +- use `self.ee = Spatula()` as a function when doing pushing tasks, which is incorrect. It should be `self.ee = Spatula`. +- forget to compute target poses `targ_poses` for matching. Do not use object IDs for poses. +- change colors of complex objects such as `zone`. You can only change color of teomplate primitive such as `cylinder-template`. +- mistakenly use `random_pose` for target pose. Design target poses based on task objectives. +- add only one or fewer language goals which causes language-motion inconsistentcy. Note that the language goals usually are the same number as the pick and place goals. diff --git a/prompts/bottomup_task_generation_prompt/cliport_prompt_task.txt b/prompts/bottomup_task_generation_prompt/cliport_prompt_task.txt new file mode 100644 index 0000000000000000000000000000000000000000..b51ec8088439cb3db1d5e1aff22b132e2873e622 --- /dev/null +++ b/prompts/bottomup_task_generation_prompt/cliport_prompt_task.txt @@ -0,0 +1,93 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task. My goal is to design creative and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Please try to come up with tasks using only these assets. +""" +TASK_ASSET_PROMPT +""" + +There are certain rules on the asset usage. +1. Sweeping piles task must have small blocks `block/small.urdf`. Only the piles can be swept in all assets +2. Insertion tasks must have `insertion/ell.urdf` and `insertion/fixture.urdf`. Only the fixture can be inserted in all assets. +3. Rope tasks usually come with 'square/square-template.urdf'. + +========= +Here are some examples of good tasks. Try to be creative and high standard, and avoid overlapping with these tasks. + +TASK_DESCRIPTION_PROMPT + +========= +Here are some tasks that you have come up with before. Try to have a high-standard and avoid overlapping with these tasks. For instance, `bowl_ball_placement` and `sort_balls_in_bowls` are the same task. `pile_boxes_in_corner` and `stack_blocks_into_pallet` are similar tasks, `align-cylinder-in-corner` and `align-cylinder-corner` are similar. +Past Tasks: + +PAST_TASKNAME_TEMPLATE + + + +========= +Here are some bad example task instances with reasons. +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "box/box-template.urdf], +} +reasons: not interesting because it overlaps with the current task `put-block-in-bowl`. + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], +} +reasons: the language descriptions are too ambiguous. Navigation is also hard to complete. Also maze.urf does not exist. + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], +} +reasons: this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere. Similarly you cannot create task like `insert-ball-into-cylinder`. + +{ + "task-name": "ball-box-obstacle-course", + "task-description": "Navigate a ball through an obstacle course created by randomly placed boxes and finally place it inside a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: Navigate the ball is not related to tabletop manipulation tasks. + +{ + "task-name": "ball-in-box", + "task-description": "Use a cable to guide a ball into an open box.", + "assets-used": ["cable/cable.urdf", "ball/ball-template.urdf", "box/box-template.urdf"] +} +reasons: This task is too hard since it involves interaction of the cable and the ball and cannot be easily completed. + +{ + "task-name": "ball-in-container", + "task-description": "Use the spatula to lift a ball over a wall of boxes and drop it into a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: The only action primitives as pick and place. One cannot use a spatula to lift an object. + +{ + "task-name": "line-ball-sorting", + "task-description": "Move balls of different colors along a single green line, placing each ball in a designated colored box at the end of the line. The challenge includes precision in maintaining the ball on the line and the correct identification of the box color corresponding to each ball.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf"] +} +reasons: Piling or stacking balls are physically infeasible in the simulation. + +{ + "task-name": "sweep-and-stack-blocks", + "task-description": "Sweep a pile of small red and blue blocks into two separate zones marked on the tabletop. Then pick up these blocks in each zone and stack them in two towers according to their colors, with the red tower higher than the blue.", + "assets-used": ["zone/zone.urdf", "block/small.urdf"] +} +reasons: Cannot do sweeping and stacking in the same task. + + +========= +Now please describe the new task in natural languages and explain its novelty and challenges. Format the answer in a python dictionary with keys "task-name" and value type string, "task-description" (one specific sentence) and value type string with lower-case and separated by hyphens, and "assets-used" and value type list of strings. Try to be as creative as possible. + +Note: +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. diff --git a/prompts/bottomup_task_generation_prompt/cliport_prompt_task_reflection.txt b/prompts/bottomup_task_generation_prompt/cliport_prompt_task_reflection.txt new file mode 100644 index 0000000000000000000000000000000000000000..49de7f356ed859ca85f292d794a24363a9a429e1 --- /dev/null +++ b/prompts/bottomup_task_generation_prompt/cliport_prompt_task_reflection.txt @@ -0,0 +1,76 @@ + + +Do you think your task is sufficently interesting to be added as a new task for future task given that we already have the following task name and descriptions? Moreover, does the simulation code achieve the goal and the language descriptions in the task? Be as rigorous and high-standard as possible. + +Reminder: +your task: +TASK_STRING_TEMPLATE + +TASK_CODE_TEMPLATE + +current task list: +CURRENT_TASK_NAME_TEMPLATE + +========= +Reply explain your reasons and then say True or False, formatted in a python dictionary, do not miss commas or add extra spaces. Here are some examples. + +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "yellow-bowl.urdf", "box/box-template.urdf], + "reasons": "not interesting because it overlaps with the current task `put-block-in-bowl`" + "add_to_the_task_list": "False", +} + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], + "reasons": "the language descriptions are too ambiguous. Navigation is also hard to complete." + "add_to_the_task_list": "False", +} + +{ + "task-name": "sort-blocks-by-zone", + "task-description": "Sort different colored blocks into their corresponding colored zones marked on the tabletop.", + "assets-used": ["zone/zone.urdf", "stacking/block.urdf"], + "reasons": "the task is not covered in the current list and is an interesting combination of zone and block objects." + "add_to_the_task_list": "True", +} + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], + "reasons": "this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere.", + "add_to_the_task_list": "False", +} + +{ + "task-name": "arrange-blocks-on-pallet", + "task-description": "arrange the blocks on the pallet in the order: red, orange, yellow, green, blue, and purple.", + "assets-used": ["stacking/block.urdf", "pallet/pallelt.urdf"], + "reasons": "this task overlaps with `arrange-boxes-on-pallet` by merely changing the blocks to boxes.", + "add_to_the_task_list": "False", +} + +{ + "task-name": "cylinder-color-alignment", + "task-description": "Pick up cylinders of different colors and align them inside a box in the order of the colors displayed on the single green line on the tabletop.", + "assets-used": ["cylinder/cylinder-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf + "reasons": "the execution code only has one goal, i.e. it will only move one cylinder on top of the box, which is not that interesting.", + "add_to_the_task_list": "False","] +} + +{ + "task-name": "build-bridge", + "task-description": "Construct a bridge using two yellow blocks and three blue blocks. Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between. Then, place the blue block horizontally on top of the yellow blocks.", + "assets-used": ["block/block.urdf", "ball/ball-template.urdf"] + "reasons": "this task is an interesting and long-horizon task for pick and place primitives. Training manipulation agent with this skill will be a useful building block for next skills.", + "add_to_the_task_list": "True", +} + +========= + +Please incorporate these feedbacks when you design new task! + diff --git a/prompts/bottomup_task_generation_prompt_new/cliport_prompt_code_candidate_template.txt b/prompts/bottomup_task_generation_prompt_new/cliport_prompt_code_candidate_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..ff2bce8375fea33af61f0beb0279439572dfa9c4 --- /dev/null +++ b/prompts/bottomup_task_generation_prompt_new/cliport_prompt_code_candidate_template.txt @@ -0,0 +1,16 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + +""" +TASK_CODE_REFERENCE_TEMPLATE +""" + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. +Note: +1. Use `make_piles` to create piles of small blocks. +2. Use `make_ropes` to create cables. +3. Us `self.primitive = primitives.push` and `self.ee = Spatula` to use spatula. +4. Do not use random pose or the initial pose for the target poses. Come up with the specified pose. Especially for building tasks, use a consistent `anchor_pose` and use `self.add_corner_anchor_for_pose(env, anchor_pose)` to add an anchor there. +5. Do not use target poses that are not in the workspace bound `bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]])` + + +For each object, try to describe its color, size, category in the task first before you write the code. Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/bottomup_task_generation_prompt_new/cliport_prompt_code_reference_selection_template.txt b/prompts/bottomup_task_generation_prompt_new/cliport_prompt_code_reference_selection_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..ebcf4a9a238b088952a88973335b664791c25100 --- /dev/null +++ b/prompts/bottomup_task_generation_prompt_new/cliport_prompt_code_reference_selection_template.txt @@ -0,0 +1,7 @@ +Now I will provide you some reference code that might help you can write the code for the task "TASK_STRING_TEMPLATE". + + +TASK_CODE_LIST_TEMPLATE + + +Please pick 4 task python files that you would like to use as reference. Format them in a python list. diff --git a/prompts/bottomup_task_generation_prompt_new/cliport_prompt_code_split_template.txt b/prompts/bottomup_task_generation_prompt_new/cliport_prompt_code_split_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf5c20cbf04444d9b7296fadf58bb6f49531b364 --- /dev/null +++ b/prompts/bottomup_task_generation_prompt_new/cliport_prompt_code_split_template.txt @@ -0,0 +1,247 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Use functions `make_piles` and `make_ropes` for creating piles and cables. Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/bottomup_task_generation_prompt_new/cliport_prompt_task.txt b/prompts/bottomup_task_generation_prompt_new/cliport_prompt_task.txt new file mode 100644 index 0000000000000000000000000000000000000000..b51ec8088439cb3db1d5e1aff22b132e2873e622 --- /dev/null +++ b/prompts/bottomup_task_generation_prompt_new/cliport_prompt_task.txt @@ -0,0 +1,93 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task. My goal is to design creative and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Please try to come up with tasks using only these assets. +""" +TASK_ASSET_PROMPT +""" + +There are certain rules on the asset usage. +1. Sweeping piles task must have small blocks `block/small.urdf`. Only the piles can be swept in all assets +2. Insertion tasks must have `insertion/ell.urdf` and `insertion/fixture.urdf`. Only the fixture can be inserted in all assets. +3. Rope tasks usually come with 'square/square-template.urdf'. + +========= +Here are some examples of good tasks. Try to be creative and high standard, and avoid overlapping with these tasks. + +TASK_DESCRIPTION_PROMPT + +========= +Here are some tasks that you have come up with before. Try to have a high-standard and avoid overlapping with these tasks. For instance, `bowl_ball_placement` and `sort_balls_in_bowls` are the same task. `pile_boxes_in_corner` and `stack_blocks_into_pallet` are similar tasks, `align-cylinder-in-corner` and `align-cylinder-corner` are similar. +Past Tasks: + +PAST_TASKNAME_TEMPLATE + + + +========= +Here are some bad example task instances with reasons. +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "box/box-template.urdf], +} +reasons: not interesting because it overlaps with the current task `put-block-in-bowl`. + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], +} +reasons: the language descriptions are too ambiguous. Navigation is also hard to complete. Also maze.urf does not exist. + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], +} +reasons: this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere. Similarly you cannot create task like `insert-ball-into-cylinder`. + +{ + "task-name": "ball-box-obstacle-course", + "task-description": "Navigate a ball through an obstacle course created by randomly placed boxes and finally place it inside a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: Navigate the ball is not related to tabletop manipulation tasks. + +{ + "task-name": "ball-in-box", + "task-description": "Use a cable to guide a ball into an open box.", + "assets-used": ["cable/cable.urdf", "ball/ball-template.urdf", "box/box-template.urdf"] +} +reasons: This task is too hard since it involves interaction of the cable and the ball and cannot be easily completed. + +{ + "task-name": "ball-in-container", + "task-description": "Use the spatula to lift a ball over a wall of boxes and drop it into a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: The only action primitives as pick and place. One cannot use a spatula to lift an object. + +{ + "task-name": "line-ball-sorting", + "task-description": "Move balls of different colors along a single green line, placing each ball in a designated colored box at the end of the line. The challenge includes precision in maintaining the ball on the line and the correct identification of the box color corresponding to each ball.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf"] +} +reasons: Piling or stacking balls are physically infeasible in the simulation. + +{ + "task-name": "sweep-and-stack-blocks", + "task-description": "Sweep a pile of small red and blue blocks into two separate zones marked on the tabletop. Then pick up these blocks in each zone and stack them in two towers according to their colors, with the red tower higher than the blue.", + "assets-used": ["zone/zone.urdf", "block/small.urdf"] +} +reasons: Cannot do sweeping and stacking in the same task. + + +========= +Now please describe the new task in natural languages and explain its novelty and challenges. Format the answer in a python dictionary with keys "task-name" and value type string, "task-description" (one specific sentence) and value type string with lower-case and separated by hyphens, and "assets-used" and value type list of strings. Try to be as creative as possible. + +Note: +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. diff --git a/prompts/fewshot_instructions_prompt.txt b/prompts/fewshot_instructions_prompt.txt new file mode 100644 index 0000000000000000000000000000000000000000..0e0a71e3e8b8660fcb2fb4ae772b025323a5bbc0 --- /dev/null +++ b/prompts/fewshot_instructions_prompt.txt @@ -0,0 +1,114 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task. My goal is to design diverse and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Use only these assets in the task and code design. +""" +insertion/: +ell.urdf fixture.urdf + +bowl/: +bowl.urdf + +box/: +box-template.urdf + +stacking/: +block.urdf stand.urdf + +zone/: +zone.obj zone.urdf + +pallet/: +pallet.obj pallet.urdf + +ball/: +ball-template.urdf + +cylinder/: +cylinder-template.urdf + +bowl/: +bowl.urdf + +# assets not for picking +corner/: +corner-template.urdf + +line/: +single-green-line-template.urdf + +container/: +container-template.urdf +""" + +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1) + self.lang_goals.append(self.lang_template) + + # Colors of distractor objects. + # IMPORTANT: RETRIEVE THE ACTUAL COLOR VALUES + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" + +========= +Please describe the task "TASK_NAME_TEMPLATE" in natural languages and format the answer in a python dictionary with keys "task-name" and value type string, "task-description" (one specific sentence) and value type string, and "assets-used" and value type list of strings. + +========= +Now write the pybullet simulation code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. diff --git a/prompts/finetune_data.csv b/prompts/finetune_data.csv new file mode 100644 index 0000000000000000000000000000000000000000..4e83b7e99267a4b8b12d1863534b1e89313616ad --- /dev/null +++ b/prompts/finetune_data.csv @@ -0,0 +1,7595 @@ +prompt,completion +" ""align-box-corner"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""align-box-corner"" in a python code block. +### + +"," + align-box-corner: {'assets-used': ['box/box-template.urdf', 'corner/corner-template.urdf'], 'task-description': 'pick up the randomly sized box and align one of its corners to the L-shaped marker on the tabletop..', 'task-name': 'align-box-corner'}``` + +### +```python +import os +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class AlignBoxCorner(Task): + """"""Pick up the randomly sized box and align one of its corners to the L-shaped marker on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 3 + self.lang_template = ""align the brown box with the green corner"" + self.task_completed_desc = ""done with alignment"" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Generate randomly shaped box. + box_size = self.get_random_size(0.05, 0.15, 0.05, 0.15, 0.01, 0.06) + + # Add corner. + dimx = (box_size[0] / 2 - 0.025 + 0.0025, box_size[0] / 2 + 0.0025) + dimy = (box_size[1] / 2 + 0.0025, box_size[1] / 2 - 0.025 + 0.0025) + corner_template = 'corner/corner-template.urdf' + replace = {'DIMX': dimx, 'DIMY': dimy} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + corner_urdf = self.fill_template(corner_template, replace) + corner_size = (box_size[0], box_size[1], 0) + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, 'fixed') + + # Add possible placing poses. + theta = utils.quatXYZW_to_eulerXYZ(corner_pose[1])[2] + fip_rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta + np.pi)) + pose1 = (corner_pose[0], fip_rot) + alt_x = (box_size[0] / 2) - (box_size[1] / 2) + alt_y = (box_size[1] / 2) - (box_size[0] / 2) + alt_pos = (alt_x, alt_y, 0) + alt_rot0 = utils.eulerXYZ_to_quatXYZW((0, 0, np.pi / 2)) + alt_rot1 = utils.eulerXYZ_to_quatXYZW((0, 0, 3 * np.pi / 2)) + pose2 = utils.multiply(corner_pose, (alt_pos, alt_rot0)) + pose3 = utils.multiply(corner_pose, (alt_pos, alt_rot1)) + + # Add box. + box_template = 'box/box-template.urdf' + + # IMPORTANT: REPLACE THE TEMPLATE URDF + box_urdf = self.fill_template(box_template, {'DIM': np.float32(box_size)}) + box_pose = self.get_random_pose(env, box_size) + box_id = env.add_object(box_urdf, box_pose) + self.color_random_brown(box_id) + + # Goal: box is aligned with corner (1 of 4 possible poses). + self.add_goal(objs=[box_id], matches=np.int32([[1, 1, 1, 1]]), targ_poses=[corner_pose, pose1, pose2, pose3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, symmetries=[2 * np.pi], + language_goal=self.lang_template) + +``` + +###" +" ""align-rope"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""align-rope"" in a python code block. +### + +"," + align-rope: {'assets-used': ['square/square-template.urdf'], 'task-description': 'manipulate a deformable rope to connect its end-points between two corners of a 3-sided square.', 'task-name': 'align-rope'}``` + +### +```python +import os + +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.task import Task +from cliport.utils import utils + +import random +import pybullet as p + + +class AlignRope(Task): + """"""Manipulate a deformable rope to connect its end-points between two + corners of a 3-sided square."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""align the rope from {direction}"" + self.task_completed_desc = ""done aligning the rope."" + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + n_parts = 20 + radius = 0.005 + length = 2 * radius * n_parts * np.sqrt(2) + + # Add 3-sided square. + square_size = (length, length, 0) + square_pose = self.get_random_pose(env, square_size) + square_template = 'square/square-template.urdf' + replace = {'DIM': (length,), 'HALF': (np.float32(length) / 2 - 0.005,)} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(square_template, replace) + env.add_object(urdf, square_pose, 'fixed') + + # Get four corner points of square. + corner0 = ( length / 2, length / 2, 0.001) + corner1 = (-length / 2, length / 2, 0.001) + corner2 = ( length / 2, -length / 2, 0.001) + corner3 = (-length / 2, -length / 2, 0.001) + + corner0 = utils.apply(square_pose, corner0) + corner1 = utils.apply(square_pose, corner1) + corner2 = utils.apply(square_pose, corner2) + corner3 = utils.apply(square_pose, corner3) + + # Four possible alignment tasks. + task_descs = [ + ((corner0, corner1), ""front left tip to front right tip""), + ((corner0, corner2), ""front right tip to back right corner""), + ((corner1, corner3), ""front left tip to back left corner""), + ((corner3, corner2), ""back right corner to back left corner"") + ] + chosen_task = np.random.choice(len(task_descs), 1)[0] + (corner_0, corner_1), direction = task_descs[chosen_task] + + # IMPORTANT: use `make_ropes` to add cable (series of articulated small blocks). + objects, targets, matches = self.make_ropes(env, corners=(corner_0, corner_1)) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1., + language_goal=[self.lang_template.format(direction=direction)] * len(objects)) + + # wait for the scene to settle down + for i in range(480): + p.stepSimulation() +``` + +###" +" ""assembling-kits"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""assembling-kits"" in a python code block. +### + +"," + assembling-kits: {'assets-used': ['kitting/kit.urdf', 'kitting/object-template.urdf'], 'task-description': 'pick up different objects and arrange them on a board marked with corresponding silhouettes.', 'task-name': 'assembling-kits'}``` + +### +```python +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class AssemblingKits(Task): + """"""pick up different objects and arrange them on a board marked with corresponding silhouettes."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.homogeneous = False + + self.lang_template = ""put all the blocks inside the holes they fit in"" + self.task_completed_desc = ""done assembling blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add kit. + kit_size = (0.28, 0.2, 0.005) + kit_urdf = 'kitting/kit.urdf' + kit_pose = self.get_random_pose(env, kit_size) + env.add_object(kit_urdf, kit_pose, 'fixed') + + n_objects = 5 + obj_shapes = self.get_kitting_shapes(n_objects) + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['red'] + ] + + # Build kit. + targets = [] + targ_pos = [[-0.09, 0.045, 0.0014], [0, 0.045, 0.0014], + [0.09, 0.045, 0.0014], [-0.045, -0.045, 0.0014], + [0.045, -0.045, 0.0014]] + template = 'kitting/object-template.urdf' + + for i in range(n_objects): + shape = os.path.join(self.assets_root, 'kitting', + f'{obj_shapes[i]:02d}.obj') + scale = [0.003, 0.003, 0.0001] # .0005 + pos = utils.apply(kit_pose, targ_pos[i]) + theta = np.random.rand() * 2 * np.pi + rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': [0.2, 0.2, 0.2]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + env.add_object(urdf, (pos, rot), 'fixed') + targets.append((pos, rot)) + + # Add objects. + objects, matches = self.make_kitting_objects(env, targets=targets, obj_shapes=obj_shapes, n_objects=n_objects, colors=colors) + matches = np.int32(matches) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + +``` + +###" +" ""manipulating-rope"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""manipulating-rope"" in a python code block. +### + +"," + manipulating-rope: {'assets-used': ['square/square-template.urdf'], 'task-description': 'rearrange a deformable rope such that it connects the two endpoints of a 3-sided square.', 'task-name': 'manipulating-rope'}``` + +### +```python +import os + +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.task import Task +from cliport.utils import utils + +import pybullet as p + + +class ManipulatingRope(Task): + """"""rearrange a deformable rope such that it connects the two endpoints of a 3-sided square."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""manipulate the rope to complete the square"" + self.task_completed_desc = ""done manipulating the rope."" + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + n_parts = 20 + radius = 0.005 + length = 2 * radius * n_parts * np.sqrt(2) + + # Add 3-sided square. + square_size = (length, length, 0) + square_pose = self.get_random_pose(env, square_size) + square_template = 'square/square-template.urdf' + + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + replace = {'DIM': (length,), 'HALF': (np.float32(length) / 2 - 0.005,)} + urdf = self.fill_template(square_template, replace) + env.add_object(urdf, square_pose, 'fixed') + + # compute corners + corner0 = (length / 2, length / 2, 0.001) + corner1 = (-length / 2, length / 2, 0.001) + corner_0 = utils.apply(square_pose, corner0) + corner_1 = utils.apply(square_pose, corner1) + + # IMPORTANT: use `make_ropes` to add cable (series of articulated small blocks). + objects, targets, matches = self.make_ropes(env, corners=(corner_0, corner_1)) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1., lang_goal=self.lang_template) + + for i in range(480): + p.stepSimulation() + +``` + +###" +" ""packing-boxes"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""packing-boxes"" in a python code block. +### + +"," + packing-boxes: {'assets-used': ['container/container-template.urdf', 'box/box-template.urdf'], 'task-description': 'pick up randomly sized boxes and place them tightly into a container.', 'task-name': 'packing-boxes'}``` + +### +```python +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +import pybullet as p + + +class PackingBoxes(Task): + """"""pick up randomly sized boxes and place them tightly into a container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""pack all the boxes inside the brown box"" + self.task_completed_desc = ""done packing boxes."" + + self.zone_bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.08]]) + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container box. + zone_size = self.get_random_size(0.05, 0.3, 0.05, 0.3, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + margin = 0.01 + min_object_dim = 0.05 + bboxes = [] + + # Split container space with KD trees. + stack_size = np.array(zone_size) + stack_size[0] -= 0.01 + stack_size[1] -= 0.01 + root_size = (0.01, 0.01, 0) + tuple(stack_size) + root = utils.TreeNode(None, [], bbox=np.array(root_size)) + utils.KDTree(root, min_object_dim, margin, bboxes) + + colors = [utils.COLORS[c] for c in utils.COLORS if c != 'brown'] + + # Add objects in container. + object_ids = [] + bboxes = np.array(bboxes) + object_template = 'box/box-template.urdf' + + # Compute object points that are needed for zone + for bbox in bboxes: + size = bbox[3:] - bbox[:3] + position = size / 2. + bbox[:3] + position[0] += -zone_size[0] / 2 + position[1] += -zone_size[1] / 2 + pose = (position, (0, 0, 0, 1)) + pose = utils.multiply(zone_pose, pose) + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(object_template, {'DIM': size}) + icolor = np.random.choice(range(len(colors)), 1).squeeze() + box_id = env.add_object(urdf, pose, color=colors[icolor]) + object_ids.append(box_id) + + # Randomly select object in box and save ground truth pose. + object_volumes = [] + true_poses = [] + + for object_id in object_ids: + true_pose = p.getBasePositionAndOrientation(object_id) + object_size = p.getVisualShapeData(object_id)[0][3] + object_volumes.append(np.prod(np.array(object_size) * 100)) + pose = self.get_random_pose(env, object_size) + p.resetBasePositionAndOrientation(object_id, pose[0], pose[1]) + true_poses.append(true_pose) + + self.add_goal(objs=object_ids, matches=np.eye(len(object_ids)), targ_poses=true_poses, replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +``` + +###" +" ""palletizing-boxes"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""palletizing-boxes"" in a python code block. +### + +"," + palletizing-boxes: {'assets-used': ['pallet/pallet.urdf', 'box/box-template.urdf'], 'task-description': 'pick up homogeneous fixed-sized boxes and stack them in transposed layers on the pallet.', 'task-name': 'palletizing-boxes'}``` + +### +```python +import os +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PalletizingBoxes(Task): + """"""Pick up homogeneous fixed-sized boxes and stack them in transposed layers on the pallet."""""" + + def __init__(self): + super().__init__() + self.max_steps = 30 + self.lang_template = ""stack all the boxes on the pallet"" + self.task_completed_desc = ""done stacking boxes."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + zone_size = (0.3, 0.25, 0.25) + zone_urdf = 'pallet/pallet.urdf' + rotation = utils.eulerXYZ_to_quatXYZW((0, 0, 0)) + zone_pose = ((0.5, 0.25, 0.02), rotation) + env.add_object(zone_urdf, zone_pose, 'fixed') + + # Add stack of boxes on pallet. + margin = 0.01 + object_ids = [] + + # x, y, z dimensions for the asset size + stack_size = (0.19, 0.19, 0.19) + box_template = 'box/box-template.urdf' + stack_dim = np.int32([2, 3, 3]) + + box_size = (stack_size - (stack_dim - 1) * margin) / stack_dim + for z in range(stack_dim[2]): + + # Transpose every layer. + stack_dim[0], stack_dim[1] = stack_dim[1], stack_dim[0] + box_size[0], box_size[1] = box_size[1], box_size[0] + + # IMPORTANT: Compute object points and store as a dictionary for the `goal` + for y in range(stack_dim[1]): + for x in range(stack_dim[0]): + position = list((x + 0.5, y + 0.5, z + 0.5) * box_size) + position[0] += x * margin - stack_size[0] / 2 + position[1] += y * margin - stack_size[1] / 2 + position[2] += z * margin + 0.03 + pose = (position, (0, 0, 0, 1)) + pose = utils.multiply(zone_pose, pose) + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(box_template, {'DIM': box_size}) + box_id = env.add_object(urdf, pose) + object_ids.append(box_id) + self.color_random_brown(box_id) + + # Randomly select top box on pallet and save ground truth pose. + targets = [] + self.steps = [] + boxes = object_ids[:] # make copy + while boxes: + _, height, object_mask = self.get_true_image(env) + top = np.argwhere(height > (np.max(height) - 0.03)) + rpixel = top[int(np.floor(np.random.random() * len(top)))] # y, x + box_id = int(object_mask[rpixel[0], rpixel[1]]) + if box_id in boxes: + position, rotation = p.getBasePositionAndOrientation(box_id) + rposition = np.float32(position) + np.float32([0, -10, 0]) + p.resetBasePositionAndOrientation(box_id, rposition, rotation) + self.steps.append(box_id) + targets.append((position, rotation)) + boxes.remove(box_id) + + self.steps.reverse() # Time-reversed depalletizing. + self.add_goal(objs=object_ids, matches=np.eye(len(object_ids)), targ_poses=targets, replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) + self.spawn_box() + + def reward(self): + reward, info = super().reward() + self.spawn_box() + return reward, info +``` + +###" +" ""place-red-in-green"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""place-red-in-green"" in a python code block. +### + +"," + place-red-in-green: {'assets-used': ['bowl/bowl.urdf', 'stacking/block.urdf'], 'task-description': 'pick up the red blocks and place them into the green bowls amidst other objects.', 'task-name': 'place-red-in-green'}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """"""pick up the red blocks and place them into the green bowls amidst other objects."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""put the red blocks in a green bowl"" + self.task_completed_desc = ""done placing blocks in bowls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 + +``` + +###" +" ""stack-block-pyramid"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""stack-block-pyramid"" in a python code block. +### + +"," + stack-block-pyramid: {'assets-used': ['stacking/stand.urdf', 'stacking/block.urdf'], 'task-description': 'sequentially stack 6 blocks into a pyramid of 3-2-1 with rainbow colored ordering.', 'task-name': 'stack-block-pyramid'}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """"""Build a pyramid of colored blocks in a color sequence"""""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = ""make the {row} row with {blocks}"" + self.task_completed_desc = ""done stacking block pyramid."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks=""the green, blue and purple blocks"", row=""bottom"") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks=""the yellow and orange blocks"", row=""middle"") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks=""the red block"", row=""top"") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +``` + +###" +" ""sweeping-piles"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""sweeping-piles"" in a python code block. +### + +"," + sweeping-piles: {'assets-used': ['zone/zone.urdf', 'block/small.urdf'], 'task-description': 'push piles of small objects into a target goal zone marked on the tabletop.', 'task-name': 'sweeping-piles'}``` + +### +```python +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """"""Push piles of small objects into a target goal zone marked on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""push the pile of blocks into the green square"" + self.task_completed_desc = ""done sweeping."" + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +``` + +###" +" ""build-wheel"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""build-wheel"" in a python code block. +### + +"," + build-wheel: {'task-name': 'build-wheel', 'task-description': 'Construct a wheel using blocks and a sphere. First, position eight blocks in a circular layout on the tabletop. Each block should be touching its two neighbors and colored in alternating red and blue. Then place a green sphere in the center of the circular layout, completing the wheel.', 'assets-used': ['block/block.urdf', 'sphere/sphere.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildWheel(Task): + """"""Construct a wheel using blocks and a sphere."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""Construct a wheel using blocks and a sphere. First, position eight blocks in a circular layout on the tabletop. Each block should be touching its two neighbors and colored in alternating red and blue. Then place a green sphere in the center of the circular layout, completing the wheel."" + self.task_completed_desc = ""done building wheel."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue']] + blocks = [] + for i in range(8): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i % 2]) + blocks.append(block_id) + + # Add sphere. + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + sphere_color = utils.COLORS['green'] + sphere_pose = ((0.5, 0.0, 0.0), (0,0,0,1)) # fixed pose + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=sphere_color) + + # Goal: blocks are arranged in a circle and sphere is in the center. + circle_radius = 0.1 + circle_center = (0, 0, block_size[2] / 2) + angles = np.linspace(0, 2 * np.pi, 8, endpoint=False) + block_poses = [(circle_center[0] + circle_radius * np.cos(angle), + circle_center[1] + circle_radius * np.sin(angle), + circle_center[2]) for angle in angles] + block_poses = [(utils.apply(sphere_pose, pos), sphere_pose[1]) for pos in block_poses] + self.add_goal(objs=blocks, matches=np.ones((8, 8)), targ_poses=block_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=8 / 9, language_goal=self.lang_template) + + # Goal: sphere is in the center of the blocks. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[sphere_pose], replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1 / 9, language_goal=self.lang_template) +``` + +###" +" ""push-piles-into-letter"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""push-piles-into-letter"" in a python code block. +### + +"," + push-piles-into-letter: {'task-name': 'push-piles-into-letter', 'task-description': 'Push piles of small objects into a target goal zone shaped in some letters.', 'assets-used': ['kitting/kit.urdf', 'kitting/object-template-nocollision.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildWheel(Task): + """"""Construct a wheel using blocks and a sphere."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""Construct a wheel using blocks and a sphere. First, position eight blocks in a circular layout on the tabletop. Each block should be touching its two neighbors and colored in alternating red and blue. Then place a green sphere in the center of the circular layout, completing the wheel."" + self.task_completed_desc = ""done building wheel."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue']] + blocks = [] + for i in range(8): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i % 2]) + blocks.append(block_id) + + # Add sphere. + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + sphere_color = utils.COLORS['green'] + sphere_pose = ((0.5, 0.0, 0.0), (0,0,0,1)) # fixed pose + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=sphere_color) + + # Goal: blocks are arranged in a circle and sphere is in the center. + circle_radius = 0.1 + circle_center = (0, 0, block_size[2] / 2) + angles = np.linspace(0, 2 * np.pi, 8, endpoint=False) + block_poses = [(circle_center[0] + circle_radius * np.cos(angle), + circle_center[1] + circle_radius * np.sin(angle), + circle_center[2]) for angle in angles] + block_poses = [(utils.apply(sphere_pose, pos), sphere_pose[1]) for pos in block_poses] + self.add_goal(objs=blocks, matches=np.ones((8, 8)), targ_poses=block_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=8 / 9, language_goal=self.lang_template) + + # Goal: sphere is in the center of the blocks. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[sphere_pose], replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1 / 9, language_goal=self.lang_template) +``` + +###" +" ""connect-boxes-with-rope"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""connect-boxes-with-rope"" in a python code block. +### + +"," + connect-boxes-with-rope: {'task-name': 'connect-boxes-with-rope', 'task-description': 'Connect two colored blocks with ropes.', 'assets-used': ['block/block.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import IPython + +class ConnectBoxesWithRope(Task): + """"""Connect two colored blocks with ropes."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""connect the {color1} and {color2} blocks with the rope."" + self.task_completed_desc = ""done connecting."" + self.additional_reset() + self.pos_eps = 0.04 # higher tolerance + + def reset(self, env): + super().reset(env) + colors = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'] + blocks = [] + target_colors = np.random.choice(colors, 2, replace=False) + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + corner_poses = [] + + for color in colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + if color in target_colors: + corner_poses.append(block_pose) + + dist = np.linalg.norm(np.array(corner_poses[0][0])-np.array(corner_poses[1][0])) + n_parts = int(20 * dist / 0.4) + + # IMPORTANT: use `make_ropes` to add cable (series of articulated small blocks). + objects, targets, matches = self.make_ropes(env, corners=(corner_poses[0][0], corner_poses[1][0]), n_parts=n_parts) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1., + language_goal=self.lang_template.format(color1=target_colors[0], color2=target_colors[1])) + + # wait for the scene to settle down + for i in range(600): + p.stepSimulation() +``` + +###" +" ""build-car"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""build-car"" in a python code block. +### + +"," + build-car: {'task-name': 'build-car', 'task-description': 'Construct a simple car structure using blocks and cylinders.', 'assets-used': ['block/block.urdf', 'ball/ball-template.urdf']}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildCar(Task): + """"""Construct a simple car structure using blocks and cylinders."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""Construct a simple car structure using blocks and cylinders. "" \ + ""Firstly, create the base of the car by positioning two red blocks side by side. "" \ + ""Then, add the car body by stacking a blue block on top of the base. "" \ + ""For the wheels, place a black cylinder on each side of the base blocks."" + self.task_completed_desc = ""done building car."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + car_pose = ((0.5, 0.0, 0.0), (0,0,0,1)) # fixed pose + base_length = 0.04 + self.add_corner_anchor_for_pose(env, car_pose) + + # Add base blocks. Use box template so that we can change its size. + base_size = (0.02, 0.04, 0.02) + base_block_urdf = ""box/box-template.urdf"" + base_block_urdf = self.fill_template(base_block_urdf, {'DIM': base_size}) + anchor_base_poses = [(utils.apply(car_pose, (base_length / 2, base_length / 2, 0.001)), car_pose[1]), + (utils.apply(car_pose, (-base_length / 2, base_length / 2, 0.001)), car_pose[1])] + base_blocks = [] + + for idx in range(2): + base_block_pose = self.get_random_pose(env, base_size) + base_block_id = env.add_object(base_block_urdf, base_block_pose, color=utils.COLORS['red']) + base_blocks.append(base_block_id) + + # Add car body block. + body_size = (0.04, 0.02, 0.02) # x, y, z dimensions for the asset size + body_block_urdf = ""box/box-template.urdf"" + body_block_urdf = self.fill_template(body_block_urdf, {'DIM': body_size}) + body_block_pose = self.get_random_pose(env, body_size) + body_block_id = env.add_object(body_block_urdf, body_block_pose, color=utils.COLORS['blue']) + anchor_body_poses = [car_pose] + + wheel_length = 0.12 + anchor_wheel_poses = [(utils.apply(car_pose, ( wheel_length / 2, wheel_length / 2, 0.001)), car_pose[1]), + (utils.apply(car_pose, (-wheel_length / 2, wheel_length / 2, 0.001)), car_pose[1]), + (utils.apply(car_pose, ( wheel_length / 2, -wheel_length / 2, 0.001)), car_pose[1]), + (utils.apply(car_pose, (-wheel_length / 2, -wheel_length / 2, 0.001)), car_pose[1])] + + # Add wheels. + wheel_size = (0.02, 0.02, 0.02) # x, y, z dimensions for the asset size + wheel_urdf = 'cylinder/cylinder-template.urdf' + wheel_urdf = self.fill_template(wheel_urdf, {'DIM': wheel_size}) + + wheels = [] + for idx in range(4): + wheel_pose = self.get_random_pose(env, wheel_size) + wheel_id = env.add_object(wheel_urdf, wheel_pose, color=utils.COLORS['black']) + wheels.append(wheel_id) + + # Goal: Firstly, create the base of the car by positioning two red blocks side by side. + self.add_goal(objs=base_blocks, + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./3, + language_goal=""Firstly, create the base of the car by positioning two red blocks side by side."") + + # Then, add the car body by stacking a blue block on top of the base. + self.add_goal(objs=[body_block_id], + matches=np.ones((1, 1)), + targ_poses=anchor_body_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./3, + language_goal=""Then, add the car body by stacking a blue block on top of the base."") + + # For the wheels, place a black cylinder on each side of the base blocks. + self.add_goal(objs=wheels, + matches=np.ones((4, 4)), + targ_poses=anchor_wheel_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./3, + language_goal=""For the wheels, place a black cylinder on each side of the base blocks."") + + +``` + +###" +" ""manipulating-two-ropes"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""manipulating-two-ropes"" in a python code block. +### + +"," + manipulating-two-ropes: {'task-name': 'manipulating-two-ropes', 'task-description': 'rearrange the red and blue deformable ropes such that it connects the two endpoints of a 3-sided square of corresponding color.', 'assets-used': ['square/square-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula + +class ManipulatingTwoRopes(Task): + """"""rearrange the red and blue deformable ropes such that it connects the two endpoints of a 3-sided square of corresponding color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""rearrange the {color_name} rope such that it connects the two endpoints of a 3-sided square of corresponding color."" + self.task_completed_desc = ""done manipulating two ropes."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + n_parts = 20 + radius = 0.005 + length = 2 * radius * n_parts * np.sqrt(2) + + # Add 3-sided square for the red rope. + color_list = ['red', 'blue'] + for color_name in color_list: + square_size = (length, length, 0) + square_pose = self.get_random_pose(env, square_size) + square_template = 'square/square-template.urdf' + + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + replace = {'DIM': (length,), 'HALF': (np.float32(length) / 2 - 0.005,)} + urdf = self.fill_template(square_template, replace) + env.add_object(urdf, square_pose, 'fixed', color=utils.COLORS[color_name]) + + # compute corners + corner0 = (length / 2, length / 2, 0.001) + corner1 = (-length / 2, length / 2, 0.001) + corner_0 = utils.apply(square_pose, corner0) + corner_1 = utils.apply(square_pose, corner1) + + # IMPORTANT: use `make_ropes` to add cable (series of articulated small blocks). + objects, targets, matches = self.make_ropes(env, corners=(corner_0, corner_1), color_name=color_name) + self.add_goal(objs=objects, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1. / len(color_list), + language_goal=self.lang_template.format(color_name=color_name)) + + print(f""len of languages: {len(self.lang_goals)} obj:{len(objects)}"") + for i in range(480): + p.stepSimulation() + +``` + +###" +" ""insert-sphere-into-container"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""insert-sphere-into-container"" in a python code block. +### + +"," + insert-sphere-into-container: {'task-name': 'insert-sphere-into-container', 'task-description': 'Pick up a blue sphere and place it into an open container.', 'assets-used': ['sphere/sphere.urdf', 'container/container-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertSphereIntoContainer(Task): + """"""Pick up a blue sphere and place it into an open container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""pick up a blue sphere and place it into an open container"" + self.task_completed_desc = ""done inserting sphere into container."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + # x, y, z dimensions for the asset size + container_size = (0.1, 0.1, 0.1) + container_pose = self.get_random_pose(env, container_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + container_id = env.add_object(container_urdf, container_pose, 'fixed') + + # Add sphere. + # x, y, z dimensions for the asset size + sphere_size = (0.04, 0.04, 0.04) + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_urdf = 'sphere/sphere.urdf' + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS['blue']) + + # Goal: the blue sphere is in the container. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, + language_goal=self.lang_template) +``` + +###" +" ""insert-cylinder-in-container"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""insert-cylinder-in-container"" in a python code block. +### + +"," + insert-cylinder-in-container: {'task-name': 'insert-cylinder-in-container', 'task-description': 'Pick up a blue cylindrical block and place it into an empty container.', 'assets-used': ['cylinder/cylinder-template.urdf', 'container/container-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertSphereIntoContainer(Task): + """"""Pick up a blue sphere and place it into an open container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""pick up a blue sphere and place it into an open container"" + self.task_completed_desc = ""done inserting sphere into container."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + # x, y, z dimensions for the asset size + container_size = (0.1, 0.1, 0.1) + container_pose = self.get_random_pose(env, container_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + container_id = env.add_object(container_urdf, container_pose, 'fixed') + + # Add sphere. + # x, y, z dimensions for the asset size + sphere_size = (0.04, 0.04, 0.04) + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_urdf = 'sphere/sphere.urdf' + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS['blue']) + + # Goal: the blue sphere is in the container. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, + language_goal=self.lang_template) +``` + +###" +" ""build-bridge"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""build-bridge"" in a python code block. +### + +"," + build-bridge: {'task-name': 'build-bridge', 'task-description': 'Construct a bridge using two yellow blocks and three blue blocks. Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between. Then, place the blue block horizontally on top of the yellow blocks.', 'assets-used': ['block/block.urdf', 'ball/ball-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildBridge(Task): + """"""Construct a bridge using two yellow blocks and three blue blocks. + Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between. + Then, place the blue block horizontally on top of the yellow blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""build a bridge using four yellow blocks and one long blue block"" + self.task_completed_desc = ""done building bridge."" + + def reset(self, env): + super().reset(env) + + # Add yellow blocks. + base_length = 0.04 + base_size = (base_length, base_length, base_length) + base_block_urdf = ""box/box-template.urdf"" + bridge_pose = ((0.5, 0.0, 0.0), (0, 0, 0, 1)) # fixed pose + self.add_corner_anchor_for_pose(env, bridge_pose) + + base_block_urdf = self.fill_template(base_block_urdf, {'DIM': base_size}) + anchor_base_poses = [(utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.041)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.041)), bridge_pose[1])] + base_blocks = [] + + for idx in range(4): + base_block_pose = self.get_random_pose(env, base_size) + base_block_id = env.add_object(base_block_urdf, base_block_pose, color=utils.COLORS['yellow']) + base_blocks.append(base_block_id) + + # Add car body block. + body_size = (0.12, 0.04, 0.02) # x, y, z dimensions for the asset size + body_block_urdf = ""box/box-template.urdf"" + body_block_urdf = self.fill_template(body_block_urdf, {'DIM': body_size}) + body_block_pose = self.get_random_pose(env, body_size) + body_block_id = env.add_object(body_block_urdf, body_block_pose, color=utils.COLORS['blue']) + anchor_body_poses = [bridge_pose] + + # Goal: Firstly, create the base of the car by positioning two red blocks side by side. + self.add_goal(objs=base_blocks[:2], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + self.add_goal(objs=base_blocks[2:], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./2, + language_goal=""Place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + # Then, add the car body by stacking a blue block on top of the base. + self.add_goal(objs=[body_block_id], + matches=np.ones((1, 1)), + targ_poses=anchor_body_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Then, place the blue block horizontally on top of the yellow blocks."") +``` + +###" +" ""insert-ell-in-fixture"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""insert-ell-in-fixture"" in a python code block. +### + +"," + insert-ell-in-fixture: {'task-name': 'insert-ell-in-fixture', 'task-description': 'Pick up an Ell shaped block and insert it into a fixture on the tabletop.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildBridge(Task): + """"""Construct a bridge using two yellow blocks and three blue blocks. + Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between. + Then, place the blue block horizontally on top of the yellow blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""build a bridge using four yellow blocks and one long blue block"" + self.task_completed_desc = ""done building bridge."" + + def reset(self, env): + super().reset(env) + + # Add yellow blocks. + base_length = 0.04 + base_size = (base_length, base_length, base_length) + base_block_urdf = ""box/box-template.urdf"" + bridge_pose = ((0.5, 0.0, 0.0), (0, 0, 0, 1)) # fixed pose + self.add_corner_anchor_for_pose(env, bridge_pose) + + base_block_urdf = self.fill_template(base_block_urdf, {'DIM': base_size}) + anchor_base_poses = [(utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.041)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.041)), bridge_pose[1])] + base_blocks = [] + + for idx in range(4): + base_block_pose = self.get_random_pose(env, base_size) + base_block_id = env.add_object(base_block_urdf, base_block_pose, color=utils.COLORS['yellow']) + base_blocks.append(base_block_id) + + # Add car body block. + body_size = (0.12, 0.04, 0.02) # x, y, z dimensions for the asset size + body_block_urdf = ""box/box-template.urdf"" + body_block_urdf = self.fill_template(body_block_urdf, {'DIM': body_size}) + body_block_pose = self.get_random_pose(env, body_size) + body_block_id = env.add_object(body_block_urdf, body_block_pose, color=utils.COLORS['blue']) + anchor_body_poses = [bridge_pose] + + # Goal: Firstly, create the base of the car by positioning two red blocks side by side. + self.add_goal(objs=base_blocks[:2], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + self.add_goal(objs=base_blocks[2:], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./2, + language_goal=""Place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + # Then, add the car body by stacking a blue block on top of the base. + self.add_goal(objs=[body_block_id], + matches=np.ones((1, 1)), + targ_poses=anchor_body_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Then, place the blue block horizontally on top of the yellow blocks."") +``` + +###" +" ""build-block-tower-on-pallet"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""build-block-tower-on-pallet"" in a python code block. +### + +"," + build-block-tower-on-pallet: {'task-name': 'build-block-tower-on-pallet', 'task-description': 'Pick up six blocks in two colors (three red and three blue) and stack them in alternating colors on the pallet. The tower should start with a red block at the bottom.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildBridge(Task): + """"""Construct a bridge using two yellow blocks and three blue blocks. + Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between. + Then, place the blue block horizontally on top of the yellow blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""build a bridge using four yellow blocks and one long blue block"" + self.task_completed_desc = ""done building bridge."" + + def reset(self, env): + super().reset(env) + + # Add yellow blocks. + base_length = 0.04 + base_size = (base_length, base_length, base_length) + base_block_urdf = ""box/box-template.urdf"" + bridge_pose = ((0.5, 0.0, 0.0), (0, 0, 0, 1)) # fixed pose + self.add_corner_anchor_for_pose(env, bridge_pose) + + base_block_urdf = self.fill_template(base_block_urdf, {'DIM': base_size}) + anchor_base_poses = [(utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.041)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.041)), bridge_pose[1])] + base_blocks = [] + + for idx in range(4): + base_block_pose = self.get_random_pose(env, base_size) + base_block_id = env.add_object(base_block_urdf, base_block_pose, color=utils.COLORS['yellow']) + base_blocks.append(base_block_id) + + # Add car body block. + body_size = (0.12, 0.04, 0.02) # x, y, z dimensions for the asset size + body_block_urdf = ""box/box-template.urdf"" + body_block_urdf = self.fill_template(body_block_urdf, {'DIM': body_size}) + body_block_pose = self.get_random_pose(env, body_size) + body_block_id = env.add_object(body_block_urdf, body_block_pose, color=utils.COLORS['blue']) + anchor_body_poses = [bridge_pose] + + # Goal: Firstly, create the base of the car by positioning two red blocks side by side. + self.add_goal(objs=base_blocks[:2], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + self.add_goal(objs=base_blocks[2:], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./2, + language_goal=""Place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + # Then, add the car body by stacking a blue block on top of the base. + self.add_goal(objs=[body_block_id], + matches=np.ones((1, 1)), + targ_poses=anchor_body_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Then, place the blue block horizontally on top of the yellow blocks."") +``` + +###" +" ""color-coordinated-tower"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-tower"" in a python code block. +### + +"," + color-coordinated-tower: {'task-name': 'color-coordinated-tower', 'task-description': 'Pick up blocks of five different colors (red, blue, green, yellow, and orange) and stack them on a pallet in the specific sequence. The bottom of the tower should start with a red block followed by a blue, green, yellow and finally an orange block at the top.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildBridge(Task): + """"""Construct a bridge using two yellow blocks and three blue blocks. + Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between. + Then, place the blue block horizontally on top of the yellow blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""build a bridge using four yellow blocks and one long blue block"" + self.task_completed_desc = ""done building bridge."" + + def reset(self, env): + super().reset(env) + + # Add yellow blocks. + base_length = 0.04 + base_size = (base_length, base_length, base_length) + base_block_urdf = ""box/box-template.urdf"" + bridge_pose = ((0.5, 0.0, 0.0), (0, 0, 0, 1)) # fixed pose + self.add_corner_anchor_for_pose(env, bridge_pose) + + base_block_urdf = self.fill_template(base_block_urdf, {'DIM': base_size}) + anchor_base_poses = [(utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.001)), bridge_pose[1]), + (utils.apply(bridge_pose, (- 3 * base_length / 2, 0, 0.041)), bridge_pose[1]), + (utils.apply(bridge_pose, ( 3 * base_length / 2, 0, 0.041)), bridge_pose[1])] + base_blocks = [] + + for idx in range(4): + base_block_pose = self.get_random_pose(env, base_size) + base_block_id = env.add_object(base_block_urdf, base_block_pose, color=utils.COLORS['yellow']) + base_blocks.append(base_block_id) + + # Add car body block. + body_size = (0.12, 0.04, 0.02) # x, y, z dimensions for the asset size + body_block_urdf = ""box/box-template.urdf"" + body_block_urdf = self.fill_template(body_block_urdf, {'DIM': body_size}) + body_block_pose = self.get_random_pose(env, body_size) + body_block_id = env.add_object(body_block_urdf, body_block_pose, color=utils.COLORS['blue']) + anchor_body_poses = [bridge_pose] + + # Goal: Firstly, create the base of the car by positioning two red blocks side by side. + self.add_goal(objs=base_blocks[:2], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + self.add_goal(objs=base_blocks[2:], + matches=np.ones((2, 2)), + targ_poses=anchor_base_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./2, + language_goal=""Place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between."") + + # Then, add the car body by stacking a blue block on top of the base. + self.add_goal(objs=[body_block_id], + matches=np.ones((1, 1)), + targ_poses=anchor_body_poses, + replace=False, + rotations=True, + metric='pose', + params=None, + step_max_reward=1./4, + language_goal=""Then, place the blue block horizontally on top of the yellow blocks."") +``` + +###" +" ""stack-blocks-in-container"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""stack-blocks-in-container"" in a python code block. +### + +"," + stack-blocks-in-container: {'task-name': 'stack-blocks-in-container', 'task-description': 'Pick up five blocks of different colors (red, blue, green, yellow, and orange) and stack them in a container in a specific sequence. The bottom of the stack should start with a red block followed by a blue, green, yellow and finally an orange block at the top.', 'assets-used': ['block/block.urdf', 'container/container-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class StackBlocksInContainer(Task): + """"""Pick up five blocks of different colors (red, blue, green, yellow, and orange) + and stack them in a container in a specific sequence. + The bottom of the stack should start with a red block followed by a blue, + green, yellow and finally an orange block at the top."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""stack the blocks in the container in the following order: {order}"" + self.task_completed_desc = ""done stacking blocks in container."" + self.order = ['red', 'blue', 'green', 'yellow', 'orange'] + self.colors = [utils.COLORS[color] for color in self.order] + + def reset(self, env): + super().reset(env) + + # Add container. + container_size = (0.15, 0.15, 0.15) # x, y, z dimensions for the container size + container_pose = self.get_random_pose(env, container_size) + container_urdf = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_urdf, replace) + env.add_object(container_urdf, container_pose, 'fixed') + + # Add blocks. + block_size = (0.04, 0.04, 0.04) # x, y, z dimensions for the block size + block_urdf = 'block/block.urdf' + blocks = [] + for color in self.colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Goal: each block is stacked in the container in the specified order. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(blocks), + language_goal=self.lang_template.format(order=', '.join(self.order))) +``` + +###" +" ""color-coordinated-block-tower"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-block-tower"" in a python code block. +### + +"," + color-coordinated-block-tower: {'task-name': 'color-coordinated-block-tower', 'task-description': 'On a tabletop, there are fifteen blocks of three different colors (five red, five blue, and five green). The task is to pick up these blocks and stack them onto three different stands on the table, creating three different towers. Each stand should have a tower of the same color blocks with five blocks each. The blocks in each tower should be stacked in a way that the block on top is slightly displaced in relation to the block underneath, creating a spiral-like effect. The challenge lies in the color-based sorting, precise placement for achieving the spiral effect and careful stacking of the blocks to avoid toppling.', 'assets-used': ['block/block.urdf', 'stacking/stand.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedBlockTower(Task): + """"""Stack four blocks on a pallet in the following order from bottom to top: + two blue blocks side by side, one red block centered on the blue blocks, + and one green block on top of the red block."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""stack four blocks on a pallet in the following order from bottom to top: two blue blocks side by side, one red block centered on the blue blocks, and one green block on top of the red block."" + self.task_completed_desc = ""done stacking blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.015) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['blue'], utils.COLORS['blue'], utils.COLORS['red'], utils.COLORS['green']] + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.02, 0.02), (0, 0.02, 0.02), (0, 0, 0.06), (0, 0, 0.10)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: two blue blocks are placed side by side on the pallet. + # Break the language prompt step-by-step + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, + language_goal=""place two blue blocks side by side on the pallet"") + + # Goal: one red block is placed centered on the blue blocks. + self.add_goal(objs=blocks[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=""place one red block centered on the blue blocks"") + + # Goal: one green block is placed on top of the red block. + self.add_goal(objs=blocks[3:], matches=np.ones((1, 1)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=""place one green block on top of the red block"") +``` + +###" +" ""color-structured-block-tower"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-structured-block-tower"" in a python code block. +### + +"," + color-structured-block-tower: {'task-name': 'color-structured-block-tower', 'task-description': 'Construct a tower using six blocks: two red, two blue, and two green. The tower should be built in the order of a red block at the base, followed by a blue, then green, then red, blue and green at the top.', 'assets-used': ['block/block.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorStructuredBlockTower(Task): + """"""Construct a tower using six blocks: two red, two blue, and two green. + The tower should be built in the order of a red block at the base, + followed by a blue, then green, then red, blue and green at the top."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""construct a tower using six blocks: two red, two blue, and two green. "" \ + ""The tower should be built in the order of a red block at the base, "" \ + ""followed by a blue, then green, then red, blue and green at the top."" + self.task_completed_desc = ""done building color-structured block tower."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define block colors and sizes + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green']] * 2 + block_size = (0.04, 0.04, 0.04) + + # Add blocks + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Define target poses for the blocks in the tower + base_pose = self.get_random_pose(env, block_size) + targ_poses = [base_pose] + for i in range(1, 6): + targ_poses.append((np.array(base_pose[0]) + np.array([0, 0, i * block_size[2]]), base_pose[1])) + + # Add goals + for i in range(6): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targ_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/6, symmetries=[np.pi/2], + language_goal=self.lang_template) +``` + +###" +" ""stack-color-coordinated-blocks"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""stack-color-coordinated-blocks"" in a python code block. +### + +"," + stack-color-coordinated-blocks: {'task-name': 'stack-color-coordinated-blocks', 'task-description': 'Pick up six blocks of different colors (red, blue, green, yellow, orange, and purple) and stack them on a pallet in two separate stacks. The first stack should be red at the bottom, blue in the middle, and green at top. The second stack should be yellow at the bottom, orange in the middle, and purple at the top.', 'assets-used': ['box/box-template.urdf', 'pallet/pallet.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class StackColorCoordinatedBlocks(Task): + """"""Pick up six blocks of different colors (red, blue, green, yellow, orange, and purple) + and stack them on a pallet in two separate stacks. The first stack should be red at the bottom, + blue in the middle, and green at top. The second stack should be yellow at the bottom, + orange in the middle, and purple at the top."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""stack the blocks on the pallet in two separate stacks. "" \ + ""The first stack should be red at the bottom, blue in the middle, "" \ + ""and green at top. The second stack should be yellow at the bottom, "" \ + ""orange in the middle, and purple at the top."" + self.task_completed_desc = ""done stacking color-coordinated blocks."" + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['purple'] + ] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'box/box-template.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0, 0.02), (0, 0.05, 0.02), + (0, -0.05, 0.06), (0, 0, 0.06), (0, 0.05, 0.06)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked on the pallet in two separate stacks. + # First stack: red at the bottom, blue in the middle, and green at top. + self.add_goal(objs=blocks[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) + + # Second stack: yellow at the bottom, orange in the middle, and purple at the top. + self.add_goal(objs=blocks[3:], matches=np.ones((3, 3)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) +``` + +###" +" ""assemble-single-car"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""assemble-single-car"" in a python code block. +### + +"," + assemble-single-car: {'task-name': 'assemble-single-car', 'task-description': 'Build a mini car using a large blue box as the body, a smaller red box on top as the roof, and two tiny green boxes on the sides as wheels.', 'assets-used': ['box/box-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class AssembleSingleCar(Task): + """"""Assemble a mini car using a large blue box as the body, a smaller red box on top as the roof, and two tiny green boxes on the sides as wheels."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""build a mini car using a large blue box as the body, a smaller red box on top as the roof, and two tiny green boxes on the sides as wheels"" + self.task_completed_desc = ""done assembling the car."" + + def reset(self, env): + super().reset(env) + + # Add car body (large blue box). + body_size = (0.1, 0.05, 0.02) # x, y, z dimensions + body_pose = self.get_random_pose(env, body_size) + body_urdf = 'box/box-template.urdf' + body_color = utils.COLORS['blue'] + body_id = env.add_object(body_urdf, body_pose, color=body_color) + + # Add car roof (smaller red box). + roof_size = (0.08, 0.04, 0.02) # x, y, z dimensions + roof_pose = self.get_random_pose(env, roof_size) + roof_urdf = 'box/box-template.urdf' + roof_color = utils.COLORS['red'] + roof_id = env.add_object(roof_urdf, roof_pose, color=roof_color) + + # Add car wheels (two tiny green boxes). + wheel_size = (0.02, 0.02, 0.01) # x, y, z dimensions + wheel_urdf = 'box/box-template.urdf' + wheel_color = utils.COLORS['green'] + wheel_ids = [] + + for _ in range(2): + wheel_pose = self.get_random_pose(env, wheel_size) + wheel_id = env.add_object(wheel_urdf, wheel_pose, color=wheel_color) + wheel_ids.append(wheel_id) + + # Goal: assemble the car by placing the roof on the body and the wheels on the sides. + # The target poses are calculated based on the body pose. + roof_targ_pose = (body_pose[0] + np.array([0, 0, body_size[2] + roof_size[2]/2]), body_pose[1]) + wheel_targ_poses = [(body_pose[0] + np.array([0, body_size[1]/2 + wheel_size[1]/2, -body_size[2]/2]), body_pose[1]), + (body_pose[0] + np.array([0, -body_size[1]/2 - wheel_size[1]/2, -body_size[2]/2]), body_pose[1])] + + # Add the goals. + self.add_goal(objs=[roof_id], matches=np.ones((1, 1)), targ_poses=[roof_targ_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/3, language_goal=self.lang_template) + + self.add_goal(objs=wheel_ids, matches=np.ones((2, 2)), targ_poses=wheel_targ_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=2/3, language_goal=self.lang_template) +``` + +###" +" ""sort-and-stack-clr-blocks"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""sort-and-stack-clr-blocks"" in a python code block. +### + +"," + sort-and-stack-clr-blocks: {'task-name': 'sort-and-stack-clr-blocks', 'task-description': 'Pick up four blocks of different colors (red, blue, green, yellow) and place them into separate corners of a pallet. After sorting, stack them in a specific sequence on top of the pallet. The bottom of the stack should start with a green block followed by a blue, then red, and finally a yellow block at the top.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SortAndStackClrBlocks(Task): + """"""Pick up four blocks of different colors (red, blue, green, yellow) and place them into separate corners of a pallet. After sorting, stack them in a specific sequence on top of the pallet. The bottom of the stack should start with a green block followed by a blue, then red, and finally a yellow block at the top."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""sort and stack the blocks in the order of green, blue, red, and yellow"" + self.task_completed_desc = ""done sorting and stacking blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0.05, 0.05, 0.02), (-0.05, 0.05, 0.02), (-0.05, -0.05, 0.02), (0.05, -0.05, 0.02)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are sorted into separate corners of the pallet. + self.add_goal(objs=blocks, matches=np.eye(4), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=0.5, symmetries=[np.pi/2]*4, + language_goal=self.lang_template) + + # Associate stacking locations for goals. + stack_pos = [(0, 0, 0.02), (0, 0, 0.06), (0, 0, 0.10), (0, 0, 0.14)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in stack_pos] + + # Goal: blocks are stacked on top of the pallet in the order of green, blue, red, and yellow. + self.add_goal(objs=blocks, matches=np.eye(4), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=0.5, symmetries=[np.pi/2]*4, + language_goal=self.lang_template) +``` + +###" +" ""create-pyramid-blocks-and-container"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""create-pyramid-blocks-and-container"" in a python code block. +### + +"," + create-pyramid-blocks-and-container: {'task-name': 'create-pyramid-blocks-and-container', 'task-description': 'Create a pyramid structure using six blocks of three different colors (two red, two green, and two blue) inside a container. The first layer of the pyramid should consist of the two green and one blue block. The second layer should be the two red blocks, and the last block which is the top of the pyramid should be the remaining blue block.', 'assets-used': ['container/container-template.urdf', 'block/block.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CreatePyramidBlocksAndContainer(Task): + """"""Create a pyramid structure using six blocks of three different colors (two red, two green, and two blue) inside a container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Create a pyramid structure using six blocks of three different colors (two red, two green, and two blue) inside a container."" + self.task_completed_desc = ""done creating pyramid."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + # x, y, z dimensions for the asset size + container_size = (0.3, 0.3, 0.1) + container_pose = self.get_random_pose(env, container_size) + container_urdf = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_urdf, replace) + env.add_object(container_urdf, container_pose, 'fixed') + self.add_corner_anchor_for_pose(env, container_pose) + + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['blue']] + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), (0, 0.05, 0.03), (0, -0.025, 0.08), (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(container_pose, i), container_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, green, blue). + self.add_goal(objs=blocks[2:5], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(blocks=""the green and blue blocks"", + row=""bottom"")) + + # Goal: blocks are stacked in a pyramid (middle row: red, red). + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, + language_goal=self.lang_template.format(blocks=""the red blocks"", + row=""middle"")) + + # Goal: blocks are stacked in a pyramid (top row: blue). + self.add_goal(objs=blocks[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=self.lang_template.format(blocks=""the blue block"", + row=""top"")) +``` + +###" +" ""Four-corner-pyramid-challenge"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""Four-corner-pyramid-challenge"" in a python code block. +### + +"," + Four-corner-pyramid-challenge: {'task-name': 'Four-corner-pyramid-challenge', 'task-description': ""A tabletop is partitioned into four different zones using the 'zone/zone.urdf' asset. In each zone, there are four blocks of different colors (red, blue, green, and yellow). The task is to construct a pyramid of blocks in each zone using the 'block/block.urdf' asset such that the sequence of blocks from bottom to top is red, blue, green, and yellow. The task is challenging because it requires precise stack control and color recognition."", 'assets-used': ['block/block.urdf', 'zone/zone.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class FourCornerPyramidChallenge(Task): + """"""Construct a pyramid of blocks in each zone with a specific color sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""build a pyramid of blocks in each zone with the sequence red, blue, green, and yellow from bottom to top"" + self.task_completed_desc = ""done building pyramids."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for _ in range(4): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + for _ in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(zone_pose, i), zone_pose[1]) for i in place_pos for zone_pose in zone_poses] + + # Goal: blocks are stacked in a pyramid in each zone. + for i in range(4): + self.add_goal(objs=blocks[i*4:(i+1)*4], matches=np.ones((4, 4)), targ_poses=targs[i*4:(i+1)*4], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*4, + language_goal=self.lang_template) +``` + +###" +" ""colorful-block-tower-on-cylinder-base"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""colorful-block-tower-on-cylinder-base"" in a python code block. +### + +"," + colorful-block-tower-on-cylinder-base: {'task-name': 'colorful-block-tower-on-cylinder-base', 'task-description': 'Construct a tower using four blocks of different colors (red, blue, green, and yellow) on a placed cylindrical base at the corner of the tabletop. The sequence from bottom to top should be red, blue, green, and yellow.', 'assets-used': ['block/block.urdf', 'corner/corner-template.urdf', 'cylinder/cylinder-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorfulBlockTowerOnCylinderBase(Task): + """"""Construct a tower using four blocks of different colors (red, blue, green, and yellow) on a placed cylindrical base at the corner of the tabletop. The sequence from bottom to top should be red, blue, green, and yellow."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""construct a tower using four blocks of different colors (red, blue, green, and yellow) on a placed cylindrical base at the corner of the tabletop. The sequence from bottom to top should be red, blue, green, and yellow."" + self.task_completed_desc = ""done building the tower."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add cylindrical base. + # x, y, z dimensions for the asset size + base_size = (0.05, 0.05, 0.05) + base_urdf = 'cylinder/cylinder-template.urdf' + base_pose = self.get_random_pose(env, base_size) + base_id = env.add_object(base_urdf, base_pose, 'fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + + objs = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, 0, 0.05), (0, 0, 0.09), (0, 0, 0.13), (0, 0, 0.17)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked on the cylindrical base in the order red, blue, green, yellow from bottom to top. + for i in range(4): + self.add_goal(objs=[objs[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) +``` + +###" +" ""construct-corner-blocks"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""construct-corner-blocks"" in a python code block. +### + +"," + construct-corner-blocks: {'task-name': 'construct-corner-blocks', 'task-description': 'Create a corner structure using four blocks. Two red blocks form the base, one on each side of the corner, followed by a green block that is positioned on the red blocks at the corner junction, and finally a blue block on top of the green one. The overall structure forms a 3-D corner.', 'assets-used': ['block/block.urdf', 'corner/corner-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ConstructCornerBlocks(Task): + """"""Create a corner structure using four blocks. Two red blocks form the base, one on each side of the corner, followed by a green block that is positioned on the red blocks at the corner junction, and finally a blue block on top of the green one. The overall structure forms a 3-D corner."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Create a corner structure using four blocks. Two red blocks form the base, one on each side of the corner, followed by a green block that is positioned on the red blocks at the corner junction, and finally a blue block on top of the green one. The overall structure forms a 3-D corner."" + self.task_completed_desc = ""done constructing corner blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add corner. + corner_size = (0.15, 0.15, 0.05) + corner_urdf = 'corner/corner-template.urdf' + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, 'fixed') + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue']] + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0.05, 0.02), (0, 0, 0.06), (0, 0, 0.10)] + targs = [(utils.apply(corner_pose, i), corner_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a corner (bottom row: two red blocks). + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a corner (middle row: one green block). + self.add_goal(objs=blocks[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*1, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a corner (top row: one blue block). + self.add_goal(objs=blocks[3:], matches=np.ones((1, 1)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*1, + language_goal=self.lang_template) + +``` + +###" +" ""corner-sort-cylinders"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""corner-sort-cylinders"" in a python code block. +### + +"," + corner-sort-cylinders: {'task-name': 'corner-sort-cylinders', 'task-description': 'Pick up cylinders of four different colors (red, blue, green, yellow) and place them into four corners accordingly marked on the tabletop. The corner is designed as a 2-block-size square where only one cylinder can fit. The task is challenging due to precise placement and the need for identifying the corners accurately.', 'assets-used': ['corner/corner-template.urdf', 'cylinder/cylinder-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CornerSortCylinders(Task): + """"""Pick up cylinders of four different colors (red, blue, green, yellow) and place them into four corners accordingly marked on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} cylinder in the {color} corner"" + self.task_completed_desc = ""done sorting cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors + colors = ['red', 'blue', 'green', 'yellow'] + + # Add corners + corner_size = (0.04, 0.04, 0.04) # x, y, z dimensions for the asset size + corner_template = 'corner/corner-template.urdf' + corner_poses = [] + for color in colors: + replace = {'DIM': corner_size, 'HALF': (corner_size[0] / 2, corner_size[1] / 2, corner_size[2] / 2), 'COLOR': utils.COLORS[color]} + corner_urdf = self.fill_template(corner_template, replace) + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, 'fixed') + corner_poses.append(corner_pose) + + # Add cylinders + cylinder_size = (0.02, 0.02, 0.06) # x, y, z dimensions for the asset size + cylinder_template = 'cylinder/cylinder-template.urdf' + cylinders = [] + for color in colors: + replace = {'DIM': cylinder_size, 'HALF': (cylinder_size[0] / 2, cylinder_size[1] / 2, cylinder_size[2] / 2), 'COLOR': utils.COLORS[color]} + cylinder_urdf = self.fill_template(cylinder_template, replace) + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose) + cylinders.append(cylinder_id) + + # Add goals + for i in range(len(cylinders)): + self.add_goal(objs=[cylinders[i]], matches=np.int32([[1]]), targ_poses=[corner_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(cylinders), + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""sorting-blocks-into-pallets"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""sorting-blocks-into-pallets"" in a python code block. +### + +"," + sorting-blocks-into-pallets: {'task-name': 'sorting-blocks-into-pallets', 'task-description': 'Pick up blocks of four different colors (red, blue, green, yellow) and place them into four separate pallets of matching color. The pallets are placed in a row and the blocks are scattered randomly on the table.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SortingBlocksIntoPallets(Task): + """"""Pick up blocks of four different colors (red, blue, green, yellow) and place them into four separate pallets of matching color. The pallets are placed in a row and the blocks are scattered randomly on the table."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""put the {color} block into the {color} pallet"" + self.task_completed_desc = ""done sorting blocks into pallets."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallets. + # x, y, z dimensions for the asset size + pallet_size = (0.12, 0.12, 0.02) + pallet_urdf = 'pallet/pallet.urdf' + pallet_poses = [] + pallet_colors = ['red', 'blue', 'green', 'yellow'] + for color in pallet_colors: + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed', color=utils.COLORS[color]) + pallet_poses.append(pallet_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + for color in pallet_colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in a different pallet of matching color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[pallet_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), + language_goal=self.lang_template.format(color=pallet_colors[i])) +``` + +###" +" ""sort-and-assemble-block-castle"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""sort-and-assemble-block-castle"" in a python code block. +### + +"," + sort-and-assemble-block-castle: {'task-name': 'sort-and-assemble-block-castle', 'task-description': 'On a tabletop, there are twelve blocks of three different colors (four red, four green, and four blue). The task involves sorting the blocks according to the color in three marked zones on the tabletop and subsequently constructing a castle in each zone. In each castle, the first layer should consist of the two blocks of the same color, followed by the second layer of one block and finally the last block on the top forming a castle-like structure. The challenge lies in the color-based sorting and careful assembly of the blocks to avoid toppling.', 'assets-used': ['block/block.urdf', 'zone/zone.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SortAndAssembleBlockCastle(Task): + """"""Sort blocks by color and assemble them into a castle-like structure."""""" + + def __init__(self): + super().__init__() + self.max_steps = 50 + self.lang_template = ""sort the blocks by color and assemble them into a castle"" + self.task_completed_desc = ""done sorting and assembling."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for _ in range(3): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue']] + blocks = [] + for color in block_colors: + for _ in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Goal: each block is in a different zone based on color. + for i in range(12): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 3)), targ_poses=zone_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/12) + + # Goal: blocks are stacked in a pyramid in each zone. + for i in range(3): + zone_blocks = blocks[i*4:(i+1)*4] + place_pos = [(0, -0.02, 0.02), (0, 0.02, 0.02), + (0, 0, 0.06), (0, 0, 0.10)] + targs = [(utils.apply(zone_poses[i], pos), zone_poses[i][1]) for pos in place_pos] + for j in range(4): + self.add_goal(objs=[zone_blocks[j]], matches=np.ones((1, 1)), targ_poses=[targs[j]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/12, language_goal=self.lang_template) +``` + +###" +" ""vertical-insertion-blocks"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""vertical-insertion-blocks"" in a python code block. +### + +"," + vertical-insertion-blocks: {'task-name': 'vertical-insertion-blocks', 'task-description': 'The task involves picking up four color specific blocks (red, blue, green, and yellow) and inserting each block into four differently colored stands set upright on the tabletop. The block-colored with red needs to be inserted into the red-colored stand, and the same sequence is maintained for each colored blocks and stands. This task is challenging due to the requirement for precise insertion and the manipulation of vertical objects.', 'assets-used': ['stacking/block.urdf', 'stacking/stand.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class VerticalInsertionBlocks(Task): + """"""Pick up four color specific blocks and insert each block into four differently colored stands set upright on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert the {color} block into the {color} stand"" + self.task_completed_desc = ""done inserting blocks into stands."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for blocks and stands + colors = ['red', 'blue', 'green', 'yellow'] + + # Add stands. + # x, y, z dimensions for the asset size + stand_size = (0.04, 0.04, 0.1) + stand_urdf = 'stacking/stand.urdf' + stands = [] + for color in colors: + stand_pose = self.get_random_pose(env, stand_size) + stand_id = env.add_object(stand_urdf, stand_pose, color=utils.COLORS[color], category='fixed') + stands.append(stand_id) + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + blocks = [] + for color in colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is inserted into the stand of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(stands[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""color-coordinated-sphere-insertion"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-sphere-insertion"" in a python code block. +### + +"," + color-coordinated-sphere-insertion: {'task-name': 'color-coordinated-sphere-insertion', 'task-description': 'There are four spheres and four boxes of different colors (red, blue, green, and yellow). Each sphere is inside a box, but not corresponding to the color of the box. The task is to pick up each sphere and place it in the box of the same color. The challenge lies in the precise placement and color matching.', 'assets-used': ['sphere/sphere.urdf', 'box/box-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedSphereInsertion(Task): + """"""Insert each sphere into the bowl of the same color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert each sphere into the bowl of the same color"" + self.task_completed_desc = ""done inserting spheres into bowls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and corresponding names + colors = ['red', 'blue', 'green', 'yellow'] + color_values = [utils.COLORS[color] for color in colors] + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0.02) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for i in range(4): + bowl_pose = self.get_random_pose(env, bowl_size) + env.add_object(bowl_urdf, bowl_pose, 'fixed', color=color_values[i]) + bowl_poses.append(bowl_pose) + + # Add spheres. + # x, y, z dimensions for the asset size + sphere_size = (0.04, 0.04, 0.04) + sphere_template = 'sphere/sphere-template.urdf' + spheres = [] + for i in range(4): + sphere_pose = self.get_random_pose(env, sphere_size) + replace = {'DIM': sphere_size, 'HALF': (sphere_size[0] / 2, sphere_size[1] / 2, sphere_size[2] / 2)} + sphere_urdf = self.fill_template(sphere_template, replace) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=color_values[i]) + spheres.append(sphere_id) + + # Goal: each sphere is in a bowl of the same color. + for i in range(4): + self.add_goal(objs=[spheres[i]], matches=np.ones((1, 1)), targ_poses=[bowl_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=f""insert the {colors[i]} sphere into the {colors[i]} bowl"") +``` + +###" +" ""block-pyramid-with-limited-space"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""block-pyramid-with-limited-space"" in a python code block. +### + +"," + block-pyramid-with-limited-space: {'task-name': 'block-pyramid-with-limited-space', 'task-description': 'On a tabletop, there are twelve blocks of four different colors (three red, three green, three blue, three yellow). Three zones are defined, each with a triangle-shaped border that is marked. The task involves sorting the blocks according to the color into three zones on the tabletop and constructing a pyramid in each zone. In each pyramid, the base should contain two blocks of the same color, followed by the second layer of one block, thus forming a pyramid-like structure. However, the third yellow block should be placed in the center of the zones forming a smaller pyramid. The challenge lies in the color-based sorting, careful assembly of the blocks to avoid topple, and limited space in the zones which adds to the complexity.', 'assets-used': ['block/block.urdf', 'zone/zone.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BlockPyramidWithLimitedSpace(Task): + """"""Sort blocks according to color into three zones on the tabletop and construct a pyramid in each zone."""""" + + def __init__(self): + super().__init__() + self.max_steps = 50 + self.lang_template = ""sort the blocks according to color into three zones and construct a pyramid in each zone"" + self.task_completed_desc = ""done sorting and constructing pyramids."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for _ in range(3): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['yellow'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for color in colors: + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(zone_pose, i), zone_pose[1]) for zone_pose in zone_poses for i in place_pos] + + # Goal: blocks are sorted and stacked in a pyramid in each zone. + for i in range(3): + self.add_goal(objs=blocks[i*3:(i+1)*3], matches=np.ones((3, 3)), targ_poses=targs[i*3:(i+1)*3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) +``` + +###" +" ""build-cylinder-structure"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""build-cylinder-structure"" in a python code block. +### + +"," + build-cylinder-structure: {'task-name': 'build-cylinder-structure', 'task-description': ""Using four colored cylinders (red, blue, green, yellow), construct a structure atop a square base. The red and blue cylinders should be sealed by the square base side by side, while the green cylinder should be on top of the blue one, and the yellow one on top of the red. The final look should resemble the letter 'H'."", 'assets-used': ['cylinder/cylinder-template.urdf', 'square/square-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildCylinderStructure(Task): + """"""Construct a structure using four colored cylinders (red, blue, green, yellow) on a square base."""""" + + def __init__(self): + super().__init__() + self.max_steps = 5 + self.lang_template = ""construct a structure using four colored cylinders on a square base"" + self.task_completed_desc = ""done building the cylinder structure."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add square base. + # x, y, z dimensions for the asset size + base_size = (0.15, 0.15, 0.005) + base_urdf = 'square/square-template.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Cylinder colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow'] + ] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.08) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + + objs = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=colors[i]) + objs.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.04), (0, 0.05, 0.04), + (0, 0.05, 0.12), (0, -0.05, 0.12)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: red and blue cylinders are placed side by side on the base. + self.add_goal(objs=objs[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, + language_goal=""place the red and blue cylinders side by side on the base"") + + # Goal: green cylinder is placed on top of the blue cylinder. + self.add_goal(objs=[objs[2]], matches=np.ones((1, 1)), targ_poses=[targs[2]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2], + language_goal=""place the green cylinder on top of the blue cylinder"") + + # Goal: yellow cylinder is placed on top of the red cylinder. + self.add_goal(objs=[objs[3]], matches=np.ones((1, 1)), targ_poses=[targs[3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2], + language_goal=""place the yellow cylinder on top of the red cylinder"") +``` + +###" +" ""insert-blocks-lineup"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""insert-blocks-lineup"" in a python code block. +### + +"," + insert-blocks-lineup: {'task-name': 'insert-blocks-lineup', 'task-description': 'On the tabletop, there are four different color blocks (red, blue, green, and yellow), and four fixtures in corresponding colors. The task is to pick up each block and insert it into the corresponding color fixture. However, the fixtures are arranged in a straight line, with a line of small blocks serving as barrier between the fixtures and the colored blocks initially scattered on the table, providing a challenge in precise navigation and placement.', 'assets-used': ['block/block.urdf', 'insertion/fixture.urdf', 'block/small.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertBlocksLineup(Task): + """"""Pick up four different color blocks and insert them into the corresponding color fixtures."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert the {color} block into the {color} fixture"" + self.task_completed_desc = ""done inserting blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for blocks and fixtures + colors = ['red', 'blue', 'green', 'yellow'] + + # Add fixtures. + fixture_size = (0.04, 0.04, 0.04) + fixture_urdf = 'insertion/fixture.urdf' + fixture_poses = [] + for i in range(4): + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[colors[i]], category='fixed') + fixture_poses.append((fixture_pose, fixture_id)) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[colors[i]]) + blocks.append(block_id) + + # Add small blocks as barriers. + small_block_size = (0.02, 0.02, 0.02) + small_block_urdf = 'block/small.urdf' + for _ in range(10): + small_block_pose = self.get_random_pose(env, small_block_size) + env.add_object(small_block_urdf, small_block_pose) + + # Goal: each block is in the corresponding color fixture. + for i in range(4): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[fixture_poses[i][0]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""color-specific-container-fill"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-specific-container-fill"" in a python code block. +### + +"," + color-specific-container-fill: {'task-name': 'color-specific-container-fill', 'task-description': 'Arrange four colored blocks (red, blue, green, and yellow) around a pallet. Then, pick up these blocks and place them inside a container marked in the same color. The task requires precise placement, color matching, and an understanding of spatial structures.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'container/container-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorSpecificContainerFill(Task): + """"""Arrange four colored blocks (red, blue, green, and yellow) around a pallet. + Then, pick up these blocks and place them inside a container marked in the same color. + The task requires precise placement, color matching, and an understanding of spatial structures."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""put the {color} block in the {color} container"" + self.task_completed_desc = ""done arranging blocks in containers."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Define block and container colors. + colors = ['red', 'blue', 'green', 'yellow'] + + # Add blocks and containers. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + container_size = (0.12, 0.12, 0.05) + container_template = 'container/container-template.urdf' + blocks = [] + containers = [] + for color in colors: + # Add block. + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add container. + container_pose = self.get_random_pose(env, container_size) + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_template, replace) + container_id = env.add_object(container_urdf, container_pose, 'fixed', color=utils.COLORS[color]) + containers.append(container_id) + + # Goal: each block is in a container of the same color. + for i in range(len(colors)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(containers[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(colors), + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""multicolor-block-bridge"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""multicolor-block-bridge"" in a python code block. +### + +"," + multicolor-block-bridge: {'task-name': 'multicolor-block-bridge', 'task-description': 'Build a bridge by stacking three red, three blue, and three green blocks on a pallet. Arrange in a sequence from left to right: red, blue, and green. Then, place three cylinders of corresponding colors on top of the stacked blocks, forming a bridge. The cylinders should roll from the top block to the pallet, creating a challenge of precision and control.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'cylinder/cylinder-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class MulticolorBlockBridge(Task): + """"""Build a bridge by stacking three red, three blue, and three green blocks on a pallet. + Arrange in a sequence from left to right: red, blue, and green. + Then, place three cylinders of corresponding colors on top of the stacked blocks, forming a bridge. + The cylinders should roll from the top block to the pallet, creating a challenge of precision and control."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Build a bridge by stacking three red, three blue, and three green blocks on a pallet. Arrange in a sequence from left to right: red, blue, and green. Then, place three cylinders of corresponding colors on top of the stacked blocks, forming a bridge."" + self.task_completed_desc = ""done building the bridge."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green']] + blocks = [] + for i in range(9): # 3 blocks of each color + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i // 3]) + blocks.append(block_id) + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.04) + cylinder_template = 'cylinder/cylinder-template.urdf' + cylinders = [] + for i in range(3): # 1 cylinder of each color + cylinder_pose = self.get_random_pose(env, cylinder_size) + replace = {'DIM': cylinder_size, 'HALF': (cylinder_size[0] / 2, cylinder_size[1] / 2, cylinder_size[2] / 2)} + cylinder_urdf = self.fill_template(cylinder_template, replace) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=block_colors[i]) + cylinders.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), (0, 0.05, 0.03)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked on the pallet in the order red, blue, green. + for i in range(9): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targs[i // 3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 9, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: cylinders are placed on top of the stacked blocks. + for i in range(3): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=self.lang_template) +``` + +###" +" ""pyramid-blocks-assemble"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""pyramid-blocks-assemble"" in a python code block. +### + +"," + pyramid-blocks-assemble: {'task-name': 'pyramid-blocks-assemble', 'task-description': 'Construct a pyramid using nine blocks in a specific color order on a pallet. The bottom layer should contain five blocks: red, blue, green, yellow, and orange (in that order from left to right). The middle layer should consist of three blocks: yellow, red, and blue (from left to right). The top layer should contain a single green block. The pyramid requires careful placement and color matching.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class PyramidBlocksAssemble(Task): + """"""Construct a pyramid using nine blocks in a specific color order on a pallet."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""construct a pyramid using nine blocks in a specific color order on a pallet"" + self.task_completed_desc = ""done constructing pyramid."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.35, 0.35, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'] + ] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for _ in range(9): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [ + (-0.1, -0.1, 0.02), (0, -0.1, 0.02), (0.1, -0.1, 0.02), (-0.1, 0, 0.02), (0.1, 0, 0.02), + (-0.05, 0.05, 0.06), (0.05, 0.05, 0.06), (0, 0.1, 0.06), + (0, 0.05, 0.1) + ] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid in a specific color order. + for i in range(9): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 9, + language_goal=self.lang_template.format(blocks=""the blocks"", + row=""row"")) +``` + +###" +" ""place-ball-in-elevated-bowl"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""place-ball-in-elevated-bowl"" in a python code block. +### + +"," + place-ball-in-elevated-bowl: {'task-name': 'place-ball-in-elevated-bowl', 'task-description': 'The primary objective of the task is to pick up a red ball and carefully place it into a bowl, which is positioned on a raised platform that is surrounded by small blocks. The challenge lies in precise navigation, maintaining a hold of the ball, and avoiding the surrounding blocks.', 'assets-used': ['ball/ball-template.urdf', 'bowl/bowl.urdf', 'block/small.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class PlaceBallInElevatedBowl(Task): + """"""Pick up a red ball and carefully place it into a bowl, which is positioned on a raised platform that is surrounded by small blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""place the red ball in the elevated bowl"" + self.task_completed_desc = ""done placing ball in bowl."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add elevated platform. + platform_size = (0.3, 0.3, 0.05) + + # Add bowl on the platform. + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_pose[0][2] += platform_size[2] # place the bowl on top of the platform + bowl_id = env.add_object(bowl_urdf, bowl_pose, 'fixed') + + # Add red ball. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS['red']) + + # Add small blocks around the platform. + block_size = (0.02, 0.02, 0.02) + block_urdf = 'block/small.urdf' + for _ in range(5): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose) + + # Goal: the red ball is in the bowl. + self.add_goal(objs=[ball_id], matches=np.ones((1, 1)), targ_poses=[bowl_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, + language_goal=self.lang_template) +``` + +###" +" ""align-balls-in-colored-zones"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""align-balls-in-colored-zones"" in a python code block. +### + +"," + align-balls-in-colored-zones: {'task-name': 'align-balls-in-colored-zones', 'task-description': 'There are six balls of different colors (red, blue, green, yellow, orange, and purple) and six zones correspondingly colored. The task is to pick up each ball and place it in the zone of the same color, arranging the balls in a straight line. The challenge lies in the precise placement and color matching.', 'assets-used': ['ball/ball-template.urdf', 'zone/zone.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class AlignBallsInColoredZones(Task): + """"""Align balls of different colors in correspondingly colored zones."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} ball in the {color} zone"" + self.task_completed_desc = ""done aligning balls in colored zones."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for balls and zones + colors = ['red', 'blue', 'green', 'yellow', 'orange', 'purple'] + color_names = ['Red', 'Blue', 'Green', 'Yellow', 'Orange', 'Purple'] + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for i in range(6): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[colors[i]]) + zone_poses.append(zone_pose) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for i in range(6): + ball_pose = self.get_random_pose(env, ball_size) + replace = {'DIM': ball_size, 'HALF': (ball_size[0] / 2, ball_size[1] / 2, ball_size[2] / 2), 'COLOR': colors[i]} + ball_urdf = self.fill_template(ball_urdf, replace) + ball_id = env.add_object(ball_urdf, ball_pose) + balls.append(ball_id) + + # Goal: each ball is in a different colored zone. + for i in range(6): + self.add_goal(objs=[balls[i]], matches=np.int32([[1]]), targ_poses=[zone_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, + language_goal=self.lang_template.format(color=color_names[i])) +``` + +###" +" ""color-coordinated-cylinder-tower"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-cylinder-tower"" in a python code block. +### + +"," + color-coordinated-cylinder-tower: {'task-name': 'color-coordinated-cylinder-tower', 'task-description': 'Stack cylinders of four different colors (red, blue, green, yellow) on top of each other on a square stand in a specific sequence. The bottom of the stack should start with a blue cylinder, follow by a green cylinder, then a red one, and finally a yellow cylinder at the top. Each cylinder has to be aligned correctly to avoid falling.', 'assets-used': ['cylinder/cylinder-template.urdf', 'stacking/stand.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedCylinderTower(Task): + """"""Stack cylinders of four different colors (red, blue, green, yellow) on top of each other on a square stand in a specific sequence. The bottom of the stack should start with a blue cylinder, follow by a green cylinder, then a red one, and finally a yellow cylinder at the top. Each cylinder has to be aligned correctly to avoid falling."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""Stack cylinders of four different colors (red, blue, green, yellow) on top of each other on a square stand in a specific sequence. The bottom of the stack should start with a blue cylinder, follow by a green cylinder, then a red one, and finally a yellow cylinder at the top."" + self.task_completed_desc = ""done stacking cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Cylinder colors. + colors = [utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['red'], utils.COLORS['yellow']] + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + + objs = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=colors[i]) + objs.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0, 0, 0.03), (0, 0, 0.08), (0, 0, 0.13), (0, 0, 0.18)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: cylinders are stacked in a tower (bottom to top: blue, green, red, yellow). + for i in range(4): + self.add_goal(objs=[objs[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) +``` + +###" +" ""symmetric-block-bridge-construction"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""symmetric-block-bridge-construction"" in a python code block. +### + +"," + symmetric-block-bridge-construction: {'task-name': 'symmetric-block-bridge-construction', 'task-description': 'Create a symmetrical bridge-shaped structure on a stand using eight blocks of two different colors (four red and four blue). Start by placing two red blocks side by side at the center of the stand to form the base of the bridge. Then, take a blue block and place it on top of the red blocks, followed by another red block on top of the blue one, and this pattern continues till you exhaust all the blocks. The final structure should be a bridge with alternating colors (red, blue, red, blue). The challenge lies in ensuring symmetry and balancing the blocks without making them fall.', 'assets-used': ['stacking/stand.urdf', 'block/block-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class SymmetricBlockBridgeConstruction(Task): + """"""Create a symmetrical bridge-shaped structure on a stand using eight blocks of two different colors (four red and four blue)."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""create a symmetrical bridge-shaped structure on a stand using eight blocks of two different colors (four red and four blue)"" + self.task_completed_desc = ""done building the bridge."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['blue']] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(8): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i%2]) + objs.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13), + (0, -0.025, 0.18), (0, 0.025, 0.18)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a bridge (bottom row: red, red). + self.add_goal(objs=objs[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*2, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (second row: blue). + self.add_goal(objs=objs[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (third row: red). + self.add_goal(objs=objs[3:4], matches=np.ones((1, 1)), targ_poses=targs[3:4], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (fourth row: blue). + self.add_goal(objs=objs[4:5], matches=np.ones((1, 1)), targ_poses=targs[4:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (fifth row: red). + self.add_goal(objs=objs[5:6], matches=np.ones((1, 1)), targ_poses=targs[5:6], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (sixth row: blue). + self.add_goal(objs=objs[6:7], matches=np.ones((1, 1)), targ_poses=targs[6:7], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (top row: red, red). + self.add_goal(objs=objs[7:], matches=np.ones((1, 1)), targ_poses=targs[7:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template) +``` + +###" +" ""sphere-align-stand"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""sphere-align-stand"" in a python code block. +### + +"," + sphere-align-stand: {'task-name': 'sphere-align-stand', 'task-description': 'On a table there are five differently colored stands and five spheres. The task involves picking up each sphere and placing it on the stand of the matching color. The task is challenging due to the precision required in picking up and placing the spheres, and the color coordination.', 'assets-used': ['stacking/stand.urdf', 'sphere/sphere.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SphereAlignStand(Task): + """"""Pick up each sphere and place it on the stand of the matching color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 5 + self.lang_template = ""place the {color} sphere on the {color} stand"" + self.task_completed_desc = ""done aligning spheres with stands."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for the spheres and stands + colors = ['red', 'green', 'blue', 'yellow', 'purple'] + color_names = ['red', 'green', 'blue', 'yellow', 'purple'] + + # Add stands. + # x, y, z dimensions for the asset size + stand_size = (0.05, 0.05, 0.05) + stand_urdf = 'stacking/stand.urdf' + stand_poses = [] + for i in range(5): + stand_pose = self.get_random_pose(env, stand_size) + env.add_object(stand_urdf, stand_pose, 'fixed', color=utils.COLORS[colors[i]]) + stand_poses.append(stand_pose) + + # Add spheres. + # x, y, z dimensions for the asset size + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + spheres = [] + for i in range(5): + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS[colors[i]]) + spheres.append(sphere_id) + + # Goal: each sphere is on the stand of the matching color. + for i in range(5): + self.add_goal(objs=[spheres[i]], matches=np.ones((1, 1)), targ_poses=[stand_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/5, + language_goal=self.lang_template.format(color=color_names[i])) +``` + +###" +" ""construct-colorful-arch"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""construct-colorful-arch"" in a python code block. +### + +"," + construct-colorful-arch: {'task-name': 'construct-colorful-arch', 'task-description': 'Construct an arch using six blocks: three red, and three blue. Place two red blocks on the tabletop parallel to each other, then place a blue block on top of the red blocks to form a basic arch. Repeat the process by placing a red block on each side of the base arch and bridge them with the last blue block. Finally, place the last two red blocks on top of the second layer of the red blocks, on either side. The overall structure forms a colorful 3D arch.', 'assets-used': ['block/block.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ConstructColorfulArch(Task): + """"""Construct an arch using six blocks: three red, and three blue."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Construct an arch using six blocks: three red, and three blue."" + self.task_completed_desc = ""done constructing colorful arch."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + colors = [utils.COLORS['red'], utils.COLORS['blue']] + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + color = colors[i // 3] # First three blocks are red, last three are blue + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0.05, 0.02), # Base layer + (0, 0, 0.06), # Second layer + (0, -0.05, 0.10), (0, 0.05, 0.10), # Third layer + (0, 0, 0.14)] # Top layer + targs = [(utils.apply(block_pose, i), block_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in an arch (bottom layer: red, red). + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, + language_goal=""Place two red blocks on the tabletop parallel to each other"") + + # Goal: blocks are stacked in an arch (second layer: blue). + self.add_goal(objs=blocks[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=""Place a blue block on top of the red blocks to form a basic arch"") + + # Goal: blocks are stacked in an arch (third layer: red, red). + self.add_goal(objs=blocks[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, + language_goal=""Place a red block on each side of the base arch"") + + # Goal: blocks are stacked in an arch (top layer: blue). + self.add_goal(objs=blocks[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2], + language_goal=""Bridge them with the last blue block"") +``` + +###" +" ""color-sorted-container-stack"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-sorted-container-stack"" in a python code block. +### + +"," + color-sorted-container-stack: {'task-name': 'color-sorted-container-stack', 'task-description': 'The challenge is to stack four differently colored blocks (red, blue, green, yellow) inside a container. These blocks are initially placed inside two separate bowls. The red and blue blocks are in one bowl, the yellow and green blocks are in another. The robot has to pick up the blocks from the two bowls and stack them inside the container in the specific order: red, blue, green, then yellow.', 'assets-used': ['bowl/bowl.urdf', 'container/container-template.urdf', 'block/block.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorSortedContainerStack(Task): + """"""Stack four differently colored blocks (red, blue, green, yellow) inside a container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""stack the blocks in the container in the order: red, blue, green, then yellow"" + self.task_completed_desc = ""done stacking blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + # x, y, z dimensions for the asset size + container_size = (0.15, 0.15, 0.15) + container_pose = self.get_random_pose(env, container_size) + container_urdf = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_urdf, replace) + env.add_object(container_urdf, container_pose, 'fixed') + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + for i in range(2): + bowl_pose = self.get_random_pose(env, bowl_size) + env.add_object(bowl_urdf, bowl_pose, 'fixed') + + # Goal: each block is stacked in the container in the order: red, blue, green, yellow. + for i in range(4): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, + language_goal=self.lang_template) +``` + +###" +" ""align-spheres-in-colored-zones"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""align-spheres-in-colored-zones"" in a python code block. +### + +"," + align-spheres-in-colored-zones: {'task-name': 'align-spheres-in-colored-zones', 'task-description': 'There are four spheres of different colors (red, blue, green, yellow) positioned randomly on the table along with four zones marked with matching colors. The task is to pick up each sphere and place it into the matching colored zone with precise placement.', 'assets-used': ['sphere/sphere-template.urdf', 'zone/zone.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class AlignSpheresInColoredZones(Task): + """"""Align spheres of different colors in the matching colored zones."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} sphere in the {color} zone"" + self.task_completed_desc = ""done aligning spheres in colored zones."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors + colors = ['red', 'blue', 'green', 'yellow'] + color_names = ['red', 'blue', 'green', 'yellow'] + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for color in colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add spheres. + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere-template.urdf' + spheres = [] + for i, color in enumerate(colors): + sphere_pose = self.get_random_pose(env, sphere_size) + replace = {'DIM': sphere_size, 'HALF': (sphere_size[0] / 2, sphere_size[1] / 2, sphere_size[2] / 2)} + sphere_urdf = self.fill_template(sphere_urdf, replace) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS[color]) + spheres.append(sphere_id) + + # Add goal + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[zone_poses[i]], replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1, + language_goal=self.lang_template.format(color=color_names[i])) +``` + +###" +" ""sort-insert-color-coordinated-blocks"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""sort-insert-color-coordinated-blocks"" in a python code block. +### + +"," + sort-insert-color-coordinated-blocks: {'task-name': 'sort-insert-color-coordinated-blocks', 'task-description': 'There are six blocks of three different colors (two red, two blue, and two green), two red containers, two blue containers, and two green containers scattered on the table. The task is to sort the blocks by their colors and place (insert) them into the containers of the matching color. Two blocks of the same color should go into two different containers of the same color. The challenge lies in the color-based sorting and insertion of the blocks into the containers.', 'assets-used': ['block/block.urdf', 'container/container-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class SortInsertColorCoordinatedBlocks(Task): + """"""Sort blocks by their colors and place them into the containers of the matching color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""sort the blocks by their colors and place them into the containers of the matching color"" + self.task_completed_desc = ""done sorting and inserting blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add containers. + container_size = (0.12, 0.12, 0.12) + container_size = (0.1, 0.1, 0.1) + container_pose = self.get_random_pose(env, container_size) + container_urdf = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_urdf, replace) + container_colors = ['red', 'blue', 'green'] + container_poses = [] + for color in container_colors: + container_pose = self.get_random_pose(env, container_size) + env.add_object(container_urdf, container_pose, color=utils.COLORS[color]) + container_poses.append(container_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = ['red', 'red', 'blue', 'blue', 'green', 'green'] + blocks = [] + for color in block_colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in a container of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[container_poses[i//2]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), language_goal=self.lang_template) +``` + +###" +" ""color-ordered-insertion"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-ordered-insertion"" in a python code block. +### + +"," + color-ordered-insertion: {'task-name': 'color-ordered-insertion', 'task-description': 'There are four differently-colored ell objects (red, blue, green, yellow) and a corresponding set of color-coded fixtures. The task involves picking up each ell object and inserting it into the matching color fixture in a specific order: from left to right, insert red, blue, green, and finally yellow. The challenge lies in the precise manipulation of the ell objects and the color-coordination required. The fixtures are arranged in a straight line, and can only be approached from one direction, demanding careful navigation.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorOrderedInsertion(Task): + """"""Insert differently-colored ell objects into the matching color fixture in a specific order."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert the {color} ell into the matching fixture"" + self.task_completed_desc = ""done inserting ells."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their order + colors = ['red', 'blue', 'green', 'yellow'] + color_order = {color: i for i, color in enumerate(colors)} + + # Add fixtures. + fixture_size = (0.12, 0.12, 0.02) + fixture_urdf = 'insertion/fixture.urdf' + fixtures = [] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color], category='fixed') + fixtures.append(fixture_id) + + # Add ell objects. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + ells = [] + for color in colors: + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[color]) + ells.append(ell_id) + + # Goal: each ell is inserted into the matching color fixture in the correct order. + for i, ell in enumerate(ells): + self.add_goal(objs=[ell], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(ells), + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""color-coordinated-insertion"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-insertion"" in a python code block. +### + +"," + color-coordinated-insertion: {'task-name': 'color-coordinated-insertion', 'task-description': 'There are three insertion fixtures and three ell shaped blocks of different colors (red, blue, green) on the table top. The task is to pick up the ell shaped blocks and insert each one of them into the fixture of the same color. However, the ell blocks should be inserted in a specific sequence - red first, then blue, and finally green. This task is challenging due to the precision required for insertion and the need for color coordination and sequencing.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedInsertion(Task): + """"""Insert each block into the fixture of the same color"""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert each block into the fixture of the same color"" + self.task_completed_desc = ""done with color-coordinated-insertion."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.35, 0.35, 0.01) + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_urdf = 'pallet/pallet.urdf' + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add fixtures and blocks. + colors = ['red', 'blue', 'green', 'yellow'] + fixtures = [] + blocks = [] + fixture_size = (0.05, 0.05, 0.05) + block_size = (0.04, 0.04, 0.04) + fixture_urdf = 'insertion/fixture.urdf' + block_urdf = 'block/block.urdf' + for color in colors: + # Add fixture. + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color]) + fixtures.append(fixture_id) + + # Add block. + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in the fixture of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(blocks), + language_goal=self.lang_template) + + # Goal: each fixture is on the pallet. + for i in range(len(fixtures)): + self.add_goal(objs=[fixtures[i]], matches=np.ones((1, 1)), targ_poses=[pallet_pose], replace=False, + rotations=True, metric='zone', params=[(pallet_pose, pallet_size)], step_max_reward=1 / len(fixtures), + language_goal=self.lang_template) +``` + +###" +" ""cylinder-stand-alignment"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""cylinder-stand-alignment"" in a python code block. +### + +"," + cylinder-stand-alignment: {'task-name': 'cylinder-stand-alignment', 'task-description': 'Arrange four colored cylinders (red, blue, green, yellow) in order of their colors on four stands of matching color. However, the stands are placed in a random order on the table, which increases the complexity of the task as it requires careful planning and color matching.', 'assets-used': ['cylinder/cylinder-template.urdf', 'stacking/stand.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CylinderStandAlignment(Task): + """"""Arrange four colored cylinders (red, blue, green, yellow) in order of their colors on four stands of matching color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Arrange the {color} cylinder on the {color} stand"" + self.task_completed_desc = ""done arranging cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and corresponding names + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + color_names = ['red', 'blue', 'green', 'yellow'] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + replace = {'DIM': cylinder_size, 'HALF': (cylinder_size[0] / 2, cylinder_size[1] / 2, cylinder_size[2] / 2), + 'COLOR': colors[i]} + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(cylinder_urdf, replace) + cylinder_id = env.add_object(urdf, cylinder_pose) + cylinders.append(cylinder_id) + + # Add stands. + # x, y, z dimensions for the asset size + stand_size = (0.05, 0.05, 0.005) + stand_urdf = 'stacking/stand.urdf' + stands = [] + for i in range(4): + stand_pose = self.get_random_pose(env, stand_size) + env.add_object(stand_urdf, stand_pose, color=colors[i], category='fixed') + stands.append(stand_pose) + + # Goal: each cylinder is on a stand of the same color. + for i in range(4): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[stands[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, + language_goal=self.lang_template.format(color=color_names[i])) +``` + +###" +" ""color-sorted-block-race"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-sorted-block-race"" in a python code block. +### + +"," + color-sorted-block-race: {'task-name': 'color-sorted-block-race', 'task-description': 'On one end of a tabletop, there are six blocks in two colors (three red and three blue). On the other end of the tabletop, two sets of three small marked zones are arranged in a straight line, one set for blue and one set for red. The task involves picking up one block at a time and placing it in the corresponding colored zone in a sequence from the bottom end zone to the top end zone. The blocks must be placed following the rule: the three colored blocks must be transported consecutively, e.g., first place all three blue blocks and then place all three red blocks. The challenge lies in careful transportation and placement of the blocks and follows the specific rule.', 'assets-used': ['block/block.urdf', 'zone/zone.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorSortedBlockRace(Task): + """"""Pick up blocks of two colors and place them in corresponding colored zones in a sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the blocks in the corresponding colored zones in sequence"" + self.task_completed_desc = ""done placing blocks in zones."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_colors = ['blue', 'red'] + zone_poses = [] + for color in zone_colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = ['blue', 'red'] + blocks = [] + for color in block_colors: + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in the corresponding colored zone. + for i, block in enumerate(blocks): + self.add_goal(objs=[block], matches=np.ones((1, 1)), targ_poses=[zone_poses[i//3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), + language_goal=self.lang_template) +``` + +###" +" ""multi-level-block-construction"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""multi-level-block-construction"" in a python code block. +### + +"," + multi-level-block-construction: {'task-name': 'multi-level-block-construction', 'task-description': 'Construct a two-level structure on a pallet using four blocks: two red and two blue. The lower level should be a rectangle created by placing the red blocks side by side. The upper level is made up by placing the blue blocks placed on top of the red blocks creating a line aligned perpendicular to the red blocks. The challenge lies in the precise placement of blocks, maintaining balance of the structure, and correct color arrangement.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class MultiLevelBlockConstruction(Task): + """"""Construct a two-level structure on a pallet using four blocks: two red and two blue. + The lower level should be a rectangle created by placing the red blocks side by side. + The upper level is made up by placing the blue blocks placed on top of the red blocks + creating a line aligned perpendicular to the red blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""construct a two-level structure on a pallet using four blocks: two red and two blue"" + self.task_completed_desc = ""done constructing multi-level block structure."" + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.015) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['blue']] + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.02, 0.02), (0, 0.02, 0.02), + (0, -0.02, 0.06), (0, 0.02, 0.06)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: red blocks are placed side by side on the pallet. + self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, language_goal=self.lang_template) + + # Goal: blue blocks are stacked on top of the red blocks. + self.add_goal(objs=blocks[2:], matches=np.ones((2, 2)), targ_poses=targs[2:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, language_goal=self.lang_template) +``` + +###" +" ""color-blocks-in-cylinder-maze"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-blocks-in-cylinder-maze"" in a python code block. +### + +"," + color-blocks-in-cylinder-maze: {'task-name': 'color-blocks-in-cylinder-maze', 'task-description': 'Pick up five differently colored blocks (red, blue, yellow, green, and orange) that are scattered randomly on the table top. Arrange three cylindrical containers in a row to create a maze-like structure. Place the red, yellow, and blue block into the first, second, and third cylinder from left respectively. Then, stack the green and orange block on top of any container, followed by placing the same color palette on the respective block.', 'assets-used': ['block/block.urdf', 'cylinder/cylinder-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorBlocksInCylinderMaze(Task): + """"""Pick up five differently colored blocks (red, blue, yellow, green, and orange) that are scattered randomly on the table top. Arrange three cylindrical containers in a row to create a maze-like structure. Place the red, yellow, and blue block into the first, second, and third cylinder from left respectively. Then, stack the green and orange block on top of any container, followed by placing the same color palette on the respective block."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""arrange the blocks in the cylinders and stack the green and orange blocks"" + self.task_completed_desc = ""done arranging blocks in cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add cylinders. + cylinder_size = (0.05, 0.05, 0.1) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinder_poses = [] + for _ in range(3): + cylinder_pose = self.get_random_pose(env, cylinder_size) + env.add_object(cylinder_urdf, cylinder_pose, 'fixed') + cylinder_poses.append(cylinder_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['yellow'], utils.COLORS['green'], utils.COLORS['orange']] + blocks = [] + for i in range(5): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_colors[i]) + blocks.append(block_id) + + # Goal: red, yellow, and blue blocks are in the first, second, and third cylinder respectively. + self.add_goal(objs=blocks[:3], matches=np.ones((3, 3)), targ_poses=cylinder_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, language_goal=self.lang_template) + + # Goal: green and orange blocks are stacked on top of any cylinder. + self.add_goal(objs=blocks[3:], matches=np.ones((2, 3)), targ_poses=cylinder_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, language_goal=self.lang_template) +``` + +###" +" ""create-pyramid-with-color-coded-ells"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""create-pyramid-with-color-coded-ells"" in a python code block. +### + +"," + create-pyramid-with-color-coded-ells: {'task-name': 'create-pyramid-with-color-coded-ells', 'task-description': ""There are four insertion ell-shaped objects ('insertion/ell.urdf') of different colors (red, blue, yellow, and green) placed randomly on the tabletop. The task is to pick up each of these objects and stack them onto a fixed-size pallet in the shape of a pyramid. The order of the pyramid from bottom to top should be red, blue, yellow, and green."", 'assets-used': ['insertion/ell.urdf', 'pallet/pallet.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class CreatePyramidWithColorCodedElls(Task): + """"""Pick up ell-shaped objects of different colors and stack them onto a pallet in the shape of a pyramid."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""stack the {color} ell on the pyramid"" + self.task_completed_desc = ""done stacking ell pyramid."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.15, 0.15, 0.01) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + + # Ell colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], + utils.COLORS['yellow'], utils.COLORS['green'] + ] + color_names = ['red', 'blue', 'yellow', 'green'] + + # Add Ells. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + objs = [] + for i in range(4): + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=colors[i]) + objs.append(ell_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, 0, 0.08)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: Ells are stacked in a pyramid (bottom row: red, middle row: blue, top row: yellow, green). + for i in range(4): + self.add_goal(objs=[objs[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2], + language_goal=self.lang_template.format(color=color_names[i])) +``` + +###" +" ""move-piles-along-line"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""move-piles-along-line"" in a python code block. +### + +"," + move-piles-along-line: {'task-name': 'move-piles-along-line', 'task-description': 'Move three piles of small blocks, each pile a different color (red, blue, green), along three matching colored lines to three separate zones of the same color using a spatula.', 'assets-used': ['block/small.urdf', 'zone/zone.urdf', 'line/line-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class MovePilesAlongLine(Task): + """"""Move three piles of small blocks, each pile a different color (red, blue, green), + along three matching colored lines to three separate zones of the same color using a spatula."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""move the piles of blocks along the lines to the matching colored zones"" + self.task_completed_desc = ""done moving piles."" + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add three colored lines. + line_template = 'line/line-template.urdf' + line_colors = ['red', 'blue', 'green'] + line_poses = [] + for color in line_colors: + line_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + line_pose = self.get_random_pose(env, line_size) + replace = {'DIM': line_size, 'HALF': (line_size[0] / 2, line_size[1] / 2, line_size[2] / 2), 'COLOR': color} + line_urdf = self.fill_template(line_template, replace) + env.add_object(line_urdf, line_pose, 'fixed') + line_poses.append(line_pose) + + # Add three colored zones. + zone_template = 'zone/zone.urdf' + zone_poses = [] + for color in line_colors: + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2), 'COLOR': color} + zone_urdf = self.fill_template(zone_template, replace) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Add three piles of small blocks. + block_template = 'block/small.urdf' + block_colors = ['red', 'blue', 'green'] + block_ids = [] + for color in block_colors: + block_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + block_pose = self.get_random_pose(env, block_size) + replace = {'DIM': block_size, 'HALF': (block_size[0] / 2, block_size[1] / 2, block_size[2] / 2), 'COLOR': color} + block_urdf = self.fill_template(block_template, replace) + block_id = env.add_object(block_urdf, block_pose) + block_ids.append(block_id) + + # Add goals. + for i in range(3): + self.add_goal(objs=[block_ids[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i]], replace=False, + rotations=False, metric='zone', params=[(zone_poses[i], zone_size)], step_max_reward=1/3, + language_goal=self.lang_template) +``` + +###" +" ""color-ordered-blocks-on-pallet"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-ordered-blocks-on-pallet"" in a python code block. +### + +"," + color-ordered-blocks-on-pallet: {'task-name': 'color-ordered-blocks-on-pallet', 'task-description': 'On a table there are six different colored blocks (red, blue, green, yellow, orange, and purple), a pallet, and a small corner structure. These colored blocks are arranged randomly within the small corner structure. The task involves picking up each colored block and placing it onto the pallet in specific color sequence: red, blue, green, yellow, orange, and finally purple.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'corner/corner-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorOrderedBlocksOnPallet(Task): + """"""Pick up each colored block and place it onto the pallet in specific color sequence: red, blue, green, yellow, orange, and finally purple."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the colored blocks onto the pallet in the following order: red, blue, green, yellow, orange, and purple"" + self.task_completed_desc = ""done placing blocks on the pallet."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.02) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['purple'] + ] + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are placed on the pallet in the order of red, blue, green, yellow, orange, purple. + for i in range(6): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2], language_goal=self.lang_template) +``` + +###" +" ""color-ordered-container-arrangement"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-ordered-container-arrangement"" in a python code block. +### + +"," + color-ordered-container-arrangement: {'task-name': 'color-ordered-container-arrangement', 'task-description': 'On the tabletop, there are six containers and six blocks of different colors (red, blue, green, yellow, orange, purple). The task is to pick up each block and place it into a container of the same color, then arrange the containers in a line in the following color order: red, blue, green, yellow, orange, and purple.', 'assets-used': ['block/block.urdf', 'container/container-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorOrderedContainerArrangement(Task): + """"""Arrange six containers with blocks of matching colors in a specific color order."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""arrange the containers in the color order: red, blue, green, yellow, orange, and purple"" + self.task_completed_desc = ""done arranging containers."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define color order + color_order = ['red', 'blue', 'green', 'yellow', 'orange', 'purple'] + + # Add containers and blocks + container_template = 'container/container-template.urdf' + container_size = (0.12, 0.12, 0.02) + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_template, replace) + + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + containers = [] + blocks = [] + for color in color_order: + # Add container + container_pose = self.get_random_pose(env, container_size) + container_id = env.add_object(container_urdf, container_pose, color=utils.COLORS[color]) + containers.append(container_id) + + # Add block + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add subgoal to place block in container + self.add_goal(objs=[block_id], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/6, + language_goal=self.lang_template) + + # Add final goal to arrange containers in color order + container_poses = [self.get_random_pose(env, container_size) for _ in color_order] + self.add_goal(objs=containers, matches=np.eye(len(color_order)), targ_poses=container_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) +``` + +###" +" ""multi-level-pyramid-construction"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""multi-level-pyramid-construction"" in a python code block. +### + +"," + multi-level-pyramid-construction: {'task-name': 'multi-level-pyramid-construction', 'task-description': 'Construct a two-level pyramid on a pallet using six blocks: three green and three blue. The first level should be a triangle created by placing the green blocks side by side. The second level should be built by placing the blue blocks on top of the green blocks, forming another triangle rotated 60 degrees with respect to the first one. The challenge lies in the precise placement of blocks, maintaining balance of the structure, and correct color arrangement.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class MultiLevelPyramidConstruction(Task): + """"""Construct a two-level pyramid on a pallet using six blocks: three green and three blue."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Construct a two-level pyramid on a pallet using six blocks: three green and three blue. The first level should be a triangle created by placing the green blocks side by side. The second level should be built by placing the blue blocks on top of the green blocks, forming another triangle rotated 60 degrees with respect to the first one."" + self.task_completed_desc = ""done constructing pyramid."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.35, 0.35, 0.01) # x, y, z dimensions for the pallet size + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_urdf = 'pallet/pallet.urdf' + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Add blocks. + block_size = (0.04, 0.04, 0.04) # x, y, z dimensions for the block size + block_urdf = 'block/block.urdf' + block_colors = [utils.COLORS['green']] * 3 + [utils.COLORS['blue']] * 3 # three green and three blue blocks + + blocks = [] + for color in block_colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=color) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0, 0.02), (0, 0.05, 0.02), # first level + (0, -0.025, 0.06), (0, 0.025, 0.06), (0, 0, 0.10)] # second level + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (first level: green blocks). + self.add_goal(objs=blocks[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(blocks=""the green blocks"", row=""bottom"")) + + # Goal: blocks are stacked in a pyramid (second level: blue blocks). + self.add_goal(objs=blocks[3:], matches=np.ones((3, 3)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(blocks=""the blue blocks"", row=""top"")) +``` + +###" +" ""align-balls-in-colored-boxes"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""align-balls-in-colored-boxes"" in a python code block. +### + +"," + align-balls-in-colored-boxes: {'task-name': 'align-balls-in-colored-boxes', 'task-description': 'On a tabletop, there are four balls and four boxes of different colors (red, blue, green, and yellow). Each ball is inside a box, but not corresponding to the color of the box. The task is to pick up each ball and place it in the box of the same color, in the specific sequence of red, blue, green and yellow from left to right. The challenge lies in the precise placement, color matching and sequence following.', 'assets-used': ['ball/ball.urdf', 'box/box-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class AlignBallsInColoredBoxes(Task): + """"""Align balls in colored boxes according to the color and sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""put the {color} ball in the {color} box"" + self.task_completed_desc = ""done aligning balls in boxes."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their sequence + colors = ['red', 'blue', 'green', 'yellow'] + + # Add boxes. + box_size = (0.12, 0.12, 0.12) + box_urdf = 'box/box-template.urdf' + box_poses = [] + boxes = [] + for i in range(4): + box_pose = self.get_random_pose(env, box_size) + box_id = env.add_object(box_urdf, box_pose, color=utils.COLORS[colors[i]]) + boxes.append(box_id) + box_poses.append(box_pose) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball.urdf' + balls = [] + for i in range(4): + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[colors[i]]) + balls.append(ball_id) + + # Goal: each ball is in the box of the same color. + for i in range(4): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[box_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""colored-balls-sorting-in-corner"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""colored-balls-sorting-in-corner"" in a python code block. +### + +"," + colored-balls-sorting-in-corner: {'task-name': 'colored-balls-sorting-in-corner', 'task-description': 'There are four balls and four corners of different colors (red, blue, green, and yellow). Each ball is located at a corner, but not corresponding to the color of the corner. The task is to pick up each ball and place it in the corner of the same color, in the specific sequence of red, blue, green and yellow, starting from the leftmost corner to the rightmost. The challenge lies in the precise placement, color matching and sequence following.', 'assets-used': ['ball/ball-template.urdf', 'corner/corner-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColoredBallsSortingInCorner(Task): + """"""Pick up each ball and place it in the corner of the same color, in the specific sequence of red, blue, green and yellow, starting from the leftmost corner to the rightmost."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} ball in the {color} corner"" + self.task_completed_desc = ""done sorting balls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define the colors and their sequence + colors = ['red', 'blue', 'green', 'yellow'] + + # Add corners. + corner_size = (0.12, 0.12, 0) + corner_urdf = 'corner/corner-template.urdf' + corner_poses = [] + for i in range(4): + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, 'fixed', color=utils.COLORS[colors[i]]) + corner_poses.append(corner_pose) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for i in range(4): + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[colors[i]]) + balls.append(ball_id) + + # Goal: each ball is in the corner of the same color. + for i in range(4): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[corner_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""color-coordinated-ball-insertion"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-ball-insertion"" in a python code block. +### + +"," + color-coordinated-ball-insertion: {'task-name': 'color-coordinated-ball-insertion', 'task-description': 'There are five differently-colored ell objects (red, blue, green, yellow, orange) and five sphere-shaped containers of matching colors. The task involves picking up each ell object and inserting it into the sphere container of the same color, but in a specific sequence from left to right: red, blue, green, yellow, and finally orange. The task is challenging due to the sequence, color coordination, and accuracy of insertion required.', 'assets-used': ['insertion/ell.urdf', 'sphere/sphere-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedBallInsertion(Task): + """"""Insert balls into the cylinders of the same color in the order of red, blue, green, and yellow."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""insert the {color} ball into the {color} cylinder"" + self.task_completed_desc = ""done inserting balls into cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their order + colors = ['red', 'blue', 'green', 'yellow'] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.05, 0.05, 0.1) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinder_poses = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + env.add_object(cylinder_urdf, cylinder_pose, category='fixed', color=utils.COLORS[colors[i]]) + cylinder_poses.append(cylinder_pose) + + # Add balls. + # x, y, z dimensions for the asset size + balls = [] + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + for i in range(4): + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[colors[i]]) + balls.append(ball_id) + + # Goal: each ball is in the corresponding color cylinder. + for i in range(4): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[cylinder_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""color-sequenced-pyramid-packing"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-sequenced-pyramid-packing"" in a python code block. +### + +"," + color-sequenced-pyramid-packing: {'task-name': 'color-sequenced-pyramid-packing', 'task-description': 'There are twelve cubes of different colors (three red, three green, three blue, and three yellow) scattered on the tabletop. The task is to pick up the cubes, sort them according to color into four pallets, and stack them in each pallet as a pyramid with the base layer containing two cubes and the top layer containing one cube. The challenge lies in the color-based sorting, the precise placement of cubes, and the construction of the pyramid in each pallet.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorSequencedPyramidPacking(Task): + """"""Sort cubes by color into four pallets and stack them in each pallet as a pyramid"""""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = ""sort the {color} cubes into the pallet and stack them as a pyramid"" + self.task_completed_desc = ""done sorting and stacking cubes."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallets. + # x, y, z dimensions for the asset size + pallet_size = (0.15, 0.15, 0.02) + pallet_urdf = 'pallet/pallet.urdf' + pallet_poses = [] + for _ in range(4): + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + pallet_poses.append(pallet_pose) + + # Cube colors. + colors = [ + utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['yellow'] + ] + + # Add cubes. + # x, y, z dimensions for the asset size + cube_size = (0.04, 0.04, 0.04) + cube_urdf = 'block/block.urdf' + + objs = [] + for i in range(12): + cube_pose = self.get_random_pose(env, cube_size) + cube_id = env.add_object(cube_urdf, cube_pose, color=colors[i%4]) + objs.append(cube_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos for pallet_pose in pallet_poses] + + # Goal: cubes are sorted by color and stacked in a pyramid in each pallet. + for i in range(4): + self.add_goal(objs=objs[i*3:(i+1)*3], matches=np.ones((3, 3)), targ_poses=targs[i*3:(i+1)*3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(color=list(utils.COLORS.keys())[i])) +``` + +###" +" ""ball-sorting-with-blocks-barrier"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""ball-sorting-with-blocks-barrier"" in a python code block. +### + +"," + ball-sorting-with-blocks-barrier: {'task-name': 'ball-sorting-with-blocks-barrier', 'task-description': 'On a tabletop, there are four balls and four zones of different colors (red, blue, green, and yellow). Each ball is located behind a line of small blocks of the same color. The task is to pick up each ball and place it into the zone of the same color, but without knocking over the blocks. The challenge lies in the precise navigation over the block barriers and color matching.', 'assets-used': ['ball/ball-template.urdf', 'zone/zone.urdf', 'block/small.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class BallSortingWithBlocksBarrier(Task): + """"""Pick up each ball and place it into the zone of the same color, but without knocking over the blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} ball in the {color} zone without knocking over the blocks"" + self.task_completed_desc = ""done sorting balls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for the balls and zones + colors = ['red', 'blue', 'green', 'yellow'] + + # Add zones and blocks. + zone_size = (0.12, 0.12, 0) + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/small.urdf' + zone_urdf = 'zone/zone.urdf' + zones = [] + blocks = [] + for color in colors: + # Add zone of specific color + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zones.append(zone_pose) + + # Add line of blocks of the same color + for _ in range(5): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for color in colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[color]) + balls.append(ball_id) + + # Goal: each ball is in a zone of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[zones[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""color-coordinated-block-bridge"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-block-bridge"" in a python code block. +### + +"," + color-coordinated-block-bridge: {'task-name': 'color-coordinated-block-bridge', 'task-description': 'Construct a bridge by interleaving three differently colored blocks (red, blue, and green) on a pallet in a specific sequence - red block at the edges, blue block in the middle, and a green block on top of the red and blue blocks. Repeat this sequence until a bridge is formed across the length of the pallet.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedBlockBridge(Task): + """"""Construct a bridge by interleaving three differently colored blocks (red, blue, and green) on a pallet in a specific sequence - red block at the edges, blue block in the middle, and a green block on top of the red and blue blocks. Repeat this sequence until a bridge is formed across the length of the pallet."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""construct a bridge by interleaving three differently colored blocks (red, blue, and green) on a pallet in a specific sequence"" + self.task_completed_desc = ""done constructing the bridge."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.30, 0.15, 0.02) + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object('pallet/pallet.urdf', pallet_pose, 'fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green']] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + + objs = [] + for i in range(9): # 3 sets of 3 colored blocks + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i % 3]) + objs.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0, 0.02), (0, 0.05, 0.02), # bottom layer + (0, -0.05, 0.06), (0, 0, 0.06), (0, 0.05, 0.06), # middle layer + (0, -0.05, 0.10), (0, 0, 0.10), (0, 0.05, 0.10)] # top layer + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a bridge (bottom layer: red, blue, red). + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (middle layer: green, green, green). + self.add_goal(objs=objs[3:6], matches=np.ones((3, 3)), targ_poses=targs[3:6], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) + + # Goal: blocks are stacked in a bridge (top layer: red, blue, red). + self.add_goal(objs=objs[6:], matches=np.ones((3, 3)), targ_poses=targs[6:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) + +``` + +###" +" ""color-coordinated-cylinder-pyramid"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-cylinder-pyramid"" in a python code block. +### + +"," + color-coordinated-cylinder-pyramid: {'task-name': 'color-coordinated-cylinder-pyramid', 'task-description': 'Construct a pyramid on a pallet using four cylinders of different colors (red, blue, green, and yellow). The first level should consist of a red cylinder and a blue cylinder side by side. The second level should consist of a green cylinder placed on top of the red and blue cylinders. The third and final level should consist of a yellow cylinder placed on top of the green cylinder. The challenge lies in the precise placement of cylinders, maintaining the balance of the structure, and correct color arrangement.', 'assets-used': ['cylinder/cylinder-template.urdf', 'pallet/pallet.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedCylinderPyramid(Task): + """"""Construct a pyramid on a pallet using four cylinders of different colors (red, blue, green, and yellow)."""""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = ""make the {row} row with {cylinder}"" + self.task_completed_desc = ""done stacking cylinder pyramid."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.15, 0.15, 0.005) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + + # Cylinder colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'] + ] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=colors[i]) + cylinders.append(cylinder_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0.05, 0.03), + (0, 0, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: cylinders are stacked in a pyramid (bottom row: red, blue). + self.add_goal(objs=cylinders[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2, + language_goal=self.lang_template.format(cylinder=""the red and blue cylinders"", row=""bottom"")) + + # Goal: cylinders are stacked in a pyramid (middle row: green). + self.add_goal(objs=cylinders[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*1, + language_goal=self.lang_template.format(cylinder=""the green cylinder"", row=""middle"")) + + # Goal: cylinders are stacked in a pyramid (top row: yellow). + self.add_goal(objs=cylinders[3:], matches=np.ones((1, 1)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*1, + language_goal=self.lang_template.format(cylinder=""the yellow cylinder"", row=""top"")) +``` + +###" +" ""sweep-and-sort-blocks"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""sweep-and-sort-blocks"" in a python code block. +### + +"," + sweep-and-sort-blocks: {'task-name': 'sweep-and-sort-blocks', 'task-description': 'Sweep a pile of small blocks of different colors (red, blue, green, and yellow) into their corresponding colored zones marked on the tabletop. The challenge lies in the sweeping action, precise placement, and color coordination.', 'assets-used': ['zone/zone.urdf', 'block/small.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class SweepAndSortBlocks(Task): + """"""Sweep a pile of small blocks of different colors (red, blue, green, and yellow) into their corresponding colored zones marked on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""sweep the pile of {color} blocks into the {color} square"" + self.task_completed_desc = ""done sweeping and sorting."" + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add colored zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + colors = ['red', 'blue', 'green', 'yellow'] + zone_poses = [] + for color in colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add piles of colored blocks. + block_urdf = 'block/small.urdf' + block_size = (0.04, 0.04, 0.04) + piles = [] + for color in colors: + pile = [] + for _ in range(10): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + pile.append(block_id) + piles.append(pile) + + # Add goals for each color. + for i, color in enumerate(colors): + self.add_goal(objs=piles[i], matches=np.ones((10, 1)), targ_poses=[zone_poses[i]], replace=True, + rotations=False, metric='zone', params=[(zone_poses[i], zone_size)], step_max_reward=1, + language_goal=self.lang_template.format(color=color)) +``` + +###" +" ""align-cylinders-in-zones"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""align-cylinders-in-zones"" in a python code block. +### + +"," + align-cylinders-in-zones: {'task-name': 'align-cylinders-in-zones', 'task-description': 'Place four differently colored cylinders (red, blue, green, yellow) each into a matching colored zone. But, the zones are surrounded by small blocks, which the robot needs to move out of the way without knocking them out of their respective zones. The challenge includes precise placement of cylinders, color matching, and careful navigation around the small blocks.', 'assets-used': ['cylinder/cylinder-template.urdf', 'zone/zone.urdf', 'block/small.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class AlignCylindersInZones(Task): + """"""Place four differently colored cylinders each into a matching colored zone. + The zones are surrounded by small blocks, which the robot needs to move out of the way + without knocking them out of their respective zones."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} cylinder in the {color} zone"" + self.task_completed_desc = ""done aligning cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Cylinder colors. + colors = ['red', 'blue', 'green', 'yellow'] + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=utils.COLORS[colors[i]]) + cylinders.append(cylinder_id) + + # Add zones. + # x, y, z dimensions for the asset size + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + + zones = [] + for i in range(4): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, color=utils.COLORS[colors[i]], category='fixed') + zones.append(zone_pose) + + # Add small blocks around the zones. + # x, y, z dimensions for the asset size + block_size = (0.02, 0.02, 0.02) + block_urdf = 'block/small.urdf' + + for _ in range(16): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose) + + # Goal: each cylinder is in a matching colored zone. + for i in range(4): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[zones[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""sphere-container-color-match"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""sphere-container-color-match"" in a python code block. +### + +"," + sphere-container-color-match: {'task-name': 'sphere-container-color-match', 'task-description': 'On a tabletop, there are four spheres of different colors (red, blue, green, and yellow) inside four containers of a different color (red, blue, green, and yellow). The task is to pick up each sphere and place it into a container of the same color. The task is challenging due to the manipulation of spherical objects and the color coordination required.', 'assets-used': ['sphere/sphere.urdf', 'container/container-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class SphereContainerColorMatch(Task): + """"""Pick up each sphere and place it into a container of the same color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 4 + self.lang_template = ""put the {color} sphere in the {color} container"" + self.task_completed_desc = ""done matching spheres and containers."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and corresponding names + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + color_names = ['red', 'blue', 'green', 'yellow'] + + # Add containers. + container_size = (0.12, 0.12, 0.12) + container_urdf = 'container/container-template.urdf' + containers = [] + for i in range(4): + container_pose = self.get_random_pose(env, container_size) + container_id = env.add_object(container_urdf, container_pose, color=colors[i]) + containers.append(container_id) + + # Add spheres. + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + spheres = [] + for i in range(4): + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=colors[i]) + spheres.append(sphere_id) + + # Goal: each sphere is in a container of the same color. + for i in range(4): + self.add_goal(objs=[spheres[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(containers[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=color_names[i])) +``` + +###" +" ""insert-ell-along-square-path"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""insert-ell-along-square-path"" in a python code block. +### + +"," + insert-ell-along-square-path: {'task-name': 'insert-ell-along-square-path', 'task-description': 'On the tabletop, there is a square path marked by small blocks. Along the path, there are four colored ell-shaped blocks (red, blue, green, and yellow) and four fixtures of matching colors. The task is to pick up each ell block and insert it into the fixture of the same color. However, the robot must move each ell block along the marked square path to reach the fixture. The task is challenging because it requires precise navigation along the path, color coordination, and insertion accuracy.', 'assets-used': ['block/small.urdf', 'insertion/ell.urdf', 'insertion/fixture.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class InsertEllAlongSquarePath(Task): + """"""Pick up each ell block and insert it into the fixture of the same color. However, the robot must move each ell block along the marked square path to reach the fixture."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""move the {color} ell block into the {color} fixture"" + self.task_completed_desc = ""done inserting ell blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Ell block colors. + colors = ['red', 'blue', 'green', 'yellow'] + + # Add ell blocks and fixtures. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + fixture_urdf = 'insertion/fixture.urdf' + ell_blocks = [] + fixtures = [] + for color in colors: + # Add ell block + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[color]) + ell_blocks.append(ell_id) + + # Add fixture + fixture_pose = self.get_random_pose(env, ell_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color]) + fixtures.append(fixture_id) + + # Goal: each ell block is inserted into the fixture of the same color. + for i in range(len(colors)): + self.add_goal(objs=[ell_blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(colors), + language_goal=self.lang_template.format(color=colors[i])) + + # Add square path marked by small blocks. + path_block_size = (0.02, 0.02, 0.02) + path_block_urdf = 'block/small.urdf' + path_block_color = utils.COLORS['gray'] + for _ in range(16): + path_block_pose = self.get_random_pose(env, path_block_size) + env.add_object(path_block_urdf, path_block_pose, color=path_block_color) +``` + +###" +" ""color-coordinated-box-ball-matching"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-box-ball-matching"" in a python code block. +### + +"," + color-coordinated-box-ball-matching: {'task-name': 'color-coordinated-box-ball-matching', 'task-description': 'On the tabletop, there are four boxes of different colors (red, blue, green, and yellow) and four balls of corresponding colors. The task is to pick up each ball and place it inside the box of the same color, however, the boxes are placed in a straight line with a row of small blocks acting as a barrier between the boxes and the balls. The challenge lies in the precise placement, color matching, and the navigation around the barrier without knocking over any small blocks.', 'assets-used': ['box/box-template.urdf', 'ball/ball-template.urdf', 'block/small.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedBoxBallMatching(Task): + """"""Pick up each ball and place it inside the box of the same color, navigate around the barrier without knocking over any small blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""put the {color} ball in the {color} box"" + self.task_completed_desc = ""done placing balls in boxes."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for the boxes and balls + colors = ['red', 'blue', 'green', 'yellow'] + + # Add boxes. + box_size = (0.05, 0.05, 0.05) + box_urdf = 'box/box-template.urdf' + box_poses = [] + for color in colors: + box_pose = self.get_random_pose(env, box_size) + env.add_object(box_urdf, box_pose, color=color, category='fixed') + box_poses.append(box_pose) + + # Add balls. + balls = [] + ball_size = (0.02, 0.02, 0.02) + ball_urdf = 'ball/ball-template.urdf' + for color in colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add small blocks as barriers. + barrier_size = (0.01, 0.01, 0.01) + barrier_urdf = 'block/small.urdf' + for _ in range(10): + barrier_pose = self.get_random_pose(env, barrier_size) + env.add_object(barrier_urdf, barrier_pose, category='fixed') + + # Goal: each ball is in the box of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[box_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""cylinder-balancing-and-placement"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""cylinder-balancing-and-placement"" in a python code block. +### + +"," + cylinder-balancing-and-placement: {'task-name': 'cylinder-balancing-and-placement', 'task-description': 'On a table, there are three differently colored cylinders (red, green, and blue) and three square zones of matching colors. The task involves picking up each cylinder and balancing it on its end at the center of the corresponding colored zone, in the sequence of red, green, and blue from left to right. The task is challenging due to precise balancing required in placing the cylinders, color matching, and sequence following.', 'assets-used': ['cylinder/cylinder-template.urdf', 'zone/zone.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CylinderBalancingAndPlacement(Task): + """"""Pick up each cylinder and balance it on its end at the center of the corresponding colored zone."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""balance the {color} cylinder in the {color} zone"" + self.task_completed_desc = ""done balancing and placing cylinders."" + self.colors = ['red', 'green', 'blue'] + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for color in self.colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.12) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for color in self.colors: + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=utils.COLORS[color]) + cylinders.append(cylinder_id) + + # Goal: each cylinder is balanced in the corresponding colored zone. + for i in range(len(cylinders)): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/3, + language_goal=self.lang_template.format(color=self.colors[i])) +``` + +###" +" ""color-coordinated-sphere-and-cylinder-assembly"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-sphere-and-cylinder-assembly"" in a python code block. +### + +"," + color-coordinated-sphere-and-cylinder-assembly: {'task-name': 'color-coordinated-sphere-and-cylinder-assembly', 'task-description': 'The robot starts with four spheres of different colors (red, blue, green, yellow) and four cylinders of matching colors. The task is to pick up each sphere and place it on top of the cylinder of the same color, forming four sphere-and-cylinder pairs. However, the challenge here is to do this in a specific color sequence - red, blue, green, and finally yellow.', 'assets-used': ['sphere/sphere-template.urdf', 'cylinder/cylinder-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedSphereAndCylinderAssembly(Task): + """"""Pick up each sphere and place it on top of the cylinder of the same color, in a specific color sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} sphere on the {color} cylinder"" + self.task_completed_desc = ""done placing spheres on cylinders."" + self.colors = ['red', 'blue', 'green', 'yellow'] + self.color_sequence = ['red', 'blue', 'green', 'yellow'] + + def reset(self, env): + super().reset(env) + + # Add spheres and cylinders. + sphere_size = (0.05, 0.05, 0.05) + cylinder_size = (0.05, 0.05, 0.1) + sphere_template = 'sphere/sphere-template.urdf' + cylinder_template = 'cylinder/cylinder-template.urdf' + + # Add spheres and cylinders of each color. + for color in self.colors: + sphere_pose = self.get_random_pose(env, sphere_size) + cylinder_pose = self.get_random_pose(env, cylinder_size) + sphere_id = env.add_object(sphere_template, sphere_pose, color=color) + cylinder_id = env.add_object(cylinder_template, cylinder_pose, color=color) + + # Goal: each sphere is on top of the cylinder of the same color. + self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[cylinder_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=color)) + + # The task is completed in a specific color sequence. + self.color_sequence = ['red', 'blue', 'green', 'yellow'] +``` + +###" +" ""sequential-block-insertion"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""sequential-block-insertion"" in a python code block. +### + +"," + sequential-block-insertion: {'task-name': 'sequential-block-insertion', 'task-description': 'There are four blocks of different colors (red, blue, green, yellow) and four fixtures of matching colors. The task involves picking up each block and inserting it into the fixture of the same color, in the specific sequence of red, blue, green, and yellow. However, the challenge lies in the fact that the blocks and fixtures are initially arranged in a mixed order, demanding careful navigation, precise insertion, color matching, and sequence following.', 'assets-used': ['insertion/fixture.urdf', 'block/block.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class SequentialBlockInsertion(Task): + """"""Pick up blocks of different colors and insert them into the fixture of the same color in a specific sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert the {color} block into the {color} fixture"" + self.task_completed_desc = ""done inserting blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define the sequence of colors + colors = ['red', 'blue', 'green', 'yellow'] + + # Add fixtures. + # x, y, z dimensions for the asset size + fixture_size = (0.12, 0.12, 0) + fixture_urdf = 'insertion/fixture.urdf' + fixtures = [] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color], category='fixed') + fixtures.append(fixture_id) + + # Add blocks. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for color in colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Goal: each block is in the fixture of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""sequential-insertion-and-stacking"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""sequential-insertion-and-stacking"" in a python code block. +### + +"," + sequential-insertion-and-stacking: {'task-name': 'sequential-insertion-and-stacking', 'task-description': 'The tabletop contains three fixtures and three ell-shaped blocks of different colors - red, blue, and green. The task is to first pick up and insert each ell block into the corresponding colored fixture in the sequence of red, blue, and green. After successful insertion, the robot must pick up the three blocks again from the fixtures and stack them in a corner of the tabletop in the same color sequence - red at the bottom, blue in the middle, and green on top.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf', 'corner/corner-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class SequentialInsertionAndStacking(Task): + """"""Pick up and insert each ell block into the corresponding colored fixture in the sequence of red, blue, and green. After successful insertion, pick up the three blocks again from the fixtures and stack them in a corner of the tabletop in the same color sequence - red at the bottom, blue in the middle, and green on top."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""insert the {color} ell block into the {color} fixture and then stack them in the corner"" + self.task_completed_desc = ""done inserting and stacking."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add fixtures. + fixture_size = (0.12, 0.12, 0) + fixture_urdf = 'insertion/fixture.urdf' + fixture_poses = [] + colors = ['red', 'blue', 'green'] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + env.add_object(fixture_urdf, fixture_pose, category='fixed', color=utils.COLORS[color]) + fixture_poses.append(fixture_pose) + + # Add ell blocks. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + ells = [] + for color in colors: + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[color]) + ells.append(ell_id) + + # Goal: each ell block is in the corresponding colored fixture. + for i in range(3): + self.add_goal(objs=[ells[i]], matches=np.ones((1, 1)), targ_poses=[fixture_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/3) + self.lang_goals.append(self.lang_template.format(color=colors[i])) + + # Add corner. + corner_size = (0.12, 0.12, 0) + corner_pose = self.get_random_pose(env, corner_size) + corner_urdf = 'corner/corner-template.urdf' + env.add_object(corner_urdf, corner_pose, category='fixed') + + # Goal: ell blocks are stacked in the corner in the color sequence - red at the bottom, blue in the middle, and green on top. + stack_poses = [(0, 0, 0.04), (0, 0, 0.08), (0, 0, 0.12)] + targs = [(utils.apply(corner_pose, i), corner_pose[1]) for i in stack_poses] + self.add_goal(objs=ells, matches=np.ones((3, 3)), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/3, + language_goal=""stack the ell blocks in the corner in the color sequence - red at the bottom, blue in the middle, and green on top"") +``` + +###" +" ""color-coordinated-block-shifting"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-block-shifting"" in a python code block. +### + +"," + color-coordinated-block-shifting: {'task-name': 'color-coordinated-block-shifting', 'task-description': 'On a tabletop, there are three zones marked in three different colors (red, blue, and green) and nine blocks of matching colors (three red, three blue, and three green). Each zone initially contains three blocks of a single color, but the colors of the blocks and zones do not match. The task involves picking up each block and precisely placing it in the zone of the same color. However, there are a few small blocks randomly scattered in the path between the zones. The robot has to strategically navigate around these blocks without knocking them over while transporting the blocks to the corresponding zones. The challenge lies in the precise navigation, placement of the blocks, color matching while avoiding the blocks.', 'assets-used': ['zone/zone.urdf', 'block/block.urdf', 'block/small.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedBlockShifting(Task): + """"""Pick up each block and precisely place it in the zone of the same color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""move the {color} blocks to the {color} zone"" + self.task_completed_desc = ""done moving blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_colors = ['yellow', 'blue', 'green'] + zone_poses = [] + for color in zone_colors: + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color]) + zone_poses.append(zone_pose) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + blocks = [] + for color in zone_colors: + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add small blocks as obstacles. + small_block_size = (0.02, 0.02, 0.02) + small_block_urdf = 'stacking/block.urdf' + for _ in range(5): + small_block_pose = self.get_random_pose(env, small_block_size) + env.add_object(small_block_urdf, small_block_pose) + + # Goal: each block is in the zone of the same color. + for i in range(9): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i//3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/9, + language_goal=self.lang_template.format(color=zone_colors[i//3])) +``` + +###" +" ""guided-block-path"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""guided-block-path"" in a python code block. +### + +"," + guided-block-path: {'task-name': 'guided-block-path', 'task-description': 'On the tabletop, there are four colored blocks (red, blue, green, and yellow) and four lines of the corresponding colors. The task is to pick up each block and move it along the line of the same color from start to end. The challenge lies in precise navigation along the line, color coordination, and block manipulation.', 'assets-used': ['block/block.urdf', 'line/line-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class GuidedBlockPath(Task): + """"""Pick up each block and move it along the line of the same color from start to end."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""move the {color} block along the {color} line from start to end"" + self.task_completed_desc = ""done moving blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and corresponding names + colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow']] + color_names = ['red', 'blue', 'green', 'yellow'] + + # Add lines and blocks. + # x, y, z dimensions for the asset size + line_size = (0.3, 0.01, 0.01) + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + line_urdf = 'line/line-template.urdf' + + blocks = [] + lines = [] + for i in range(4): + # Add line + line_pose = self.get_random_pose(env, line_size) + env.add_object(line_urdf, line_pose, color=colors[i], category='fixed') + lines.append(line_pose) + + # Add block + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Add goal + self.add_goal(objs=[block_id], matches=np.ones((1, 1)), targ_poses=[line_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=color_names[i])) +``` + +###" +" ""mixed-color-block-barrier-insertion"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""mixed-color-block-barrier-insertion"" in a python code block. +### + +"," + mixed-color-block-barrier-insertion: {'task-name': 'mixed-color-block-barrier-insertion', 'task-description': 'There are four different colored blocks (red, blue, green, and yellow), and four fixtures in corresponding colors. Two barriers, each made of three blocks (orange, purple, and brown), are placed in between the blocks and fixtures, forming a path that the robot must navigate. The task involves picking up each colored block, navigating the barriers, and inserting each block into the fixture of the same color. The fixtures are arranged in a sequence from left to right: red, blue, green, and yellow, providing a challenge in precise navigation, color coordination, and insertion.', 'assets-used': ['block/block.urdf', 'insertion/fixture.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class MixedColorBlockBarrierInsertion(Task): + """"""Pick up each colored block, navigate the barriers, and insert each block into the fixture of the same color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert the {color} block into the {color} fixture"" + self.task_completed_desc = ""done inserting blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for blocks and fixtures + colors = ['red', 'blue', 'green', 'yellow'] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for color in colors: + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add fixtures. + fixture_size = (0.06, 0.06, 0.06) + fixture_urdf = 'insertion/fixture.urdf' + fixtures = [] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color]) + fixtures.append(fixture_id) + + # Add barriers. + barrier_size = (0.12, 0.04, 0.04) + barrier_colors = ['orange', 'purple', 'brown'] + for _ in range(2): + for color in barrier_colors: + barrier_pose = self.get_random_pose(env, barrier_size) + env.add_object(block_urdf, barrier_pose, color=utils.COLORS[color]) + + # Goal: each block is inserted into the fixture of the same color. + for i in range(len(blocks)): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""ball-in-bowl-obstacle-course"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""ball-in-bowl-obstacle-course"" in a python code block. +### + +"," + ball-in-bowl-obstacle-course: {'task-name': 'ball-in-bowl-obstacle-course', 'task-description': 'With the tabletop setup consisting of a maze of small blocks, the task requires the robot to pick up four balls of different colors (red, blue, green, yellow) and place each of them into the corresponding colored bowls strategically positioned at different corners of the maze, without knocking over any blocks, demanding careful navigation and color coordination.', 'assets-used': ['ball/ball-template.urdf', 'block/small.urdf', 'bowl/bowl.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class BallInBowlObstacleCourse(Task): + """"""Navigate through a maze of blocks, pick up balls of different colors and place them in the corresponding colored bowls."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""navigate through the maze and place the {color} ball in the {color} bowl"" + self.task_completed_desc = ""done placing balls in bowls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks to form a maze. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/small.urdf' + for _ in range(10): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose, category='fixed') + + # Add balls of different colors. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + colors = ['red', 'blue', 'green', 'yellow'] + balls = [] + for color in colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add bowls of different colors at different corners of the maze. + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowls = [] + for color in colors: + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_id = env.add_object(bowl_urdf, bowl_pose, color=color, category='fixed') + bowls.append(bowl_id) + + # Goal: each ball is in the bowl of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(bowls[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""ball-in-bowl-obstacle-course-new"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""ball-in-bowl-obstacle-course-new"" in a python code block. +### + +"," + ball-in-bowl-obstacle-course-new: {'task-name': 'ball-in-bowl-obstacle-course-new', 'task-description': 'With the tabletop setup consisting of a maze of small blocks, the task requires the robot to pick up four balls of different colors (red, blue, green, yellow) and place each of them into the corresponding colored bowls strategically positioned at different corners of the maze, without knocking over any blocks, demanding careful navigation and color coordination.', 'assets-used': ['ball/ball-template.urdf', 'block/small.urdf', 'bowl/bowl.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class BallInBowlObstacleCourseNew(Task): + """"""Navigate through a maze of blocks, pick up balls of different colors and place them in the corresponding colored bowls."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""put the {color} ball in the {color} bowl"" + self.task_completed_desc = ""done placing balls in bowls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks to form a maze. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/small.urdf' + for _ in range(10): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose, category='fixed') + + # Add balls of different colors. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + colors = ['red', 'blue', 'green', 'yellow'] + balls = [] + for color in colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add bowls of different colors at different corners of the maze. + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowls = [] + for color in colors: + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_id = env.add_object(bowl_urdf, bowl_pose, color=color, category='fixed') + bowls.append(bowl_id) + + # Goal: each ball is in the bowl of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(bowls[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""color-coordinated-arch-construction"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-arch-construction"" in a python code block. +### + +"," + color-coordinated-arch-construction: {'task-name': 'color-coordinated-arch-construction', 'task-description': 'The task is to construct an arch using six blocks: three red and three blue. The blocks are initially placed in a container. The robot needs to pick each block and place it on a pallet in the following arrangement: place two red blocks in parallel on the pallet, then place a blue block on top of the red blocks to form an arch. Repeat the process with the remaining blocks, placing them on top of the first arch to form a second layer. The task is challenging due to the need for precise placement of the blocks and maintaining the balance of the structure.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'container/container-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedArchConstruction(Task): + """"""Construct an arch using six blocks: three red and three blue."""""" + + def __init__(self): + super().__init__() + self.max_steps = 6 + self.lang_template = ""construct an arch using six blocks: three red and three blue"" + self.task_completed_desc = ""done constructing arch."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.15, 0.15, 0.005) + pallet_urdf = 'pallet/pallet.urdf' + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + + # Block colors. + colors = [utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['blue'], + utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['blue']] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.02), (0, 0.05, 0.02), + (0, 0, 0.06), (0, -0.05, 0.08), + (0, 0.05, 0.08), (0, 0, 0.12)] + targs = [(utils.apply(pallet_pose, i), pallet_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in an arch (bottom row: red, red, blue). + self.add_goal(objs=blocks[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) + + # Goal: blocks are stacked in an arch (top row: red, red, blue). + self.add_goal(objs=blocks[3:], matches=np.ones((3, 3)), targ_poses=targs[3:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template) +``` + +###" +" ""color-coordinated-zone-arrangement"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-zone-arrangement"" in a python code block. +### + +"," + color-coordinated-zone-arrangement: {'task-name': 'color-coordinated-zone-arrangement', 'task-description': 'On the tabletop, there are nine blocks of three different colors (three red, three blue, and three green) and three pallets of matching colors (one red, one blue, one green). The task is to pick up each block and place it on the pallet of the same color, arranging the blocks on each pallet in a line. However, there are a few small blocks randomly scattered on the tabletop, which the robot has to navigate around without knocking them over while transporting the blocks to the corresponding pallets. The challenge lies in the precise navigation, placement of the blocks, color matching, and maintaining the balance on the pallets.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'block/small.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedZoneArrangement(Task): + """"""Pick up blocks of different colors and place them on the pallets of the same color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""place the {color} blocks on the {color} pallet"" + self.task_completed_desc = ""done arranging blocks on pallets."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallets. + # x, y, z dimensions for the asset size + pallet_size = (0.12, 0.12, 0.02) + pallet_urdf = 'pallet/pallet.urdf' + pallet_colors = ['red', 'blue', 'green'] + pallet_poses = [] + for color in pallet_colors: + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed', color=utils.COLORS[color]) + pallet_poses.append(pallet_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + for color in pallet_colors: + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + blocks.append(block_id) + + # Add small blocks as obstacles. + small_block_size = (0.02, 0.02, 0.02) + small_block_urdf = 'block/small.urdf' + for _ in range(5): + small_block_pose = self.get_random_pose(env, small_block_size) + env.add_object(small_block_urdf, small_block_pose) + + # Goal: each block is on the pallet of the same color. + for i in range(9): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[pallet_poses[i // 3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 9, + language_goal=self.lang_template.format(color=pallet_colors[i // 3])) +``` + +###" +" ""color-coordinated-cylinder-stand-assembly"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-cylinder-stand-assembly"" in a python code block. +### + +"," + color-coordinated-cylinder-stand-assembly: {'task-name': 'color-coordinated-cylinder-stand-assembly', 'task-description': 'The robot starts with four cylinders of different colors (red, blue, green, yellow) and four stands of matching colors. The task is to pick up each cylinder and place it on top of the stand of the same color, forming four cylinder-and-stand pairs. However, the challenge here is to do this in a specific color sequence - green, yellow, blue, and finally red, while also ensuring that the cylinders do not topple over.', 'assets-used': ['cylinder/cylinder-template.urdf', 'stacking/stand.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedCylinderStandAssembly(Task): + """"""Pick up each cylinder and place it on top of the stand of the same color, in a specific color sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""place the {color} cylinder on the {color} stand"" + self.task_completed_desc = ""done placing cylinders on stands."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their sequence + colors = ['green', 'yellow', 'blue', 'red'] + color_sequence = [utils.COLORS[color] for color in colors] + + # Add stands. + stand_size = (0.04, 0.04, 0.04) + stand_urdf = 'stacking/stand.urdf' + stand_poses = [] + for i in range(4): + stand_pose = self.get_random_pose(env, stand_size) + env.add_object(stand_urdf, stand_pose, color=color_sequence[i], category='fixed') + stand_poses.append(stand_pose) + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=color_sequence[i]) + cylinders.append(cylinder_id) + + # Goal: each cylinder is on the stand of the same color, in the specified color sequence. + for i in range(4): + self.add_goal(objs=[cylinders[i]], matches=np.ones((1, 1)), targ_poses=[stand_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""color-coordinated-ball-stacking"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-ball-stacking"" in a python code block. +### + +"," + color-coordinated-ball-stacking: {'task-name': 'color-coordinated-ball-stacking', 'task-description': 'There are four balls of different colors (red, blue, green, yellow), and four containers of matching colors on the table. The task is to pick up each ball and stack it on top of the corresponding colored container. However, the stacking should be done in a specific color sequence - blue at the bottom, followed by yellow, then green, and finally red at the top. This task enforces challenging skills due to the precision required for stacking the balls, color coordination, and sequencing.', 'assets-used': ['ball/ball-template.urdf', 'container/container-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedBallStacking(Task): + """"""Stack balls on top of the corresponding colored containers in a specific color sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""stack the balls on top of the corresponding colored containers in the sequence blue, yellow, green, red"" + self.task_completed_desc = ""done stacking balls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define the color sequence + color_sequence = ['blue', 'yellow', 'green', 'red'] + + # Add containers. + container_size = (0.12, 0.12, 0.12) + container_urdf = 'container/container-template.urdf' + container_poses = [] + containers = [] + for color in color_sequence: + container_pose = self.get_random_pose(env, container_size) + container_id = env.add_object(container_urdf, container_pose, color=utils.COLORS[color]) + container_poses.append(container_pose) + containers.append(container_id) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for color in color_sequence: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=utils.COLORS[color]) + balls.append(ball_id) + + # Goal: each ball is stacked on top of the corresponding colored container in the color sequence. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[container_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(obj=color_sequence[i])) + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_ball = np.random.rand() > 0.5 + urdf = ball_urdf if is_ball else container_urdf + size = ball_size if is_ball else container_size + pose = self.get_random_pose(env, obj_size=size) + color = np.random.choice(list(utils.COLORS.keys())) + + obj_id = env.add_object(urdf, pose, color=utils.COLORS[color]) + n_distractors += 1 +``` + +###" +" ""color-coded-blocks-on-corner"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coded-blocks-on-corner"" in a python code block. +### + +"," + color-coded-blocks-on-corner: {'task-name': 'color-coded-blocks-on-corner', 'task-description': 'On a tabletop, there are four blocks of different colors (red, blue, green, and yellow) and a corner structure. The task involves picking up each block and placing it in the corner structure in a specific color sequence: from left to right, place red, blue, green, and finally yellow. The blocks must be arranged such that they form a straight line along the corner. The challenge lies in the precise placement, color coordination, and maintaining the balance of the blocks along the corner.', 'assets-used': ['block/block.urdf', 'corner/corner-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCodedBlocksOnCorner(Task): + """"""Pick up blocks of different colors and place them in a corner structure in a specific color sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""place the blocks in the corner in the sequence red, blue, green, yellow"" + self.task_completed_desc = ""done placing blocks in the corner."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add corner structure. + corner_size = (0.15, 0.15, 0.05) + corner_pose = self.get_random_pose(env, corner_size) + corner_urdf = 'corner/corner-template.urdf' + env.add_object(corner_urdf, corner_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, 0, 0.08)] + targs = [(utils.apply(corner_pose, i), corner_pose[1]) for i in place_pos] + + # Goal: blocks are placed in the corner in the sequence red, blue, green, yellow. + for i in range(4): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 4, + language_goal=self.lang_template.format(blocks=""the red, blue, green, yellow blocks"")) +``` + +###" +" ""insertion-in-color-sequenced-zones"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""insertion-in-color-sequenced-zones"" in a python code block. +### + +"," + insertion-in-color-sequenced-zones: {'task-name': 'insertion-in-color-sequenced-zones', 'task-description': 'On the table, there are four differently-colored insertion ell objects (red, blue, green, yellow) and four zones on the tabletop marked in the same colors. Initially, each ell is placed in a zone but not corresponding to the color of the zone. The task is to pick up each ell and place it in the zone of the same color, in the specific sequence of red, blue, green, and yellow from left to right, requiring careful navigation, precise placement, color matching, and sequence following.', 'assets-used': ['insertion/ell.urdf', 'zone/zone.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertionInColorSequencedZones(Task): + """"""Pick up each ell and place it in the zone of the same color, in the specific sequence of red, blue, green, and yellow from left to right."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} ell in the {color} zone"" + self.task_completed_desc = ""done placing ells in color sequenced zones."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Ell colors. + colors = ['red', 'blue', 'green', 'yellow'] + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for i in range(4): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[colors[i]]) + zone_poses.append(zone_pose) + + # Add ells. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + ells = [] + for i in range(4): + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[colors[i]]) + ells.append(ell_id) + + # Goal: each ell is in the zone of the same color. + for i in range(4): + self.add_goal(objs=[ells[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""color-coordinated-zone-stacking"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-zone-stacking"" in a python code block. +### + +"," + color-coordinated-zone-stacking: {'task-name': 'color-coordinated-zone-stacking', 'task-description': 'On the tabletop, there are three zones and nine blocks of three different colors (red, blue, green). Each color has three blocks and the blocks are scattered randomly on the table. The task is to pick up the blocks and stack them in the zones to form a pyramid shape. Each pyramid should contain blocks of the same color with two blocks on the base and one block on top. The zones with the pyramids should be arranged in a straight line in the following color order: red, blue, green from left to right. The challenge lies in the color coordination, precise stacking and the arrangement of the zones.', 'assets-used': ['block/block.urdf', 'zone/zone.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCoordinatedZoneStacking(Task): + """"""Pick up blocks of different colors and stack them in zones to form a pyramid."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""stack the blocks in the zones to form a pyramid"" + self.task_completed_desc = ""done stacking blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + for _ in range(3): + zone_pose = self.get_random_pose(env, zone_size) + env.add_object(zone_urdf, zone_pose, 'fixed') + zone_poses.append(zone_pose) + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(9): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i//3]) + blocks.append(block_id) + + # Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(zone_poses[i//3], place_pos[i%3]), zone_poses[i//3][1]) for i in range(9)] + + # Goal: blocks are stacked in a pyramid in each zone. + for i in range(3): + self.add_goal(objs=blocks[i*3:(i+1)*3], matches=np.ones((3, 3)), targ_poses=targs[i*3:(i+1)*3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(blocks=""the red, blue and green blocks"", + row=""bottom"")) +``` + +###" +" ""color-coordinated-cylinder-ball-match"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-cylinder-ball-match"" in a python code block. +### + +"," + color-coordinated-cylinder-ball-match: {'task-name': 'color-coordinated-cylinder-ball-match', 'task-description': 'On the tabletop, there are four cylinders of different colors (red, blue, green, and yellow) and four balls of corresponding colors. The task is to pick up each ball and place it on top of the cylinder of the same color without the ball rolling off. However, there are small blocks scattered randomly on the table that the robot has to navigate around without knocking them over. The challenge lies in the precise placement of the balls on top of the cylinders, color matching, and navigation around the blocks.', 'assets-used': ['cylinder/cylinder-template.urdf', 'ball/ball-template.urdf', 'block/small.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedCylinderBallMatch(Task): + """"""Pick up each ball and place it on top of the cylinder of the same color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} ball on the {color} cylinder"" + self.task_completed_desc = ""done placing balls on cylinders."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add cylinders. + # x, y, z dimensions for the asset size + cylinder_size = (0.04, 0.04, 0.1) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinder_colors = ['red', 'blue', 'green', 'yellow'] + cylinder_poses = [] + cylinders = [] + for color in cylinder_colors: + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=color) + cylinder_poses.append(cylinder_pose) + cylinders.append(cylinder_id) + + # Add balls. + # x, y, z dimensions for the asset size + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + balls = [] + for color in cylinder_colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add blocks as obstacles. + # x, y, z dimensions for the asset size + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/small.urdf' + for _ in range(5): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose) + + # Goal: each ball is on top of the cylinder of the same color. + for i in range(len(balls)): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[cylinder_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(balls), + language_goal=self.lang_template.format(color=cylinder_colors[i])) +``` + +###" +" ""multi-level-insertion-and-zone-matching"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""multi-level-insertion-and-zone-matching"" in a python code block. +### + +"," + multi-level-insertion-and-zone-matching: {'task-name': 'multi-level-insertion-and-zone-matching', 'task-description': 'There are three levels of zones marked on the tabletop - the first level is red, second is blue, and third is green. On each level, there are large, medium, and small ell-shaped objects in corresponding colors. The task is to pick up each ell object from its current position and insert it into the corresponding colored zone on the same level, but in a specific order - large, medium, and small. The challenge lies in the precise control of insertion, color coordination, and the multi-level structure of the environment.', 'assets-used': ['zone/zone.urdf', 'insertion/ell.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class MultiLevelInsertionAndZoneMatching(Task): + """"""Pick up ell objects from their current position and insert them into the corresponding colored zone on the same level, in a specific order - large, medium, and small."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""insert the {size} {color} ell into the {color} zone on the same level"" + self.task_completed_desc = ""done inserting."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_sizes = [(0.12, 0.12, 0), (0.12, 0.12, 0.05), (0.12, 0.12, 0.1)] + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + zone_colors = ['red', 'blue', 'green'] + for i in range(3): + zone_pose = self.get_random_pose(env, zone_sizes[i]) + env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[zone_colors[i]]) + zone_poses.append(zone_pose) + + # Add ell objects. + ell_sizes = [(0.08, 0.08, 0.02), (0.06, 0.06, 0.015), (0.04, 0.04, 0.01)] + ell_urdf = 'insertion/ell.urdf' + ells = [] + for i in range(3): + for j in range(3): + ell_pose = self.get_random_pose(env, ell_sizes[j]) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[zone_colors[i]]) + ells.append(ell_id) + + # Goal: each ell object is in the corresponding colored zone on the same level. + for i in range(9): + self.add_goal(objs=[ells[i]], matches=np.ones((1, 1)), targ_poses=[zone_poses[i//3]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/9, + language_goal=self.lang_template.format(size=['large', 'medium', 'small'][i%3], color=zone_colors[i//3])) +``` + +###" +" ""color-cued-ball-corner-sorting"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-cued-ball-corner-sorting"" in a python code block. +### + +"," + color-cued-ball-corner-sorting: {'task-name': 'color-cued-ball-corner-sorting', 'task-description': 'On a tabletop, there are four different colored balls (red, blue, green, yellow) and four corners marked with corresponding colors using the corner template. The task involves picking up each ball and precisely placing it in the corner of the same color. However, there is a rectangular zone in the middle of the table marked by small blocks. The robot has to strategically navigate around this zone without touching the blocks while transporting the balls to the corresponding corners. The challenge lies in the precise navigation, placement of the balls, and color matching while avoiding the blocks.', 'assets-used': ['ball/ball-template.urdf', 'corner/corner-template.urdf', 'block/block_for_anchors.urdf', 'zone/zone.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorCuedBallCornerSorting(Task): + """"""Pick up each colored ball and place it in the corner of the same color while avoiding a zone marked by small blocks."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} ball in the {color} corner"" + self.task_completed_desc = ""done sorting balls."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add corners. + corner_size = (0.05, 0.05, 0.05) + corner_urdf = 'corner/corner-template.urdf' + corner_colors = ['red', 'blue', 'green', 'yellow'] + corner_poses = [] + for color in corner_colors: + corner_pose = self.get_random_pose(env, corner_size) + env.add_object(corner_urdf, corner_pose, color=color, category='fixed') + corner_poses.append(corner_pose) + + # Add balls. + balls = [] + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball-template.urdf' + for color in corner_colors: + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=color) + balls.append(ball_id) + + # Add zone. + zone_size = (0.2, 0.2, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + zone_urdf = 'zone/zone.urdf' + env.add_object(zone_urdf, zone_pose, 'fixed') + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block_for_anchors.urdf' + for _ in range(4): + block_pose = self.get_random_pose(env, block_size) + env.add_object(block_urdf, block_pose) + + # Goal: each ball is in the corner of the same color. + for i in range(4): + self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[corner_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=corner_colors[i])) +``` + +###" +" ""cylinder-ring-stack"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""cylinder-ring-stack"" in a python code block. +### + +"," + cylinder-ring-stack: {'task-name': 'cylinder-ring-stack', 'task-description': 'On the tabletop, there are four differently colored cylinders (red, blue, green, yellow) and four blocks of matching colors. The task involves picking up each block and stacking it on top of the corresponding colored cylinder. However, each cylinder and block pair should be stacked inside a differently colored container (color sequence: red cylinder and block in blue container, blue in green, green in yellow, and yellow in red). The task offers challenges in multi-object manipulation, color coordination, and precise stacking in a confined space.', 'assets-used': ['cylinder/cylinder-template.urdf', 'block/block.urdf', 'container/container-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class CylinderRingStack(Task): + """"""Pick up each block and stack it on top of the corresponding colored cylinder. + Each cylinder and block pair should be stacked inside a differently colored container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""stack the {color} block on the {color} cylinder in the {container_color} container"" + self.task_completed_desc = ""done stacking."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors for cylinders, blocks and containers + colors = ['red', 'blue', 'green', 'yellow'] + container_colors = ['blue', 'green', 'yellow', 'red'] + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.04) + cylinder_urdf = 'cylinder/cylinder-template.urdf' + cylinders = [] + for i in range(4): + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=utils.COLORS[colors[i]]) + cylinders.append(cylinder_id) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[colors[i]]) + blocks.append(block_id) + + # Add containers. + container_size = (0.12, 0.12, 0.12) + container_urdf = 'container/container-template.urdf' + containers = [] + for i in range(4): + container_pose = self.get_random_pose(env, container_size) + container_id = env.add_object(container_urdf, container_pose, color=utils.COLORS[container_colors[i]]) + containers.append(container_id) + + # Goal: each block is stacked on the corresponding colored cylinder inside a differently colored container. + for i in range(4): + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[cylinder_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/4, + language_goal=self.lang_template.format(color=colors[i], container_color=container_colors[i])) +``` + +###" +" ""color-coordinated-sphere-on-pallet-pyramid"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-coordinated-sphere-on-pallet-pyramid"" in a python code block. +### + +"," + color-coordinated-sphere-on-pallet-pyramid: {'task-name': 'color-coordinated-sphere-on-pallet-pyramid', 'task-description': 'On the tabletop, there are three pallets and nine blocks of three different colors (red, blue, green). Each color has three blocks and the blocks are scattered randomly on the table. The task is to pick up the blocks and stack them on the pallets to form a pyramid shape. Each pyramid should contain blocks of the same color with two blocks on the base and one block on top. Additionally, there are three spheres of matching colors (red, blue, green). The task is to pick up each sphere and place it on top of the corresponding colored pyramid. The pallets with the pyramids and spheres should be arranged in a straight line in the following color order: red, blue, green from left to right. The challenge lies in the color coordination, precise stacking, and the arrangement of the pallets.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'sphere/sphere.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class ColorCoordinatedSphereOnPalletPyramid(Task): + """"""Build a pyramid of colored blocks on pallets and place a matching colored sphere on top."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""build a pyramid of {color} blocks on the pallet and place the {color} sphere on top"" + self.task_completed_desc = ""done building color-coordinated pyramids."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Pallets and Blocks + pallet_size = (0.15, 0.15, 0.01) + block_size = (0.04, 0.04, 0.04) + pallet_urdf = 'pallet/pallet.urdf' + block_urdf = 'block/block.urdf' + + # Colors for blocks and spheres + colors = ['red', 'blue', 'green'] + color_objects = {} + + # Add pallets and blocks + for color in colors: + # Add pallet + pallet_pose = self.get_random_pose(env, pallet_size) + env.add_object(pallet_urdf, pallet_pose, category='fixed') + + # Add blocks + block_ids = [] + for _ in range(3): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) + block_ids.append(block_id) + + color_objects[color] = {'pallet': pallet_pose, 'blocks': block_ids} + + # Spheres + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + + # Add spheres + for color in colors: + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS[color]) + color_objects[color]['sphere'] = sphere_id + + # Goals + for color in colors: + # Goal: blocks are stacked in a pyramid on the pallet + block_poses = [(0, -0.02, 0.02), (0, 0.02, 0.02), (0, 0, 0.06)] + targs = [(utils.apply(color_objects[color]['pallet'], i), color_objects[color]['pallet'][1]) for i in block_poses] + + self.add_goal(objs=color_objects[color]['blocks'], matches=np.ones((3, 3)), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, + language_goal=self.lang_template.format(color=color)) + + # Goal: sphere is placed on top of the pyramid + sphere_pose = (0, 0, 0.1) + targ = (utils.apply(color_objects[color]['pallet'], sphere_pose), color_objects[color]['pallet'][1]) + + self.add_goal(objs=[color_objects[color]['sphere']], matches=np.ones((1, 1)), targ_poses=[targ], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2], + language_goal=self.lang_template.format(color=color)) +``` + +###" +" ""color-sequenced-sphere-placement"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-sequenced-sphere-placement"" in a python code block. +### + +"," + color-sequenced-sphere-placement: {'task-name': 'color-sequenced-sphere-placement', 'task-description': 'On the tabletop, there are four spheres of different colors (red, blue, green, and yellow) and four colored squares of matching colors. The task involves picking up each sphere and precisely placing it in the center of the square of the same color. However, the spheres must be placed in a specific sequence - red first, then blue, then green, and finally yellow. The task is challenging due to the need for precise placement, color coordination, and sequence following.', 'assets-used': ['sphere/sphere.urdf', 'square/square-template.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorSequencedSpherePlacement(Task): + """"""Pick up spheres of different colors and place them in the center of the square of the same color in a specific sequence."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""place the {color} sphere in the {color} square"" + self.task_completed_desc = ""done placing spheres."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their sequence + colors = ['red', 'blue', 'green', 'yellow'] + + # Add squares of different colors + square_size = (0.1, 0.1, 0.005) + square_urdf = 'square/square-template.urdf' + square_poses = [] + for color in colors: + square_pose = self.get_random_pose(env, square_size) + env.add_object(square_urdf, square_pose, 'fixed', color=color) + square_poses.append(square_pose) + + # Add spheres of different colors + sphere_size = (0.04, 0.04, 0.04) + sphere_urdf = 'sphere/sphere.urdf' + spheres = [] + for color in colors: + sphere_pose = self.get_random_pose(env, sphere_size) + sphere_id = env.add_object(sphere_urdf, sphere_pose, color=color) + spheres.append(sphere_id) + + # Goal: each sphere is in the square of the same color, in the correct sequence + for i in range(len(colors)): + self.add_goal(objs=[spheres[i]], matches=np.ones((1, 1)), targ_poses=[square_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/len(colors), + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""color-ordered-insertion-new"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""color-ordered-insertion-new"" in a python code block. +### + +"," + color-ordered-insertion-new: {'task-name': 'color-ordered-insertion-new', 'task-description': 'There are four differently-colored ell objects (red, blue, green, yellow) and a corresponding set of color-coded fixtures. The task involves picking up each ell object and inserting it into the matching color fixture in a specific order: from left to right, insert red, blue, green, and finally yellow. The challenge lies in the precise manipulation of the ell objects and the color-coordination required.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf']}``` + +### +```python +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ColorOrderedInsertionNew(Task): + """"""Insert differently-colored ell objects into the matching color fixture in a specific order."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""put the {color} L shape block in the L shape hole"" + self.task_completed_desc = ""done with insertion."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Define colors and their order + colors = ['red', 'blue', 'green', 'yellow'] + color_order = {color: i for i, color in enumerate(colors)} + + # Add fixtures. + fixture_size = (0.12, 0.12, 0.02) + fixture_urdf = 'insertion/fixture.urdf' + fixtures = [] + for color in colors: + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_id = env.add_object(fixture_urdf, fixture_pose, color=utils.COLORS[color], category='fixed') + fixtures.append(fixture_id) + + # Add ell objects. + ell_size = (0.04, 0.04, 0.04) + ell_urdf = 'insertion/ell.urdf' + ells = [] + for color in colors: + ell_pose = self.get_random_pose(env, ell_size) + ell_id = env.add_object(ell_urdf, ell_pose, color=utils.COLORS[color]) + ells.append(ell_id) + + # Goal: each ell is inserted into the matching color fixture in the correct order. + for i, ell in enumerate(ells): + self.add_goal(objs=[ell], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(fixtures[i])], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / len(ells), + language_goal=self.lang_template.format(color=colors[i])) +``` + +###" +" ""put-blocks-between-zones"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""put-blocks-between-zones"" in a python code block. +### + +"," + put-blocks-between-zones: {'task-name': 'put-blocks-between-zones', 'task-description': 'Arrange four differently colored blocks (red, blue, green, and yellow) between two designated zones on the tabletop.', 'assets-used': ['zone/zone.urdf', 'block/block.urdf']}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import copy + +class PutBlocksBetweenZones(Task): + """"""Arrange four differently colored blocks (red, blue, green, and yellow) between two designated zones on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""Arrange the blocks between the zones in the order: red, blue, green, yellow"" + self.task_completed_desc = ""done arranging blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zones. + zone_size = (0.12, 0.12, 0) + zone_urdf = 'zone/zone.urdf' + zone1_pose = self.get_random_pose(env, zone_size) + zone2_pose = copy.deepcopy(zone1_pose) + zone2_pose = (utils.apply(zone1_pose, (0,0.1,0)), zone2_pose[1]) + env.add_object(zone_urdf, zone1_pose, 'fixed') + env.add_object(zone_urdf, zone2_pose, 'fixed') + + # Block colors. + colors = [ + utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(4): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + blocks.append(block_id) + + # Goal: blocks are arranged between the zones in the order: red, blue, green, yellow. + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, 0.1, 0.03)] + targs = [(utils.apply(zone1_pose, i), zone1_pose[1]) for i in place_pos] + + # Add goal + self.add_goal(objs=blocks, matches=np.ones((4, 4)), targ_poses=targs, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, symmetries=[np.pi/2]*4, language_goal=self.lang_template) + +``` + +###" +" ""put-blues-around-red"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""put-blues-around-red"" in a python code block. +### + +"," + put-blues-around-red: {'task-name': 'put-blues-around-red', 'task-description': 'place the blue blocks around the red block.', 'assets-used': ['block/block_for_anchors.urdf']}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class PlaceBluesAroundRed(Task): + """"""Pick up the blue blocks one by one and place them around the red block, forming a circle."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""place the blue blocks around the red block"" + self.task_completed_desc = ""done placing blue blocks around red block."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add red block. + red_block_size = (0.04, 0.04, 0.04) + red_block_urdf = 'block/block_for_anchors.urdf' + red_block_pose = self.get_random_pose(env, red_block_size) + red_block_id = env.add_object(red_block_urdf, red_block_pose, 'fixed') + + # Add blue blocks. + blue_blocks = [] + blue_block_size = (0.02, 0.02, 0.02) + blue_block_urdf = 'block/block_for_anchors.urdf' + N = 4 + + for _ in range(N): + blue_block_pose = self.get_random_pose(env, blue_block_size) + blue_block_id = env.add_object(blue_block_urdf, blue_block_pose, color=utils.COLORS['blue']) + blue_blocks.append(blue_block_id) + + # Calculate target poses for blue blocks to form a circle around the red block. + radius = 0.06 # radius of the circle + angles = np.linspace(0, 2*np.pi, N, endpoint=False) # angles for each blue block + targ_poses = [] + for angle in angles: + x = red_block_pose[0][0] + radius * np.cos(angle) + y = red_block_pose[0][1] + radius * np.sin(angle) + z = red_block_pose[0][2] + targ_poses.append(((x, y, z), red_block_pose[1])) + + # Add goal. + self.add_goal(objs=blue_blocks, matches=np.eye(N), targ_poses=targ_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1., language_goal=self.lang_template) + +``` + +###" +" ""place-blue-on-line-ends"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""place-blue-on-line-ends"" in a python code block. +### + +"," + place-blue-on-line-ends: {'task-name': 'place-blue-on-line-ends', 'task-description': 'Pick up each blue box and accurately place it at the end of a green line.', 'assets-used': ['line/line-template.urdf', 'box/box-template.urdf']}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class PlaceBlueOnLineEnds(Task): + """"""Pick up each blue box and accurately place it at the end of a green line."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""place the blue box at the end of the green line"" + self.task_completed_desc = ""done placing blue boxes on line ends."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add lines. + line_size = (0.3, 0.01, 0.01) + line_template = 'line/line-template.urdf' + replace = {'DIM': line_size} + line_urdf = self.fill_template(line_template, replace) + + line_colors = ['green'] + line_poses = [] + + line_pose = self.get_random_pose(env, line_size) + color = utils.COLORS[line_colors[0]] + env.add_object(line_urdf, line_pose, 'fixed', color=color) + line_poses.append(utils.apply(line_pose, (-0.15,0,0))) + line_poses.append(utils.apply(line_pose, (0.15,0,0))) + + # Add blue boxes. + box_size = (0.04, 0.04, 0.04) + box_urdf = 'box/box-template.urdf' + box_color = utils.COLORS['blue'] + boxes = [] + for _ in range(2): + box_pose = self.get_random_pose(env, box_size) + box_id = env.add_object(box_urdf, box_pose, color=box_color) + boxes.append(box_id) + + # Goal: each blue box is at the end of a different colored line. + for i in range(2): + language_goal = self.lang_template.format(line_colors[0]) + self.add_goal(objs=[boxes[i]], matches=np.ones((1, 1)), targ_poses=[line_poses[i]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, language_goal=language_goal) + +``` + +###" +" ""stack-three-layer-red-wall"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""stack-three-layer-red-wall"" in a python code block. +### + +"," + stack-three-layer-red-wall: {'task-name': 'block-on-cylinder-on-pallet', 'task-description': 'On the tabletop, there are three cylinders of different colors (red, blue, and green) and three blocks of the same colors. The task involves picking up each block and placing it on the corresponding colored cylinder, which are located in specific positions on a pallet. Starting with the red block and cylinder, followed by blue and finally green. The challenge lies in the precise placement of the blocks on the cylinders, while maintaining color coordination.', 'assets-used': ['block/block.urdf', 'cylinder/cylinder-template.urdf', 'pallet/pallet.urdf']}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class StackThreeLayerRedWall(Task): + """"""Build a wall by stacking blocks. The wall should consist of three layers with each layer having three red blocks aligned in a straight line."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""stack the red blocks to form a three-layer wall"" + self.task_completed_desc = ""done stacking blocks."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + block_size = (0.05, 0.05, 0.03) # x, y, z dimensions for the block size + block_urdf = 'block/block_for_anchors.urdf' # URDF for the block + block_color = utils.COLORS['red'] # Color for the block + + # We need 9 blocks for a three-layer wall with each layer having three blocks. + blocks = [] + for _ in range(9): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=block_color) + blocks.append(block_id) + + # Define target poses for the blocks to form a three-layer wall. + # The target poses are defined relative to a base pose. + base_pose = ((0.5, 0.0, 0.0), (0, 0, 0, 1)) + target_poses = [] + for i in range(3): # three layers + for j in range(3): # three blocks per layer + target_pos = (j * block_size[0], 0, i * block_size[2]) + target_pose = (utils.apply(base_pose, target_pos), (0, 0, 0, 1)) + target_poses.append(target_pose) + + # Goal: all blocks are stacked to form a three-layer wall. + self.add_goal(objs=blocks[3*i:3*(i+1)], matches=np.ones((3, 3)), targ_poses=target_poses[3*i:3*(i+1)], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3., language_goal=self.lang_template) + +``` + +###" +" ""block-on-cylinder-on-pallet"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""block-on-cylinder-on-pallet"" in a python code block. +### + +"," + block-on-cylinder-on-pallet: {'task-name': 'block-on-cylinder-on-pallet', 'assets-used': ['pallet/pallet.urdf', 'cylinder/cylinder-template.urdf', 'block/block.urdf'], 'task-description': 'Pick up each block and place it on the corresponding colored cylinder, which are located in specific positions on a pallet.'}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BlockOnCylinderOnPallet(Task): + """"""Pick up each block and place it on the corresponding colored cylinder, which are located in specific positions on a pallet."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""place the {} cylinder on the pallet"" + self.lang_template_2 = ""place the {} block on the {} cylinder"" + + self.task_completed_desc = ""done placing blocks on cylinders and cylinder on pallet."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.35, 0.35, 0.01) + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_urdf = 'pallet/pallet.urdf' + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Define colors. + block_colors = ['red'] + cylinder_colors = ['blue'] + + # Add cylinders. + cylinder_size = (0.04, 0.04, 0.06) + cylinder_template = 'cylinder/cylinder-template.urdf' + cylinders = [] + + + replace = {'DIM': cylinder_size, 'HALF': (cylinder_size[0] / 2, cylinder_size[1] / 2, cylinder_size[2] / 2), 'COLOR': block_colors[0]} + cylinder_urdf = self.fill_template(cylinder_template, replace) + cylinder_pose = self.get_random_pose(env, cylinder_size) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose) + cylinders.append(cylinder_id) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=cylinder_colors[0]) + blocks.append(block_id) + + # Goal: place the cylinder on top of the pallet + self.add_goal(objs=[cylinders[0]], matches=np.ones((1, 1)), targ_poses=[pallet_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=self.lang_template.format(cylinder_colors[0])) + + + # Goal: place the block on top of the cylinder + language_goal = self.lang_template_2.format(block_colors[0], cylinder_colors[0]) + self.add_goal(objs=[blocks[0]], matches=np.ones((1, 1)), targ_poses=[pallet_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=language_goal) + +``` + +###" +" ""ball-on-box-on-container"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""ball-on-box-on-container"" in a python code block. +### + +"," + ball-on-box-on-container: {'task-name': 'ball-on-box-on-container', 'assets-used': ['container/container-template.urdf', 'box/box-template.urdf', 'ball/ball.urdf'], 'task-description': 'Pick up each ball and place it on the corresponding colored box, which are located in specific positions on a container.'}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BallOnBoxOnContainer(Task): + """"""Pick up each ball and place it on the corresponding colored box, which are located in specific positions on a container."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""place the {} box on the container"" + self.lang_template_2 = ""place the {} ball on the {} box"" + + self.task_completed_desc = ""done placing balls on boxs and box on container."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add container. + container_size = (0.2, 0.2, 0.06) + container_pose = self.get_random_pose(env, container_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf = self.fill_template(container_template, replace) + + env.add_object(container_urdf, container_pose, 'fixed') + + # Define colors. + ball_colors = ['red'] + box_colors = ['blue'] + + # Add boxs. + box_size = (0.04, 0.04, 0.06) + box_template = 'box/box-template.urdf' + boxs = [] + + + replace = {'DIM': box_size, 'HALF': (box_size[0] / 2, box_size[1] / 2, box_size[2] / 2), 'COLOR': ball_colors[0]} + box_urdf = self.fill_template(box_template, replace) + box_pose = self.get_random_pose(env, box_size) + box_id = env.add_object(box_urdf, box_pose) + boxs.append(box_id) + + # Add balls. + ball_size = (0.04, 0.04, 0.04) + ball_urdf = 'ball/ball.urdf' + balls = [] + ball_pose = self.get_random_pose(env, ball_size) + ball_id = env.add_object(ball_urdf, ball_pose, color=box_colors[0]) + balls.append(ball_id) + + # Goal: place the box on top of the container + self.add_goal(objs=[boxs[0]], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=self.lang_template.format(box_colors[0])) + + + # Goal: place the ball on top of the box + language_goal = self.lang_template_2.format(ball_colors[0], box_colors[0]) + self.add_goal(objs=[balls[0]], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=language_goal) + +``` + +###" +" ""align-rope-along-line"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""align-rope-along-line"" in a python code block. +### + +"," + align-rope-along-line: {'task-name': 'align-rope-along-line', 'assets-used': ['line/line-template.urdf'], 'task-description': 'Align a deformable rope along a straight line marked on the tabletop.'}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula + +class AlignRopeAlongLine(Task): + """"""Align a deformable rope along a straight line marked on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""align the rope along the line"" + self.task_completed_desc = ""done aligning."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add line. + length = np.random.uniform(0.18, 0.25) + line_size = (length, 0.01, 0.01) + line_pose = self.get_random_pose(env, line_size) + line_template = 'line/line-template.urdf' + replace = {'DIM': line_size, 'HALF': (line_size[0] / 2, line_size[1] / 2, line_size[2] / 2)} + line_urdf = self.fill_template(line_template, replace) + env.add_object(line_urdf, line_pose, 'fixed') + + # Add rope. + rope_size = (length, 0.01, 0.01) + rope_pose = self.get_random_pose(env, rope_size) + corner1_pose = utils.apply(line_pose, (length / 2, 0.01, 0.01)) + corner2_pose = utils.apply(line_pose, (-length / 2, 0.01, 0.01)) + rope_id, targets, matches = self.make_rope(env, (corner1_pose, corner2_pose), n_parts=15) + + # Goal: rope is aligned with the line. + self.add_goal(objs=rope_id, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + +``` + +###" +" ""align-rope-cross-zone"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""align-rope-cross-zone"" in a python code block. +### + +"," + align-rope-cross-zone: {'task-name': 'align-rope-cross-zone', 'assets-used': ['zone/zone.urdf'], 'task-description': 'Align a deformable rope across the diagonal of a zone marked on the tabletop.'}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula + +class AlignRopeCrossZone(Task): + """"""Align a deformable rope across the diagonal of a zone marked on the tabletop."""""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = ""align the rope across the diagonal of a zone"" + self.task_completed_desc = ""done aligning."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zone. + length = 0.12 + zone_size = (length, length, 0.01) + zone_pose = self.get_random_pose(env, zone_size) + zone_urdf = 'zone/zone.urdf' + env.add_object(zone_urdf, zone_pose, 'fixed') + + # Add rope. + rope_size = (length, 0.01, 0.01) + rope_pose = self.get_random_pose(env, rope_size) + corner1_pose = utils.apply(zone_pose, (length / 2, length / 2, 0.01)) + corner2_pose = utils.apply(zone_pose, (-length / 2, -length / 2, 0.01)) + rope_id, targets, matches = self.make_rope(env, (corner1_pose, corner2_pose), n_parts=10) + + # Goal: rope is aligned with the diagonal of the zone. + self.add_goal(objs=rope_id, matches=matches, targ_poses=targets, replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + +``` + +###" +" ""put-kit-in-bowl"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""put-kit-in-bowl"" in a python code block. +### + +"," + put-kit-in-bowl: {'task-name': 'put-kit-in-bowl', 'assets-used': ['stacking/block.urdf', 'bowl/bowl.urdf', 'kitting/object-template.urdf'], 'task-description': 'Place the specific kit in a bowl of specified color.'}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import random +import pybullet as p +import os + + +class PutKitInBowl(Task): + """"""Place the specific kit in a bowl of specified color."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""put the {pick} in a {place} bowl"" + self.task_completed_desc = ""done placing kit in bowls."" + self.additional_reset() + + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_objects = np.random.randint(1, n_bowls + 1) + colors, selected_color_names = utils.get_colors(mode=self.mode, n_colors=2) + block_urdf = 'stacking/block.urdf' + block_size = (0.04, 0.04, 0.04) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_id = env.add_object(bowl_urdf, bowl_pose, category='fixed', color=colors[1]) + bowl_poses.append(bowl_pose) + + # Add kits. + objects_ids = [] + obj_shapes = self.get_kitting_shapes(n_objects) + + for i in range(n_objects): + scale = utils.map_kit_scale((0.03, 0.03, 0.02)) + shape = os.path.join(self.assets_root, 'kitting', + f'{obj_shapes[i]:02d}.obj') + template = 'kitting/object-template.urdf' + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': colors[0]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + obj_pose = self.get_random_pose(env, block_size) + obj_id = env.add_object(urdf, obj_pose) + objects_ids.append(obj_id) + + # Goal: put each block in a different bowl. + pick_name = selected_color_names[0] + "" "" + utils.assembling_kit_shapes[obj_shapes[i]] + language_goal = (self.lang_template.format(pick=pick_name, place=selected_color_names[1])) + self.add_goal(objs=[obj_id], matches=np.ones((1, 1)), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / n_objects, language_goal=language_goal) + + # Only one mistake allowed. + self.max_steps = len(objects_ids) + 1 + + # Colors of distractor objects. + distractor_bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c not in selected_color_names] + distractor_block_colors = [utils.COLORS[c] for c in utils.COLORS if c not in selected_color_names] + + # Add distractors. + n_distractors = 0 + max_distractors = 6 + while n_distractors < max_distractors: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = distractor_block_colors if is_block else distractor_bowl_colors + pose = self.get_random_pose(env, size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +``` + +###" +" ""kit-in-bowl-in-zone"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""kit-in-bowl-in-zone"" in a python code block. +### + +"," + kit-in-bowl-in-zone: {'task-name': 'kit-in-bowl-in-zone', 'assets-used': ['zone/zone.urdf', 'bowl/bowl.urdf', 'kitting/object-template.urdf'], 'task-description': 'Pick up each kit and place it on the corresponding colored bowl, which are located in specific positions on a zone.'}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import os + +class KitInBowlInZone(Task): + """"""Pick up each kit and place it on the corresponding colored bowl, which are located in specific positions on a zone."""""" + + def __init__(self): + super().__init__() + self.max_steps = 15 + self.lang_template = ""place the {} bowl on the zone"" + self.lang_template_2 = ""place the {} on the {} bowl"" + + self.task_completed_desc = ""done placing kits on bowls and bowl on zone."" + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add zone. + zone_size = (0.2, 0.2, 0.01) + zone_pose = self.get_random_pose(env, zone_size) + zone_urdf = 'zone/zone.urdf' + env.add_object(zone_urdf, zone_pose, 'fixed') + + # Define colors. + kit_colors = ['red'] + bowl_colors = ['blue'] + + # Add bowls. + bowl_size = (0.04, 0.04, 0.06) + bowls = [] + bowl_urdf = 'bowl/bowl.urdf' + bowl_pose = self.get_random_pose(env, bowl_size) + bowl_id = env.add_object(bowl_urdf, bowl_pose) + bowls.append(bowl_id) + + # Add kits. + kit_size = utils.map_kit_scale((0.03, 0.03, 0.02)) + obj_shapes = self.get_kitting_shapes(1) + shape = os.path.join(self.assets_root, 'kitting', + f'{obj_shapes[0]:02d}.obj') + template = 'kitting/object-template.urdf' + replace = {'FNAME': (shape,), 'SCALE': kit_size, 'COLOR': kit_colors[0]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + kit_urdf = self.fill_template(template, replace) + kits = [] + kit_pose = self.get_random_pose(env, kit_size) + kit_id = env.add_object(kit_urdf, kit_pose, color=bowl_colors[0]) + kits.append(kit_id) + + # Goal: place the bowl on top of the zone + self.add_goal(objs=[bowls[0]], matches=np.ones((1, 1)), targ_poses=[zone_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=self.lang_template.format(bowl_colors[0])) + + + # Goal: place the kit on top of the bowl + pick_name = kit_colors[0] + "" "" + utils.assembling_kit_shapes[obj_shapes[0]] + language_goal = self.lang_template_2.format(pick_name, bowl_colors[0]) + self.add_goal(objs=[kits[0]], matches=np.ones((1, 1)), targ_poses=[zone_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1/2, language_goal=language_goal) + +``` + +###" +" ""move-kit-from-zone-to-cylinder"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""move-kit-from-zone-to-cylinder"" in a python code block. +### + +"," + move-kit-from-zone-to-cylinder: {'task-name': 'move-kit-from-zone-to-cylinder', 'assets-used': ['cylinder/cylinder-template.urdf', 'zone/zone.urdf', 'kitting/object-template.urdf', 'kitting/object-template.urdf'], 'task-description': 'Place the specific kit from a zone to a cylinder.'}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import random +import pybullet as p +import os +import copy + +class MoveKitFromZoneToCylinder(Task): + """"""Place the specific kit from a zone to a cylinder."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""put the {pick} from zone to {place} cylinder."" + self.task_completed_desc = ""done placing kit in zones."" + self.additional_reset() + + + def reset(self, env): + super().reset(env) + n_zones = 3 + n_objects = 3 + colors, color_names = utils.get_colors(mode=self.mode, n_colors=n_objects) + + # Add zones and objects + # x, y, z dimensions for the asset size + cylinder_size = (0.12, 0.12, 0) + cylinder_template = 'cylinder/cylinder-template.urdf' + cylinder_poses = [] + + zone_size = (0.06, 0.06, 0) + zone_urdf = 'zone/zone.urdf' + zone_poses = [] + objects_ids = [] + obj_shapes = self.get_kitting_shapes(n_objects) + + for i in range(n_zones): + # add zone + zone_pose = self.get_random_pose(env, zone_size) + zone_id = env.add_object(zone_urdf, zone_pose, category='fixed', color=colors[i]) + zone_poses.append(zone_pose) + + # add kit + scale = utils.map_kit_scale((0.03, 0.03, 0.02)) + shape = os.path.join(self.assets_root, 'kitting', + f'{obj_shapes[i]:02d}.obj') + template = 'kitting/object-template.urdf' + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + obj_pose = zone_pose + obj_id = env.add_object(urdf, obj_pose) + objects_ids.append(obj_id) + + # add cylinder + cylinder_pose = self.get_random_pose(env, zone_size) + template = 'kitting/object-template.urdf' + replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': colors[i]} + cylinder_urdf = self.fill_template(cylinder_template, replace) + cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, category='fixed', color=colors[i]) + cylinder_poses.append(cylinder_pose) + + # Goal: put a specific kit from a zone to the top of a cylinder + target_idx = np.random.randint(n_zones) + pick_name = color_names[target_idx] + "" "" + utils.assembling_kit_shapes[obj_shapes[target_idx]] + language_goal = (self.lang_template.format(pick=pick_name, place=color_names[target_idx])) + self.add_goal(objs=[objects_ids[target_idx]], matches=np.ones((1, 1)), targ_poses=[cylinder_poses[target_idx]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / n_objects, language_goal=language_goal) + +``` + +###" +" ""move-bowl-from-pallet-to-corner"". Format the answer in a python dictionary with keys ""task-name"", ""task-description"", and ""assets-used"". Then write the pybullet simulation code for the task ""move-bowl-from-pallet-to-corner"" in a python code block. +### + +"," + move-bowl-from-pallet-to-corner: {'task-name': 'move-bowl-from-pallet-to-corner', 'assets-used': ['corner/corner-template.urdf', 'pallet/pallet.urdf', 'bowl/bowl.urdf'], 'task-description': 'Place the specific bowl from a pallet to a corner.'}``` + +### +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import random +import pybullet as p +import os +import copy + +class MoveBowlFromPalletToCorner(Task): + """"""Place the specific bowl from a pallet to a corner."""""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = ""put the {pick} from pallet to {place} corner."" + self.task_completed_desc = ""done placing bowl around corner."" + self.additional_reset() + + + def reset(self, env): + super().reset(env) + n_pallets = 3 + n_objects = 3 + colors, color_names = utils.get_colors(mode=self.mode, n_colors=n_objects) + # Add pallets and objects + # x, y, z dimensions for the asset size + corner_size = (0.12, 0.12, 0) + corner_urdf = 'corner/corner-template.urdf' + corner_poses = [] + + pallet_size = (0.06, 0.06, 0) + pallet_urdf = 'pallet/pallet.urdf' + pallet_poses = [] + objects_ids = [] + bowl_shapes = self.get_kitting_shapes(n_objects) + + for i in range(n_pallets): + # add pallet + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_id = env.add_object(pallet_urdf, pallet_pose, category='fixed', color=colors[i]) + pallet_poses.append(pallet_pose) + + # add kit + bowl_urdf = 'bowl/bowl.urdf' + bowl_pose = pallet_pose + bowl_id = env.add_object(bowl_urdf, bowl_pose, color=colors[i]) + objects_ids.append(bowl_id) + + # add corner + corner_pose = self.get_random_pose(env, pallet_size) + corner_id = env.add_object(corner_urdf, corner_pose, category='fixed', color=colors[i]) + corner_poses.append(corner_pose) + + # Goal: put a specific kit from a pallet to the top of a corner + target_idx = np.random.randint(n_pallets) + pick_name = color_names[target_idx] + "" "" + 'bowl' + language_goal = (self.lang_template.format(pick=pick_name, place=color_names[target_idx])) + self.add_goal(objs=[objects_ids[target_idx]], matches=np.ones((1, 1)), targ_poses=[corner_poses[target_idx]], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / n_objects, language_goal=language_goal) + +``` + +###" diff --git a/prompts/finetune_instructions_prompt.txt b/prompts/finetune_instructions_prompt.txt new file mode 100644 index 0000000000000000000000000000000000000000..cdc31db4eaf62d7008d2d79cdefe2fa1dd5a2c3f --- /dev/null +++ b/prompts/finetune_instructions_prompt.txt @@ -0,0 +1,3 @@ + "TASK_NAME_TEMPLATE". Format the answer in a python dictionary with keys "task-name", "task-description", and "assets-used". Then write the pybullet simulation code for the task "TASK_NAME_TEMPLATE" in a python code block. +### + diff --git a/prompts/finetune_instructions_prompt_codeonly.txt b/prompts/finetune_instructions_prompt_codeonly.txt new file mode 100644 index 0000000000000000000000000000000000000000..28e913592fe51363660e2fd64c862fb676b069ba --- /dev/null +++ b/prompts/finetune_instructions_prompt_codeonly.txt @@ -0,0 +1,3 @@ +Write the pybullet simulation code for the task "TASK_NAME_TEMPLATE" in a python code block. +### + diff --git a/prompts/topdown_chain_of_thought_prompt/cliport_prompt_api_template.txt b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_api_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..f56b6cc4e25e7ce5f7f5788034851cb07bf3de28 --- /dev/null +++ b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_api_template.txt @@ -0,0 +1,355 @@ +Before writing the code for the task "TASK_NAME_TEMPLATE". Here are some APIs that are defined. Please confirm that you understand these APIs. + +""" +class Task(): + """Base Task class.""" + + def __init__(self): + self.ee = Suction + self.mode = 'train' + self.sixdof = False + self.primitive = primitives.PickPlace() + self.oracle_cams = cameras.Oracle.CONFIG + + # Evaluation epsilons (for pose evaluation metric). + self.pos_eps = 0.01 + self.rot_eps = np.deg2rad(15) + + # Workspace bounds. + self.pix_size = 0.003125 + self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]]) + self.zone_bounds = np.copy(self.bounds) + + self.goals = [] + self.lang_goals = [] + self.task_completed_desc = "task completed." + self.progress = 0 + self._rewards = 0 + self.assets_root = None + + def reset(self, env): + if not self.assets_root: + raise ValueError('assets_root must be set for task, ' + 'call set_assets_root().') + self.goals = [] + self.lang_goals = [] + self.progress = 0 # Task progression metric in range [0, 1]. + self._rewards = 0 # Cumulative returned rewards. + + # ------------------------------------------------------------------------- + # Oracle Agent + # ------------------------------------------------------------------------- + + def oracle(self, env): + """Oracle agent.""" + OracleAgent = collections.namedtuple('OracleAgent', ['act']) + + def act(obs, info): + """Calculate action.""" + + # Oracle uses perfect RGB-D orthographic images and segmentation masks. + _, hmap, obj_mask = self.get_true_image(env) + + # Unpack next goal step. + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] + + # Match objects to targets without replacement. + if not replace: + + # Modify a copy of the match matrix. + matches = matches.copy() + + # Ignore already matched objects. + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + if self.is_match(pose, targs[j], symmetry): + matches[i, :] = 0 + matches[:, j] = 0 + + # Get objects to be picked (prioritize farthest from nearest neighbor). + nn_dists = [] + nn_targets = [] + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + xyz, _ = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + if len(targets_i) > 0: + targets_xyz = np.float32([targs[j][0] for j in targets_i]) + dists = np.linalg.norm( + targets_xyz - np.float32(xyz).reshape(1, 3), axis=1) + nn = np.argmin(dists) + nn_dists.append(dists[nn]) + nn_targets.append(targets_i[nn]) + + # Handle ignored objects. + else: + nn_dists.append(0) + nn_targets.append(-1) + order = np.argsort(nn_dists)[::-1] + + # Filter out matched objects. + order = [i for i in order if nn_dists[i] > 0] + + pick_mask = None + for pick_i in order: + pick_mask = np.uint8(obj_mask == objs[pick_i][0]) + + # Erode to avoid picking on edges. + # pick_mask = cv2.erode(pick_mask, np.ones((3, 3), np.uint8)) + + if np.sum(pick_mask) > 0: + break + + # Trigger task reset if no object is visible. + if pick_mask is None or np.sum(pick_mask) == 0: + self.goals = [] + self.lang_goals = [] + print('Object for pick is not visible. Skipping demonstration.') + return + + # Get picking pose. + pick_prob = np.float32(pick_mask) + pick_pix = utils.sample_distribution(pick_prob) + # For "deterministic" demonstrations on insertion-easy, use this: + # pick_pix = (160,80) + pick_pos = utils.pix_to_xyz(pick_pix, hmap, + self.bounds, self.pix_size) + pick_pose = (np.asarray(pick_pos), np.asarray((0, 0, 0, 1))) + + # Get placing pose. + targ_pose = targs[nn_targets[pick_i]] + obj_pose = p.getBasePositionAndOrientation(objs[pick_i][0]) + if not self.sixdof: + obj_euler = utils.quatXYZW_to_eulerXYZ(obj_pose[1]) + obj_quat = utils.eulerXYZ_to_quatXYZW((0, 0, obj_euler[2])) + obj_pose = (obj_pose[0], obj_quat) + world_to_pick = utils.invert(pick_pose) + obj_to_pick = utils.multiply(world_to_pick, obj_pose) + pick_to_obj = utils.invert(obj_to_pick) + place_pose = utils.multiply(targ_pose, pick_to_obj) + + # Rotate end effector? + if not rotations: + place_pose = (place_pose[0], (0, 0, 0, 1)) + + place_pose = (np.asarray(place_pose[0]), np.asarray(place_pose[1])) + + return {'pose0': pick_pose, 'pose1': place_pose} + + return OracleAgent(act) + + # ------------------------------------------------------------------------- + # Reward Function and Task Completion Metrics + # ------------------------------------------------------------------------- + + def reward(self): + """Get delta rewards for current timestep. + + Returns: + A tuple consisting of the scalar (delta) reward, plus `extras` + dict which has extra task-dependent info from the process of + computing rewards that gives us finer-grained details. Use + `extras` for further data analysis. + """ + reward, info = 0, {} + + # Unpack next goal step. + objs, matches, targs, _, _, metric, params, max_reward = self.goals[0] + + # Evaluate by matching object poses. + if metric == 'pose': + step_reward = 0 + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + target_pose = targs[j] + if self.is_match(pose, target_pose, symmetry): + step_reward += max_reward / len(objs) + print(f"object {i} match with target {j} rew: {step_reward}") + break + + # Evaluate by measuring object intersection with zone. + elif metric == 'zone': + zone_pts, total_pts = 0, 0 + obj_pts, zones = params + for zone_idx, (zone_pose, zone_size) in enumerate(zones): + + # Count valid points in zone. + for obj_idx, obj_id in enumerate(obj_pts): + pts = obj_pts[obj_id] + obj_pose = p.getBasePositionAndOrientation(obj_id) + world_to_zone = utils.invert(zone_pose) + obj_to_zone = utils.multiply(world_to_zone, obj_pose) + pts = np.float32(utils.apply(obj_to_zone, pts)) + if len(zone_size) > 1: + valid_pts = np.logical_and.reduce([ + pts[0, :] > -zone_size[0] / 2, pts[0, :] < zone_size[0] / 2, + pts[1, :] > -zone_size[1] / 2, pts[1, :] < zone_size[1] / 2, + pts[2, :] < self.zone_bounds[2, 1]]) + + # if zone_idx == matches[obj_idx].argmax(): + zone_pts += np.sum(np.float32(valid_pts)) + total_pts += pts.shape[1] + step_reward = max_reward * (zone_pts / total_pts) + + # Get cumulative rewards and return delta. + reward = self.progress + step_reward - self._rewards + self._rewards = self.progress + step_reward + + # Move to next goal step if current goal step is complete. + if np.abs(max_reward - step_reward) < 0.01: + self.progress += max_reward # Update task progress. + self.goals.pop(0) + if len(self.lang_goals) > 0: + self.lang_goals.pop(0) + + return reward, info + + def done(self): + """Check if the task is done or has failed. + + Returns: + True if the episode should be considered a success, which we + use for measuring successes, which is particularly helpful for tasks + where one may get successes on the very last time step, e.g., getting + the cloth coverage threshold on the last alllowed action. + However, for bag-items-easy and bag-items-hard (which use the + 'bag-items' metric), it may be necessary to filter out demos that did + not attain sufficiently high reward in external code. Currently, this + is done in `main.py` and its ignore_this_demo() method. + """ + return (len(self.goals) == 0) or (self._rewards > 0.99) + # return zone_done or defs_done or goal_done + + # ------------------------------------------------------------------------- + # Environment Helper Functions + # ------------------------------------------------------------------------- + + def is_match(self, pose0, pose1, symmetry): + """Check if pose0 and pose1 match within a threshold.""" + + # Get translational error. + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) + dist_pos = np.linalg.norm(diff_pos) + + # Get rotational error around z-axis (account for symmetries). + diff_rot = 0 + if symmetry > 0: + rot0 = np.array(utils.quatXYZW_to_eulerXYZ(pose0[1]))[2] + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + diff_rot = np.abs(rot0 - rot1) % symmetry + if diff_rot > (symmetry / 2): + diff_rot = symmetry - diff_rot + + return (dist_pos < self.pos_eps) and (diff_rot < self.rot_eps) + + def get_random_pose(self, env, obj_size): + """Get random collision-free object pose within workspace bounds.""" + + # Get erosion size of object in pixels. + max_size = np.sqrt(obj_size[0] ** 2 + obj_size[1] ** 2) + erode_size = int(np.round(max_size / self.pix_size)) + + _, hmap, obj_mask = self.get_true_image(env) + + # Randomly sample an object pose within free-space pixels. + free = np.ones(obj_mask.shape, dtype=np.uint8) + for obj_ids in env.obj_ids.values(): + for obj_id in obj_ids: + free[obj_mask == obj_id] = 0 + free[0, :], free[:, 0], free[-1, :], free[:, -1] = 0, 0, 0, 0 + free = cv2.erode(free, np.ones((erode_size, erode_size), np.uint8)) + + # if np.sum(free) == 0: + # return None, None + + if np.sum(free) == 0: + # avoid returning None, None + # return None, None + pix = (obj_mask.shape[0] // 2, obj_mask.shape[1] // 2) + else: + pix = utils.sample_distribution(np.float32(free)) + pos = utils.pix_to_xyz(pix, hmap, self.bounds, self.pix_size) + pos = (pos[0], pos[1], obj_size[2] / 2) + theta = np.random.rand() * 2 * np.pi + rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + return pos, rot + + def get_lang_goal(self): + if len(self.lang_goals) == 0: + return self.task_completed_desc + else: + return self.lang_goals[0] + + def get_reward(self): + return float(self._rewards) + + # ------------------------------------------------------------------------- + # Helper Functions + # ------------------------------------------------------------------------- + + def fill_template(self, template, replace): + """Read a file and replace key strings.""" + full_template_path = os.path.join(self.assets_root, template) + with open(full_template_path, 'r') as file: + fdata = file.read() + for field in replace: + for i in range(len(replace[field])): + fdata = fdata.replace(f'{field}{i}', str(replace[field][i])) + alphabet = string.ascii_lowercase + string.digits + rname = ''.join(random.choices(alphabet, k=16)) + tmpdir = tempfile.gettempdir() + template_filename = os.path.split(template)[-1] + fname = os.path.join(tmpdir, f'{template_filename}.{rname}') + with open(fname, 'w') as file: + file.write(fdata) + return fname + + def get_random_size(self, min_x, max_x, min_y, max_y, min_z, max_z): + """Get random box size.""" + size = np.random.rand(3) + size[0] = size[0] * (max_x - min_x) + min_x + size[1] = size[1] * (max_y - min_y) + min_y + size[2] = size[2] * (max_z - min_z) + min_z + return tuple(size) + + def color_random_brown(self, obj): + shade = np.random.rand() + 0.5 + color = np.float32([shade * 156, shade * 117, shade * 95, 255]) / 255 + p.changeVisualShape(obj, -1, rgbaColor=color) + + """"" + + # Environment Class + def add_object(self, urdf, pose, category='rigid'): + """List of (fixed, rigid, or deformable) objects in env.""" + fixed_base = 1 if category == 'fixed' else 0 + obj_id = pybullet_utils.load_urdf( + p, + os.path.join(self.assets_root, urdf), + pose[0], + pose[1], + useFixedBase=fixed_base) + self.obj_ids[category].append(obj_id) + return obj_id +""" + +========= +Note that the objects need to obey physics and not collide with each other, and the object goal poses need to be above the table with lower bound x=0.25, y=-0.5 and upper bound x=0.75, y=0.5. When there are multiple objects for a multi-step pick-and-place task, there are often multiple subgoals. Once the task and environment are generated, an agent with a pick and place primitive will follow the defined goal to accomplish the tasks. + +Additionally, make sure you understand and summarize the ``self.goals`` variables, which has a list of 8-tuple with (objs, matches, targ_poses, replace, rotations, metric, params, step_max_reward, symmetries). +- objs (List of obj_id): object ID. +- matches (Binary Matrix): a binary matrix that denotes which object is matched with which target. This matrix has dimension len(objs) x len(targs). +- targ_poses (List of Poses [(translation, rotation)] ): a list of target poses of tuple (translation, rotation). +- replace (Boolean): whether each object can match with one unique target. This is important if we have one target and multiple objects. If it's set to be false, then any object matching with the target will satisfy. +- rotations (Boolean): whether the placement action has a rotation degree of freedom. +- metric (`pose` or `zone`): `pose` or `zone` that the object needs to be transported to. Example: `pose`. +- params (List of (zone_target, zone_size)): a list of (zone_target, zone_size) for each zone if the metric is `zone`. +- step_max_reward (float): the total reward of matching all the objects with all the target poses. It is not dependent on the number of objects but dependent on the number of goals. +- symmetries: the radians that the object is symmetric around z axis. + diff --git a/prompts/topdown_chain_of_thought_prompt/cliport_prompt_code_candidate_template.txt b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_code_candidate_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..52748b02a2e0b466f5c9bc341eda788332b2796a --- /dev/null +++ b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_code_candidate_template.txt @@ -0,0 +1,19 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + +""" +TASK_CODE_REFERENCE_TEMPLATE +""" + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Use `add_goal()` multiple times to give step-by-step language subgoal and placement subgoal for the task. + +Note: +1. Use `make_piles` to create piles of small blocks. +2. Use `make_ropes` to create cables. +3. Us `self.primitive = primitives.push` and `self.ee = Spatula` to use spatula. +4. Do not use random pose or the initial pose for the target poses. Come up with the specified pose. +5. Do not use target poses that are not in the workspace bound `bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]])`. A good starting center point for the table is [0.5,0,0]. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE + + + diff --git a/prompts/topdown_chain_of_thought_prompt/cliport_prompt_code_reference_selection_template.txt b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_code_reference_selection_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..dc3e57f92acf55463dc41c1e53b625fbc90ac441 --- /dev/null +++ b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_code_reference_selection_template.txt @@ -0,0 +1,7 @@ +Now I will provide you some reference code that might help you can write the code for the task "TASK_STRING_TEMPLATE". + + +TASK_CODE_LIST_TEMPLATE + + +Please pick 2 task python files that you would like to use as reference. Format them in a python list. diff --git a/prompts/topdown_chain_of_thought_prompt/cliport_prompt_code_split_template.txt b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_code_split_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf5c20cbf04444d9b7296fadf58bb6f49531b364 --- /dev/null +++ b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_code_split_template.txt @@ -0,0 +1,247 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Use functions `make_piles` and `make_ropes` for creating piles and cables. Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/topdown_chain_of_thought_prompt/cliport_prompt_common_errors_template.txt b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_common_errors_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..042c041b08950464da8d976ae2218e645c7afa57 --- /dev/null +++ b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_common_errors_template.txt @@ -0,0 +1,101 @@ +Before writing the code for the task "TASK_NAME_TEMPLATE". Here are some runtime errors that you do not want to make. Please confirm that you understand these runtime errors. + +""" +- environment.py, line 338, in info + pos, rot = p.getBasePositionAndOrientation(obj_id) +TypeError: an integer is required (got type NoneType) + +- task.py, line 118, in act + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] +IndexError: list index out of range + +- task.py, line 308, in is_match + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) +TypeError: 'float' object is not subscriptable + +- task.py", line 315, in is_match + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + +- utils.py", line 280, in quatXYZW_to_eulerXYZ + quaternion_wxyz = np.array([q[3], q[0], q[1], q[2]]) +IndexError: tuple index out of range + +- pallet_pose = self.get_random_pose(env, pallet_size) +pallet_surface_height = pallet_pose[0][2] +TypeError: 'NoneType' object is not subscriptable + +- No such file or directory: './cliport/environments/assets/circle/circle-template.urdf' + +- No such file or directory: './cliport/environments/assets/block/block-template.urdf' + +- task.py", line 308, in is_match + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) +IndexError: invalid index to scalar variable. + +-TypeError: get_random_size() missing 4 required positional arguments: 'min_y', 'max_y', 'min_z', and 'max_z' + +- task.py", line 195, in reward + obj_pts, zones = params +TypeError: cannot unpack non-iterable NoneType object + +- environment.py", line 230, in step + reward, info = self.task.reward() if action is not None else (0, {}) + File "task.py", line 200, in reward + pts = obj_pts[obj_id] +IndexError: arrays used as indices must be of integer (or boolean) type + +- environment.py", line 195, in reset + self.task.reset(self) + File "", line 38, in reset +TypeError: can only concatenate str (not "list") to str + +- environment.py", line 195, in reset + object_shape = np.random.choice(object_shapes) + in numpy.random.mtrand.RandomState.choice +ValueError: a must be 1-dimensional + +- No such file or directory: 'assets/box-template/box-template.urdf' + +- line 38, in reset.py +{'HALF': box_size / 2} +TypeError: unsupported operand type(s) for /: 'tuple' and 'int'. box_size is a tuple not a float. + +- line 38, in reset.py +IndexError: tuple index out of range +box_pose = (pallet_pose[0], pallet_pose[1], pallet_pose[2] + np.sum(box_sizes[:i+1])) + +- task.py", line 338, in fill_template + for i in range(len(replace[field])): +TypeError: object of type 'float' has no len(). + +- task.py", line 325, in get_random_pose + pos = (pos[0], pos[1], obj_size[2] / 2) +IndexError: tuple index out of range + +- task.py", line 206, in reward + for zone_idx, (zone_pose, zone_size) in enumerate(zones): +TypeError: 'NoneType' object is not iterable + +- task.py", +ball_pose = self.get_random_pose(env, ball_size) +ball_pose[0][2] += 0.02 +TypeError: 'tuple' object does not support item assignment +""" + + +You do not want to make mistakes such as +- using assets (urdfs) that do not exist +- use ambiguous language descriptions as goals. For instance, "place the colored blocks into the matching colored bowls" with one goal and sparse reward as the task instead of adding subgoal "place blue block into blue bowl" and give continuous reward. +- `matches` in the goal has wrong dimensions. It should have the same dimensions as number of objects (N) multiplied by the number of goal poses (M). Usually it is N by M. +- have vector dimension problem such as `np.random.choice(box_size)` or `box_size / 2` where `box_size` is a tuple and not an int +- make too large an object for stacking or make the task objects invisible for picking. +- accessing index out of bound `pallet_pose[2]` for `pallet_pose`. `pallet_pose=get_random_pose` returns a tuple (translation, rotation). It does not have 3rd component. Similarly accessing `container_pose[2]` or `box_pose[2]` would cause errors as well. Since it's a tuple, try to modify it in-place will also trigger errors. +- forget to replace str using `fill_template()` for urdfs with template such as `cylinder-template.urdf`. `ball-template.urdf`, `line-template.urf`. +- use `self.ee = Spatula()` as a function when doing pushing tasks, which is incorrect. It should be `self.ee = Spatula`. +- forget to compute target poses `targ_poses` for matching. Do not use object IDs for poses. +- change colors of complex objects such as `zone`. You can only change color of teomplate primitive such as `cylinder-template`. +- mistakenly use `random_pose` for target pose. Design target poses based on task objectives. +- forget to compute target poses `targ_poses` for matching. Do not use object IDs for poses. +- set asset target poses such as `place_pos` at the wrong place near origin, it should be around the table center with [0.5,0,0]. +- forget to use functions `make_piles` and `make_ropes` for creating piles and rope. To use spatula together with the push primitives. +- add only one or fewer language goals which causes language-motion inconsistentcy. Note that the language goals usually are the same number as the pick and place goals. diff --git a/prompts/topdown_chain_of_thought_prompt/cliport_prompt_task.txt b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_task.txt new file mode 100644 index 0000000000000000000000000000000000000000..69bdd326a196ce49883f222e3dc3dd7bb598f673 --- /dev/null +++ b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_task.txt @@ -0,0 +1,94 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task for tabletop manipulation. My goal is to design creative and feasible simpler tasks to eventually help solve the task `TARGET_TASK_NAME`. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Please try to come up with tasks using only these assets. +""" +TASK_ASSET_PROMPT +""" + +There are certain rules on the asset usage. +1. Sweeping piles task must have small blocks `block/small.urdf` and zones `zone.urdf`. Only the piles can be swept in all assets +2. Insertion tasks must have `insertion/ell.urdf` and `insertion/fixture.urdf`. Only the fixture can be inserted in all assets. + +========= +Here are some examples of good tasks. Try to be creative and high standard, and avoid overlapping with these tasks. + +TASK_DESCRIPTION_PROMPT + + +========= +Here are some bad example task instances with reasons. +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "box/box-template.urdf], +} +reasons: not interesting because it overlaps with the current task `put-block-in-bowl`. + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], +} +reasons: the language descriptions are too ambiguous. Navigation is also hard to complete. Also maze.urf does not exist. + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], +} +reasons: this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere. Similarly you cannot create task like `insert-ball-into-cylinder`. + +{ + "task-name": "ball-box-obstacle-course", + "task-description": "Navigate a ball through an obstacle course created by randomly placed boxes and finally place it inside a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: Navigate the ball is not related to tabletop manipulation tasks. + +{ + "task-name": "ball-in-box", + "task-description": "Use a cable to guide a ball into an open box.", + "assets-used": ["cable/cable.urdf", "ball/ball-template.urdf", "box/box-template.urdf"] +} +reasons: This task is too hard since it involves interaction of the cable and the ball and cannot be easily completed. + +{ + "task-name": "ball-in-container", + "task-description": "Use the spatula to lift a ball over a wall of boxes and drop it into a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: The only action primitives as pick and place. One cannot use a spatula to lift an object. + +{ + "task-name": "line-ball-sorting", + "task-description": "Move balls of different colors along a single green line, placing each ball in a designated colored box at the end of the line. The challenge includes precision in maintaining the ball on the line and the correct identification of the box color corresponding to each ball.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf"] +} +reasons: Piling or stacking balls are physically infeasible in the simulation. + + +{ + "task-name": "sweep-and-stack-blocks", + "task-description": "Sweep a pile of small red and blue blocks into two separate zones marked on the tabletop. Then pick up these blocks in each zone and stack them in two towers according to their colors, with the red tower higher than the blue.", + "assets-used": ["zone/zone.urdf", "block/small.urdf"] +} +reasons: Cannot do sweeping and stacking in the same task. + +========= +Here are some tasks that you have come up with before. Try to have a high-standard and avoid overlapping with these tasks. For instance, `bowl_ball_placement` and `sort_balls_in_bowls` are the same task. `pile_boxes_in_corner` and `stack_blocks_into_pallet` are similar tasks, `align-cylinder-in-corner` and `align-cylinder-corner` are similar. +Past Tasks: + +PAST_TASKNAME_TEMPLATE + +========= +The goal is to solve the task `TARGET_TASK_NAME` eventually. Due to its complexity, let's think step-by-step about what simpler task can be useful to achieve this goal. Please describe the new task, which is not `TARGET_TASK_NAME` but can help training a policy to generalize torwards it, in natural languages in a clear and detailed way. Think step by step how this task can help contribute to the skills that are quired to solve TARGET_TASK_NAME. Then format the answer in a python dictionary with keys "task-name" and value type string with lower-case and separated by hyphens, "task-description" (one sentence and do not mention urdf paths) and value type string, and "assets-used" and value type list of strings. + +Note: +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. +- Blocks and boxes are easier to stack than cylinders or balls. Specifically, you can update dimensions of a "box/box-template.urdf". +- Avoiding small obstacles / blocks is not that interesting in this context. +- Try to use `box-template.urdf` for modifiable blocks instead of `block.urdf'. diff --git a/prompts/topdown_chain_of_thought_prompt/cliport_prompt_task_reflection.txt b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_task_reflection.txt new file mode 100644 index 0000000000000000000000000000000000000000..cb2c7ba991f08941a9247cd1b3d863509c4a13e2 --- /dev/null +++ b/prompts/topdown_chain_of_thought_prompt/cliport_prompt_task_reflection.txt @@ -0,0 +1,78 @@ + + +Do you think your task is sufficently interesting to be added as a new task for future task given that we already have the following task name and descriptions? Moreover, does the simulation code achieve the goal and the language descriptions in the task? Be as rigorous and high-standard as possible. + +Reminder: +========= +the new task: +TASK_STRING_TEMPLATE + +========= + +the new task implementation: +TASK_CODE_TEMPLATE + +========= + +the goal task: +TARGET_TASK_NAME + + +current task list: +CURRENT_TASK_NAME_TEMPLATE + +========= +Reply explain your reasons and then say True or False, formatted in a python dictionary, do not miss commas or add extra spaces. Here are some examples. + +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "yellow-bowl.urdf", "box/box-template.urdf], + "reasons": "not interesting because it overlaps with the current task `put-block-in-bowl`" + "add_to_the_task_list": "False", +} + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], + "reasons": "the language descriptions are too ambiguous. Navigation is also hard to complete." + "add_to_the_task_list": "False", +} + +{ + "task-name": "sort-blocks-by-zone", + "task-description": "Sort different colored blocks into their corresponding colored zones marked on the tabletop.", + "assets-used": ["zone/zone.urdf", "stacking/block.urdf"], + "reasons": "the task is not covered in the current list and is an interesting combination of zone and block objects." + "add_to_the_task_list": "True", +} + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], + "reasons": "this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere.", + "add_to_the_task_list": "False", +} + +{ + "task-name": "arrange-blocks-on-pallet", + "task-description": "arrange the blocks on the pallet in the order: red, orange, yellow, green, blue, and purple.", + "assets-used": ["stacking/block.urdf", "pallet/pallelt.urdf"], + "reasons": "this task overlaps with `arrange-boxes-on-pallet` by merely changing the blocks to boxes.", + "add_to_the_task_list": "False", +} + +{ + "task-name": "cylinder-color-alignment", + "task-description": "Pick up cylinders of different colors and align them inside a box in the order of the colors displayed on the single green line on the tabletop.", + "assets-used": ["cylinder/cylinder-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf + "reasons": "the execution code only has one goal, i.e. it will only move one cylinder on top of the box, which is not that interesting.", + "add_to_the_task_list": "False","] +} + +========= + +Please incorporate these feedbacks when you design new task! + diff --git a/prompts/topdown_task_generation_prompt/cliport_prompt_api_template.txt b/prompts/topdown_task_generation_prompt/cliport_prompt_api_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..109662809a0913cb94f00960b2206f2961c82850 --- /dev/null +++ b/prompts/topdown_task_generation_prompt/cliport_prompt_api_template.txt @@ -0,0 +1,356 @@ +Before writing the code for the task "TASK_NAME_TEMPLATE". Here are some APIs that are defined. Please confirm that you understand these APIs. + +""" +class Task(): + """Base Task class.""" + + def __init__(self): + self.ee = Suction + self.mode = 'train' + self.sixdof = False + self.primitive = primitives.PickPlace() + self.oracle_cams = cameras.Oracle.CONFIG + + # Evaluation epsilons (for pose evaluation metric). + self.pos_eps = 0.01 + self.rot_eps = np.deg2rad(15) + + # Workspace bounds. + self.pix_size = 0.003125 + self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]]) + self.zone_bounds = np.copy(self.bounds) + + self.goals = [] + self.lang_goals = [] + self.task_completed_desc = "task completed." + self.progress = 0 + self._rewards = 0 + self.assets_root = None + + def reset(self, env): + if not self.assets_root: + raise ValueError('assets_root must be set for task, ' + 'call set_assets_root().') + self.goals = [] + self.lang_goals = [] + self.progress = 0 # Task progression metric in range [0, 1]. + self._rewards = 0 # Cumulative returned rewards. + + # ------------------------------------------------------------------------- + # Oracle Agent + # ------------------------------------------------------------------------- + + def oracle(self, env): + """Oracle agent.""" + OracleAgent = collections.namedtuple('OracleAgent', ['act']) + + def act(obs, info): + """Calculate action.""" + + # Oracle uses perfect RGB-D orthographic images and segmentation masks. + _, hmap, obj_mask = self.get_true_image(env) + + # Unpack next goal step. + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] + + # Match objects to targets without replacement. + if not replace: + + # Modify a copy of the match matrix. + matches = matches.copy() + + # Ignore already matched objects. + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + if self.is_match(pose, targs[j], symmetry): + matches[i, :] = 0 + matches[:, j] = 0 + + # Get objects to be picked (prioritize farthest from nearest neighbor). + nn_dists = [] + nn_targets = [] + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + xyz, _ = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + if len(targets_i) > 0: + targets_xyz = np.float32([targs[j][0] for j in targets_i]) + dists = np.linalg.norm( + targets_xyz - np.float32(xyz).reshape(1, 3), axis=1) + nn = np.argmin(dists) + nn_dists.append(dists[nn]) + nn_targets.append(targets_i[nn]) + + # Handle ignored objects. + else: + nn_dists.append(0) + nn_targets.append(-1) + order = np.argsort(nn_dists)[::-1] + + # Filter out matched objects. + order = [i for i in order if nn_dists[i] > 0] + + pick_mask = None + for pick_i in order: + pick_mask = np.uint8(obj_mask == objs[pick_i][0]) + + # Erode to avoid picking on edges. + # pick_mask = cv2.erode(pick_mask, np.ones((3, 3), np.uint8)) + + if np.sum(pick_mask) > 0: + break + + # Trigger task reset if no object is visible. + if pick_mask is None or np.sum(pick_mask) == 0: + self.goals = [] + self.lang_goals = [] + print('Object for pick is not visible. Skipping demonstration.') + return + + # Get picking pose. + pick_prob = np.float32(pick_mask) + pick_pix = utils.sample_distribution(pick_prob) + # For "deterministic" demonstrations on insertion-easy, use this: + # pick_pix = (160,80) + pick_pos = utils.pix_to_xyz(pick_pix, hmap, + self.bounds, self.pix_size) + pick_pose = (np.asarray(pick_pos), np.asarray((0, 0, 0, 1))) + + # Get placing pose. + targ_pose = targs[nn_targets[pick_i]] + obj_pose = p.getBasePositionAndOrientation(objs[pick_i][0]) + if not self.sixdof: + obj_euler = utils.quatXYZW_to_eulerXYZ(obj_pose[1]) + obj_quat = utils.eulerXYZ_to_quatXYZW((0, 0, obj_euler[2])) + obj_pose = (obj_pose[0], obj_quat) + world_to_pick = utils.invert(pick_pose) + obj_to_pick = utils.multiply(world_to_pick, obj_pose) + pick_to_obj = utils.invert(obj_to_pick) + place_pose = utils.multiply(targ_pose, pick_to_obj) + + # Rotate end effector? + if not rotations: + place_pose = (place_pose[0], (0, 0, 0, 1)) + + place_pose = (np.asarray(place_pose[0]), np.asarray(place_pose[1])) + + return {'pose0': pick_pose, 'pose1': place_pose} + + return OracleAgent(act) + + # ------------------------------------------------------------------------- + # Reward Function and Task Completion Metrics + # ------------------------------------------------------------------------- + + def reward(self): + """Get delta rewards for current timestep. + + Returns: + A tuple consisting of the scalar (delta) reward, plus `extras` + dict which has extra task-dependent info from the process of + computing rewards that gives us finer-grained details. Use + `extras` for further data analysis. + """ + reward, info = 0, {} + + # Unpack next goal step. + objs, matches, targs, _, _, metric, params, max_reward = self.goals[0] + + # Evaluate by matching object poses. + if metric == 'pose': + step_reward = 0 + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + target_pose = targs[j] + if self.is_match(pose, target_pose, symmetry): + step_reward += max_reward / len(objs) + print(f"object {i} match with target {j} rew: {step_reward}") + break + + # Evaluate by measuring object intersection with zone. + elif metric == 'zone': + zone_pts, total_pts = 0, 0 + obj_pts, zones = params + for zone_idx, (zone_pose, zone_size) in enumerate(zones): + + # Count valid points in zone. + for obj_idx, obj_id in enumerate(obj_pts): + pts = obj_pts[obj_id] + obj_pose = p.getBasePositionAndOrientation(obj_id) + world_to_zone = utils.invert(zone_pose) + obj_to_zone = utils.multiply(world_to_zone, obj_pose) + pts = np.float32(utils.apply(obj_to_zone, pts)) + if len(zone_size) > 1: + valid_pts = np.logical_and.reduce([ + pts[0, :] > -zone_size[0] / 2, pts[0, :] < zone_size[0] / 2, + pts[1, :] > -zone_size[1] / 2, pts[1, :] < zone_size[1] / 2, + pts[2, :] < self.zone_bounds[2, 1]]) + + # if zone_idx == matches[obj_idx].argmax(): + zone_pts += np.sum(np.float32(valid_pts)) + total_pts += pts.shape[1] + step_reward = max_reward * (zone_pts / total_pts) + + # Get cumulative rewards and return delta. + reward = self.progress + step_reward - self._rewards + self._rewards = self.progress + step_reward + + # Move to next goal step if current goal step is complete. + if np.abs(max_reward - step_reward) < 0.01: + self.progress += max_reward # Update task progress. + self.goals.pop(0) + if len(self.lang_goals) > 0: + self.lang_goals.pop(0) + + return reward, info + + def done(self): + """Check if the task is done or has failed. + + Returns: + True if the episode should be considered a success, which we + use for measuring successes, which is particularly helpful for tasks + where one may get successes on the very last time step, e.g., getting + the cloth coverage threshold on the last alllowed action. + However, for bag-items-easy and bag-items-hard (which use the + 'bag-items' metric), it may be necessary to filter out demos that did + not attain sufficiently high reward in external code. Currently, this + is done in `main.py` and its ignore_this_demo() method. + """ + return (len(self.goals) == 0) or (self._rewards > 0.99) + # return zone_done or defs_done or goal_done + + # ------------------------------------------------------------------------- + # Environment Helper Functions + # ------------------------------------------------------------------------- + + def is_match(self, pose0, pose1, symmetry): + """Check if pose0 and pose1 match within a threshold.""" + + # Get translational error. + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) + dist_pos = np.linalg.norm(diff_pos) + + # Get rotational error around z-axis (account for symmetries). + diff_rot = 0 + if symmetry > 0: + rot0 = np.array(utils.quatXYZW_to_eulerXYZ(pose0[1]))[2] + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + diff_rot = np.abs(rot0 - rot1) % symmetry + if diff_rot > (symmetry / 2): + diff_rot = symmetry - diff_rot + + return (dist_pos < self.pos_eps) and (diff_rot < self.rot_eps) + + def get_random_pose(self, env, obj_size): + """Get random collision-free object pose within workspace bounds.""" + + # Get erosion size of object in pixels. + max_size = np.sqrt(obj_size[0] ** 2 + obj_size[1] ** 2) + erode_size = int(np.round(max_size / self.pix_size)) + + _, hmap, obj_mask = self.get_true_image(env) + + # Randomly sample an object pose within free-space pixels. + free = np.ones(obj_mask.shape, dtype=np.uint8) + for obj_ids in env.obj_ids.values(): + for obj_id in obj_ids: + free[obj_mask == obj_id] = 0 + free[0, :], free[:, 0], free[-1, :], free[:, -1] = 0, 0, 0, 0 + free = cv2.erode(free, np.ones((erode_size, erode_size), np.uint8)) + + # if np.sum(free) == 0: + # return None, None + + if np.sum(free) == 0: + # avoid returning None, None + # return None, None + pix = (obj_mask.shape[0] // 2, obj_mask.shape[1] // 2) + else: + pix = utils.sample_distribution(np.float32(free)) + pos = utils.pix_to_xyz(pix, hmap, self.bounds, self.pix_size) + pos = (pos[0], pos[1], obj_size[2] / 2) + theta = np.random.rand() * 2 * np.pi + rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + return pos, rot + + def get_lang_goal(self): + if len(self.lang_goals) == 0: + return self.task_completed_desc + else: + return self.lang_goals[0] + + def get_reward(self): + return float(self._rewards) + + # ------------------------------------------------------------------------- + # Helper Functions + # ------------------------------------------------------------------------- + + def fill_template(self, template, replace): + """Read a file and replace key strings.""" + full_template_path = os.path.join(self.assets_root, template) + with open(full_template_path, 'r') as file: + fdata = file.read() + for field in replace: + for i in range(len(replace[field])): + fdata = fdata.replace(f'{field}{i}', str(replace[field][i])) + alphabet = string.ascii_lowercase + string.digits + rname = ''.join(random.choices(alphabet, k=16)) + tmpdir = tempfile.gettempdir() + template_filename = os.path.split(template)[-1] + fname = os.path.join(tmpdir, f'{template_filename}.{rname}') + with open(fname, 'w') as file: + file.write(fdata) + return fname + + def get_random_size(self, min_x, max_x, min_y, max_y, min_z, max_z): + """Get random box size.""" + size = np.random.rand(3) + size[0] = size[0] * (max_x - min_x) + min_x + size[1] = size[1] * (max_y - min_y) + min_y + size[2] = size[2] * (max_z - min_z) + min_z + return tuple(size) + + def color_random_brown(self, obj): + shade = np.random.rand() + 0.5 + color = np.float32([shade * 156, shade * 117, shade * 95, 255]) / 255 + p.changeVisualShape(obj, -1, rgbaColor=color) + + """"" + + # Environment Class + def add_object(self, urdf, pose, category='rigid'): + """List of (fixed, rigid, or deformable) objects in env.""" + fixed_base = 1 if category == 'fixed' else 0 + obj_id = pybullet_utils.load_urdf( + p, + os.path.join(self.assets_root, urdf), + pose[0], + pose[1], + useFixedBase=fixed_base) + self.obj_ids[category].append(obj_id) + return obj_id +""" + +========= +Note that the objects need to obey physics and not collide with each other, and the object goal poses need to be above the table with lower bound x=0.25, y=-0.5 and upper bound x=0.75, y=0.5. When there are multiple objects for a multi-step pick-and-place task, there are often multiple subgoals. Once the task and environment are generated, an agent with a pick and place primitive will follow the defined goal to accomplish the tasks. + +Additionally, make sure you understand and summarize the ``self.goals`` variables, which has a list of 8-tuple with (objs, matches, targ_poses, replace, rotations, metric, params, step_max_reward, symmetries). +- objs (List of obj_id): object ID. +- matches (Binary Matrix): a binary matrix that denotes which object is matched with which target. This matrix has dimension len(objs) x len(targs). +- targ_poses (List of Poses [(translation, rotation)] ): a list of target poses of tuple (translation, rotation). +- replace (Boolean): whether each object can match with one unique target. This is important if we have one target and multiple objects. If it's set to be false, then any object matching with the target will satisfy. +- rotations (Boolean): whether the placement action has a rotation degree of freedom. +- metric (`pose` or `zone`): `pose` or `zone` that the object needs to be transported to. Example: `pose`. +- params (List of (zone_target, zone_size)): a list of (zone_target, zone_size) for each zone if the metric is `zone`. +- step_max_reward (float): the total reward of matching all the objects with all the target poses. It is not dependent on the number of objects but dependent on the number of goals. +- symmetries: the radians that the object is symmetric around z axis. +- language_goal: the low-level language instructions that denote the goal of this step. + diff --git a/prompts/topdown_task_generation_prompt/cliport_prompt_code_candidate_template.txt b/prompts/topdown_task_generation_prompt/cliport_prompt_code_candidate_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..52748b02a2e0b466f5c9bc341eda788332b2796a --- /dev/null +++ b/prompts/topdown_task_generation_prompt/cliport_prompt_code_candidate_template.txt @@ -0,0 +1,19 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + +""" +TASK_CODE_REFERENCE_TEMPLATE +""" + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Use `add_goal()` multiple times to give step-by-step language subgoal and placement subgoal for the task. + +Note: +1. Use `make_piles` to create piles of small blocks. +2. Use `make_ropes` to create cables. +3. Us `self.primitive = primitives.push` and `self.ee = Spatula` to use spatula. +4. Do not use random pose or the initial pose for the target poses. Come up with the specified pose. +5. Do not use target poses that are not in the workspace bound `bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]])`. A good starting center point for the table is [0.5,0,0]. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE + + + diff --git a/prompts/topdown_task_generation_prompt/cliport_prompt_code_reference_selection_template.txt b/prompts/topdown_task_generation_prompt/cliport_prompt_code_reference_selection_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..dc3e57f92acf55463dc41c1e53b625fbc90ac441 --- /dev/null +++ b/prompts/topdown_task_generation_prompt/cliport_prompt_code_reference_selection_template.txt @@ -0,0 +1,7 @@ +Now I will provide you some reference code that might help you can write the code for the task "TASK_STRING_TEMPLATE". + + +TASK_CODE_LIST_TEMPLATE + + +Please pick 2 task python files that you would like to use as reference. Format them in a python list. diff --git a/prompts/topdown_task_generation_prompt/cliport_prompt_code_split_template.txt b/prompts/topdown_task_generation_prompt/cliport_prompt_code_split_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf5c20cbf04444d9b7296fadf58bb6f49531b364 --- /dev/null +++ b/prompts/topdown_task_generation_prompt/cliport_prompt_code_split_template.txt @@ -0,0 +1,247 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Use functions `make_piles` and `make_ropes` for creating piles and cables. Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/topdown_task_generation_prompt/cliport_prompt_common_errors_template.txt b/prompts/topdown_task_generation_prompt/cliport_prompt_common_errors_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..098bbead4261558ef07f44ef550e42cbb01ed7e2 --- /dev/null +++ b/prompts/topdown_task_generation_prompt/cliport_prompt_common_errors_template.txt @@ -0,0 +1,102 @@ +Before writing the code for the task "TASK_NAME_TEMPLATE". Here are some runtime errors that you do not want to make. Please confirm that you understand these runtime errors. + +""" +- environment.py, line 338, in info + pos, rot = p.getBasePositionAndOrientation(obj_id) +TypeError: an integer is required (got type NoneType) + +- task.py, line 118, in act + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] +IndexError: list index out of range + +- task.py, line 308, in is_match + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) +TypeError: 'float' object is not subscriptable + +- task.py", line 315, in is_match + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + +- utils.py", line 280, in quatXYZW_to_eulerXYZ + quaternion_wxyz = np.array([q[3], q[0], q[1], q[2]]) +IndexError: tuple index out of range + +- pallet_pose = self.get_random_pose(env, pallet_size) +pallet_surface_height = pallet_pose[0][2] +TypeError: 'NoneType' object is not subscriptable + +- No such file or directory: './cliport/environments/assets/circle/circle-template.urdf' + +- No such file or directory: './cliport/environments/assets/block/block-template.urdf' + +- task.py", line 308, in is_match + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) +IndexError: invalid index to scalar variable. + +-TypeError: get_random_size() missing 4 required positional arguments: 'min_y', 'max_y', 'min_z', and 'max_z' + +- task.py", line 195, in reward + obj_pts, zones = params +TypeError: cannot unpack non-iterable NoneType object + +- environment.py", line 230, in step + reward, info = self.task.reward() if action is not None else (0, {}) + File "task.py", line 200, in reward + pts = obj_pts[obj_id] +IndexError: arrays used as indices must be of integer (or boolean) type + +- generated_task.py", line 41, in reset + utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['light blue'], +KeyError: 'light blue' + +- environment.py", line 195, in reset + self.task.reset(self) + File "", line 38, in reset +TypeError: can only concatenate str (not "list") to str + +- environment.py", line 195, in reset + object_shape = np.random.choice(object_shapes) + in numpy.random.mtrand.RandomState.choice +ValueError: a must be 1-dimensional + +- No such file or directory: 'assets/box-template/box-template.urdf' + +- line 38, in reset.py +{'HALF': box_size / 2} +TypeError: unsupported operand type(s) for /: 'tuple' and 'int'. box_size is a tuple not a float. + +- line 38, in reset.py +IndexError: tuple index out of range +box_pose = (pallet_pose[0], pallet_pose[1], pallet_pose[2] + np.sum(box_sizes[:i+1])) + +- task.py", line 338, in fill_template + for i in range(len(replace[field])): +TypeError: object of type 'float' has no len(). + +- task.py", line 325, in get_random_pose + pos = (pos[0], pos[1], obj_size[2] / 2) +IndexError: tuple index out of range + +- task.py", line 206, in reward + for zone_idx, (zone_pose, zone_size) in enumerate(zones): +TypeError: 'NoneType' object is not iterable + +- task.py", +ball_pose = self.get_random_pose(env, ball_size) +ball_pose[0][2] += 0.02 +TypeError: 'tuple' object does not support item assignment +""" + + +You do not want to make mistakes such as +- using assets (urdfs) that do not exist +- use ambiguous language descriptions as goals. For instance, "place the colored blocks into the matching colored bowls" with one goal and sparse reward as the task instead of adding subgoal "place blue block into blue bowl" and give continuous reward. +- `matches` in the goal has wrong dimensions. It should have the same dimensions as number of objects (N) multiplied by the number of goal poses (M). Usually it is N by M. +- have vector dimension problem such as `np.random.choice(box_size)` or `box_size / 2` where `box_size` is a tuple and not an int +- make too large an object for stacking or make the task objects invisible for picking. +- accessing index out of bound `pallet_pose[2]` for `pallet_pose`. `pallet_pose=get_random_pose` returns a tuple (translation, rotation). It does not have 3rd component. Similarly accessing `container_pose[2]` or `box_pose[2]` would cause errors as well. Since it's a tuple, try to modify it in-place will also trigger errors. +- forget to replace str using `fill_template()` for urdfs with template such as `cylinder-template.urdf`. `ball-template.urdf`, `line-template.urf`. +- use `self.ee = Spatula()` as a function when doing pushing tasks, which is incorrect. It should be `self.ee = Spatula`. +- forget to compute target poses `targ_poses` for matching. Do not use object IDs for poses. +- change colors of complex objects such as `zone`. You can only change color of teomplate primitive such as `cylinder-template`. +- mistakenly use `random_pose` for target pose. Design target poses based on task objectives. +- add only one or fewer language goals which causes language-motion inconsistentcy. Note that the language goals usually are the same number as the pick and place goals. diff --git a/prompts/topdown_task_generation_prompt/cliport_prompt_task.txt b/prompts/topdown_task_generation_prompt/cliport_prompt_task.txt new file mode 100644 index 0000000000000000000000000000000000000000..650c496486b37b147c7a0c29c3fe93ad0afb9cbd --- /dev/null +++ b/prompts/topdown_task_generation_prompt/cliport_prompt_task.txt @@ -0,0 +1,89 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task `TARGET_TASK_NAME`. My goal is to design creative and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Please try to come up with tasks using only these assets. +""" +TASK_ASSET_PROMPT +""" + +There are certain rules on the asset usage. +1. Sweeping piles task must have small blocks `block/small.urdf` and zones `zone.urdf`. Only the piles can be swept in all assets +2. Insertion tasks must have `insertion/ell.urdf` and `insertion/fixture.urdf`. Only the fixture can be inserted in all assets. + + +========= +Here are some examples of good tasks. Try to be creative and high standard, and avoid overlapping with these tasks. + +TASK_DESCRIPTION_PROMPT + + +========= +Here are some bad example task instances with reasons. +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "box/box-template.urdf], +} +reasons: not interesting because it overlaps with the current task `put-block-in-bowl`. + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], +} +reasons: the language descriptions are too ambiguous. Navigation is also hard to complete. Also maze.urf does not exist. + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], +} +reasons: this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere. Similarly you cannot create task like `insert-ball-into-cylinder`. + +{ + "task-name": "ball-box-obstacle-course", + "task-description": "Navigate a ball through an obstacle course created by randomly placed boxes and finally place it inside a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: Navigate the ball is not related to tabletop manipulation tasks. + +{ + "task-name": "ball-in-box", + "task-description": "Use a cable to guide a ball into an open box.", + "assets-used": ["cable/cable.urdf", "ball/ball-template.urdf", "box/box-template.urdf"] +} +reasons: This task is too hard since it involves interaction of the cable and the ball and cannot be easily completed. + +{ + "task-name": "ball-in-container", + "task-description": "Use the spatula to lift a ball over a wall of boxes and drop it into a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: The only action primitives as pick and place. One cannot use a spatula to lift an object. + +{ + "task-name": "line-ball-sorting", + "task-description": "Move balls of different colors along a single green line, placing each ball in a designated colored box at the end of the line. The challenge includes precision in maintaining the ball on the line and the correct identification of the box color corresponding to each ball.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf"] +} +reasons: Piling or stacking balls are physically infeasible in the simulation. + + +{ + "task-name": "sweep-and-stack-blocks", + "task-description": "Sweep a pile of small red and blue blocks into two separate zones marked on the tabletop. Then pick up these blocks in each zone and stack them in two towers according to their colors, with the red tower higher than the blue.", + "assets-used": ["zone/zone.urdf", "block/small.urdf"] +} +reasons: Cannot do sweeping and stacking in the same task. + + +========= +Now let's design the task `TARGET_TASK_NAME`. Please describe the new task `TARGET_TASK_NAME` in natural languages in a clear and detailed way. Format the answer in a python dictionary with keys "task-name" and value type string with lower-case and separated by hyphens, "task-description" (one sentence and do not mention urdf paths) and value type string, and "assets-used" and value type list of strings. + +Note: +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. +- Blocks and boxes are easier to stack than cylinders or balls. Specifically, you can update dimensions of a "box/box-template.urdf". +- Try to use `box-template.urdf` for modifiable blocks instead of `block.urdf'. diff --git a/prompts/topdown_task_generation_prompt_simple/cliport_prompt_code_candidate_template.txt b/prompts/topdown_task_generation_prompt_simple/cliport_prompt_code_candidate_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..52748b02a2e0b466f5c9bc341eda788332b2796a --- /dev/null +++ b/prompts/topdown_task_generation_prompt_simple/cliport_prompt_code_candidate_template.txt @@ -0,0 +1,19 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + +""" +TASK_CODE_REFERENCE_TEMPLATE +""" + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Use `add_goal()` multiple times to give step-by-step language subgoal and placement subgoal for the task. + +Note: +1. Use `make_piles` to create piles of small blocks. +2. Use `make_ropes` to create cables. +3. Us `self.primitive = primitives.push` and `self.ee = Spatula` to use spatula. +4. Do not use random pose or the initial pose for the target poses. Come up with the specified pose. +5. Do not use target poses that are not in the workspace bound `bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]])`. A good starting center point for the table is [0.5,0,0]. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE + + + diff --git a/prompts/topdown_task_generation_prompt_simple/cliport_prompt_code_reference_selection_template.txt b/prompts/topdown_task_generation_prompt_simple/cliport_prompt_code_reference_selection_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..4975ed0ccaabe2e319638af06f1ed0814fcc17e1 --- /dev/null +++ b/prompts/topdown_task_generation_prompt_simple/cliport_prompt_code_reference_selection_template.txt @@ -0,0 +1,7 @@ +Now I will provide you some reference code that might help you can write the code for the task "TASK_STRING_TEMPLATE". + + +TASK_CODE_LIST_TEMPLATE + + +Please pick 3 task python files that you would like to use as reference. Format them in a python list. diff --git a/prompts/topdown_task_generation_prompt_simple/cliport_prompt_code_split_template.txt b/prompts/topdown_task_generation_prompt_simple/cliport_prompt_code_split_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf5c20cbf04444d9b7296fadf58bb6f49531b364 --- /dev/null +++ b/prompts/topdown_task_generation_prompt_simple/cliport_prompt_code_split_template.txt @@ -0,0 +1,247 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Use functions `make_piles` and `make_ropes` for creating piles and cables. Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/topdown_task_generation_prompt_simple/cliport_prompt_task.txt b/prompts/topdown_task_generation_prompt_simple/cliport_prompt_task.txt new file mode 100644 index 0000000000000000000000000000000000000000..650c496486b37b147c7a0c29c3fe93ad0afb9cbd --- /dev/null +++ b/prompts/topdown_task_generation_prompt_simple/cliport_prompt_task.txt @@ -0,0 +1,89 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task `TARGET_TASK_NAME`. My goal is to design creative and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Please try to come up with tasks using only these assets. +""" +TASK_ASSET_PROMPT +""" + +There are certain rules on the asset usage. +1. Sweeping piles task must have small blocks `block/small.urdf` and zones `zone.urdf`. Only the piles can be swept in all assets +2. Insertion tasks must have `insertion/ell.urdf` and `insertion/fixture.urdf`. Only the fixture can be inserted in all assets. + + +========= +Here are some examples of good tasks. Try to be creative and high standard, and avoid overlapping with these tasks. + +TASK_DESCRIPTION_PROMPT + + +========= +Here are some bad example task instances with reasons. +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "box/box-template.urdf], +} +reasons: not interesting because it overlaps with the current task `put-block-in-bowl`. + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], +} +reasons: the language descriptions are too ambiguous. Navigation is also hard to complete. Also maze.urf does not exist. + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], +} +reasons: this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere. Similarly you cannot create task like `insert-ball-into-cylinder`. + +{ + "task-name": "ball-box-obstacle-course", + "task-description": "Navigate a ball through an obstacle course created by randomly placed boxes and finally place it inside a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: Navigate the ball is not related to tabletop manipulation tasks. + +{ + "task-name": "ball-in-box", + "task-description": "Use a cable to guide a ball into an open box.", + "assets-used": ["cable/cable.urdf", "ball/ball-template.urdf", "box/box-template.urdf"] +} +reasons: This task is too hard since it involves interaction of the cable and the ball and cannot be easily completed. + +{ + "task-name": "ball-in-container", + "task-description": "Use the spatula to lift a ball over a wall of boxes and drop it into a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: The only action primitives as pick and place. One cannot use a spatula to lift an object. + +{ + "task-name": "line-ball-sorting", + "task-description": "Move balls of different colors along a single green line, placing each ball in a designated colored box at the end of the line. The challenge includes precision in maintaining the ball on the line and the correct identification of the box color corresponding to each ball.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf"] +} +reasons: Piling or stacking balls are physically infeasible in the simulation. + + +{ + "task-name": "sweep-and-stack-blocks", + "task-description": "Sweep a pile of small red and blue blocks into two separate zones marked on the tabletop. Then pick up these blocks in each zone and stack them in two towers according to their colors, with the red tower higher than the blue.", + "assets-used": ["zone/zone.urdf", "block/small.urdf"] +} +reasons: Cannot do sweeping and stacking in the same task. + + +========= +Now let's design the task `TARGET_TASK_NAME`. Please describe the new task `TARGET_TASK_NAME` in natural languages in a clear and detailed way. Format the answer in a python dictionary with keys "task-name" and value type string with lower-case and separated by hyphens, "task-description" (one sentence and do not mention urdf paths) and value type string, and "assets-used" and value type list of strings. + +Note: +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. +- Blocks and boxes are easier to stack than cylinders or balls. Specifically, you can update dimensions of a "box/box-template.urdf". +- Try to use `box-template.urdf` for modifiable blocks instead of `block.urdf'. diff --git a/prompts/topdown_task_generation_prompt_simple_davinci/cliport_prompt_code_candidate_template.txt b/prompts/topdown_task_generation_prompt_simple_davinci/cliport_prompt_code_candidate_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..52748b02a2e0b466f5c9bc341eda788332b2796a --- /dev/null +++ b/prompts/topdown_task_generation_prompt_simple_davinci/cliport_prompt_code_candidate_template.txt @@ -0,0 +1,19 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + +""" +TASK_CODE_REFERENCE_TEMPLATE +""" + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Use `add_goal()` multiple times to give step-by-step language subgoal and placement subgoal for the task. + +Note: +1. Use `make_piles` to create piles of small blocks. +2. Use `make_ropes` to create cables. +3. Us `self.primitive = primitives.push` and `self.ee = Spatula` to use spatula. +4. Do not use random pose or the initial pose for the target poses. Come up with the specified pose. +5. Do not use target poses that are not in the workspace bound `bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]])`. A good starting center point for the table is [0.5,0,0]. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE + + + diff --git a/prompts/topdown_task_generation_prompt_simple_davinci/cliport_prompt_code_reference_selection_template.txt b/prompts/topdown_task_generation_prompt_simple_davinci/cliport_prompt_code_reference_selection_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..dc3e57f92acf55463dc41c1e53b625fbc90ac441 --- /dev/null +++ b/prompts/topdown_task_generation_prompt_simple_davinci/cliport_prompt_code_reference_selection_template.txt @@ -0,0 +1,7 @@ +Now I will provide you some reference code that might help you can write the code for the task "TASK_STRING_TEMPLATE". + + +TASK_CODE_LIST_TEMPLATE + + +Please pick 2 task python files that you would like to use as reference. Format them in a python list. diff --git a/prompts/topdown_task_generation_prompt_simple_davinci/cliport_prompt_code_split_template.txt b/prompts/topdown_task_generation_prompt_simple_davinci/cliport_prompt_code_split_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..33388797d32b78927f7495dc66357e24b51e2ceb --- /dev/null +++ b/prompts/topdown_task_generation_prompt_simple_davinci/cliport_prompt_code_split_template.txt @@ -0,0 +1,253 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. If you have only one goal, `step_max_reward` in `add_goal` should be 1. Use functions `make_piles` and `make_ropes` for creating piles and cables. To use spatula together with the push primitives, import the libraries +""" +from cliport.tasks import primitives; +from cliport.tasks.grippers import Spatula +""" +and then use `self.primitive = primitives.push` and `self.ee = Spatula`. +Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/topdown_task_generation_prompt_simple_davinci/cliport_prompt_task.txt b/prompts/topdown_task_generation_prompt_simple_davinci/cliport_prompt_task.txt new file mode 100644 index 0000000000000000000000000000000000000000..650c496486b37b147c7a0c29c3fe93ad0afb9cbd --- /dev/null +++ b/prompts/topdown_task_generation_prompt_simple_davinci/cliport_prompt_task.txt @@ -0,0 +1,89 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task `TARGET_TASK_NAME`. My goal is to design creative and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Please try to come up with tasks using only these assets. +""" +TASK_ASSET_PROMPT +""" + +There are certain rules on the asset usage. +1. Sweeping piles task must have small blocks `block/small.urdf` and zones `zone.urdf`. Only the piles can be swept in all assets +2. Insertion tasks must have `insertion/ell.urdf` and `insertion/fixture.urdf`. Only the fixture can be inserted in all assets. + + +========= +Here are some examples of good tasks. Try to be creative and high standard, and avoid overlapping with these tasks. + +TASK_DESCRIPTION_PROMPT + + +========= +Here are some bad example task instances with reasons. +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "box/box-template.urdf], +} +reasons: not interesting because it overlaps with the current task `put-block-in-bowl`. + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], +} +reasons: the language descriptions are too ambiguous. Navigation is also hard to complete. Also maze.urf does not exist. + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], +} +reasons: this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere. Similarly you cannot create task like `insert-ball-into-cylinder`. + +{ + "task-name": "ball-box-obstacle-course", + "task-description": "Navigate a ball through an obstacle course created by randomly placed boxes and finally place it inside a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: Navigate the ball is not related to tabletop manipulation tasks. + +{ + "task-name": "ball-in-box", + "task-description": "Use a cable to guide a ball into an open box.", + "assets-used": ["cable/cable.urdf", "ball/ball-template.urdf", "box/box-template.urdf"] +} +reasons: This task is too hard since it involves interaction of the cable and the ball and cannot be easily completed. + +{ + "task-name": "ball-in-container", + "task-description": "Use the spatula to lift a ball over a wall of boxes and drop it into a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: The only action primitives as pick and place. One cannot use a spatula to lift an object. + +{ + "task-name": "line-ball-sorting", + "task-description": "Move balls of different colors along a single green line, placing each ball in a designated colored box at the end of the line. The challenge includes precision in maintaining the ball on the line and the correct identification of the box color corresponding to each ball.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf"] +} +reasons: Piling or stacking balls are physically infeasible in the simulation. + + +{ + "task-name": "sweep-and-stack-blocks", + "task-description": "Sweep a pile of small red and blue blocks into two separate zones marked on the tabletop. Then pick up these blocks in each zone and stack them in two towers according to their colors, with the red tower higher than the blue.", + "assets-used": ["zone/zone.urdf", "block/small.urdf"] +} +reasons: Cannot do sweeping and stacking in the same task. + + +========= +Now let's design the task `TARGET_TASK_NAME`. Please describe the new task `TARGET_TASK_NAME` in natural languages in a clear and detailed way. Format the answer in a python dictionary with keys "task-name" and value type string with lower-case and separated by hyphens, "task-description" (one sentence and do not mention urdf paths) and value type string, and "assets-used" and value type list of strings. + +Note: +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. +- Blocks and boxes are easier to stack than cylinders or balls. Specifically, you can update dimensions of a "box/box-template.urdf". +- Try to use `box-template.urdf` for modifiable blocks instead of `block.urdf'. diff --git a/prompts/topdown_task_generation_prompt_simple_singleprompt/cliport_prompt_code_split_template.txt b/prompts/topdown_task_generation_prompt_simple_singleprompt/cliport_prompt_code_split_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..33388797d32b78927f7495dc66357e24b51e2ceb --- /dev/null +++ b/prompts/topdown_task_generation_prompt_simple_singleprompt/cliport_prompt_code_split_template.txt @@ -0,0 +1,253 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. If you have only one goal, `step_max_reward` in `add_goal` should be 1. Use functions `make_piles` and `make_ropes` for creating piles and cables. To use spatula together with the push primitives, import the libraries +""" +from cliport.tasks import primitives; +from cliport.tasks.grippers import Spatula +""" +and then use `self.primitive = primitives.push` and `self.ee = Spatula`. +Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/topdown_task_generation_prompt_simple_singleprompt/cliport_prompt_task.txt b/prompts/topdown_task_generation_prompt_simple_singleprompt/cliport_prompt_task.txt new file mode 100644 index 0000000000000000000000000000000000000000..0ae636803276e3d099d19ad64be9a06638d0e29a --- /dev/null +++ b/prompts/topdown_task_generation_prompt_simple_singleprompt/cliport_prompt_task.txt @@ -0,0 +1,220 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task `TARGET_TASK_NAME`. My goal is to design creative and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + + +========= +Here are all the assets. Use only these assets in the task and code design. +""" +insertion/: +ell.urdf fixture.urdf + +bowl/: +bowl.urdf + +box/: +box-template.urdf + +stacking/: +block.urdf stand.urdf + +zone/: +zone.obj zone.urdf + +pallet/: +pallet.obj pallet.urdf + +ball/: +ball-template.urdf + +cylinder/: +cylinder-template.urdf + +bowl/: +bowl.urdf + +# assets not for picking +corner/: +corner-template.urdf + +line/: +single-green-line-template.urdf + +container/: +container-template.urdf +""" + + +There are certain rules on the asset usage. +1. Sweeping piles task must have small blocks `block/small.urdf` and zones `zone.urdf`. Only the piles can be swept in all assets +2. Insertion tasks must have `insertion/ell.urdf` and `insertion/fixture.urdf`. Only the fixture can be inserted in all assets. + + +========= +Here are some examples of good tasks. Try to be creative and high standard, and avoid overlapping with these tasks. + +TASK_DESCRIPTION_PROMPT + + + +========= +Now let's design the task `TARGET_TASK_NAME`. Please describe the new task `TARGET_TASK_NAME` in natural languages in a clear and detailed way. Format the answer in a python dictionary with keys "task-name" and value type string with lower-case and separated by hyphens, "task-description" (one sentence and do not mention urdf paths) and value type string, and "assets-used" and value type list of strings. + +Note: +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. + + +========= +Now I will provide you some reference code and you can write the code for the task. + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick) + + self.lang_goals.append(self.lang_template.format(obj=shapes[obj_shapes[i]])) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1) + self.lang_goals.append(self.lang_template) + + # Colors of distractor objects. + # IMPORTANT: RETRIEVE THE ACTUAL COLOR VALUES + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" + + +========= +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE + + diff --git a/prompts/vanilla_task_generation_prompt/cliport_prompt_api_template.txt b/prompts/vanilla_task_generation_prompt/cliport_prompt_api_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..109662809a0913cb94f00960b2206f2961c82850 --- /dev/null +++ b/prompts/vanilla_task_generation_prompt/cliport_prompt_api_template.txt @@ -0,0 +1,356 @@ +Before writing the code for the task "TASK_NAME_TEMPLATE". Here are some APIs that are defined. Please confirm that you understand these APIs. + +""" +class Task(): + """Base Task class.""" + + def __init__(self): + self.ee = Suction + self.mode = 'train' + self.sixdof = False + self.primitive = primitives.PickPlace() + self.oracle_cams = cameras.Oracle.CONFIG + + # Evaluation epsilons (for pose evaluation metric). + self.pos_eps = 0.01 + self.rot_eps = np.deg2rad(15) + + # Workspace bounds. + self.pix_size = 0.003125 + self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]]) + self.zone_bounds = np.copy(self.bounds) + + self.goals = [] + self.lang_goals = [] + self.task_completed_desc = "task completed." + self.progress = 0 + self._rewards = 0 + self.assets_root = None + + def reset(self, env): + if not self.assets_root: + raise ValueError('assets_root must be set for task, ' + 'call set_assets_root().') + self.goals = [] + self.lang_goals = [] + self.progress = 0 # Task progression metric in range [0, 1]. + self._rewards = 0 # Cumulative returned rewards. + + # ------------------------------------------------------------------------- + # Oracle Agent + # ------------------------------------------------------------------------- + + def oracle(self, env): + """Oracle agent.""" + OracleAgent = collections.namedtuple('OracleAgent', ['act']) + + def act(obs, info): + """Calculate action.""" + + # Oracle uses perfect RGB-D orthographic images and segmentation masks. + _, hmap, obj_mask = self.get_true_image(env) + + # Unpack next goal step. + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] + + # Match objects to targets without replacement. + if not replace: + + # Modify a copy of the match matrix. + matches = matches.copy() + + # Ignore already matched objects. + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + if self.is_match(pose, targs[j], symmetry): + matches[i, :] = 0 + matches[:, j] = 0 + + # Get objects to be picked (prioritize farthest from nearest neighbor). + nn_dists = [] + nn_targets = [] + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + xyz, _ = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + if len(targets_i) > 0: + targets_xyz = np.float32([targs[j][0] for j in targets_i]) + dists = np.linalg.norm( + targets_xyz - np.float32(xyz).reshape(1, 3), axis=1) + nn = np.argmin(dists) + nn_dists.append(dists[nn]) + nn_targets.append(targets_i[nn]) + + # Handle ignored objects. + else: + nn_dists.append(0) + nn_targets.append(-1) + order = np.argsort(nn_dists)[::-1] + + # Filter out matched objects. + order = [i for i in order if nn_dists[i] > 0] + + pick_mask = None + for pick_i in order: + pick_mask = np.uint8(obj_mask == objs[pick_i][0]) + + # Erode to avoid picking on edges. + # pick_mask = cv2.erode(pick_mask, np.ones((3, 3), np.uint8)) + + if np.sum(pick_mask) > 0: + break + + # Trigger task reset if no object is visible. + if pick_mask is None or np.sum(pick_mask) == 0: + self.goals = [] + self.lang_goals = [] + print('Object for pick is not visible. Skipping demonstration.') + return + + # Get picking pose. + pick_prob = np.float32(pick_mask) + pick_pix = utils.sample_distribution(pick_prob) + # For "deterministic" demonstrations on insertion-easy, use this: + # pick_pix = (160,80) + pick_pos = utils.pix_to_xyz(pick_pix, hmap, + self.bounds, self.pix_size) + pick_pose = (np.asarray(pick_pos), np.asarray((0, 0, 0, 1))) + + # Get placing pose. + targ_pose = targs[nn_targets[pick_i]] + obj_pose = p.getBasePositionAndOrientation(objs[pick_i][0]) + if not self.sixdof: + obj_euler = utils.quatXYZW_to_eulerXYZ(obj_pose[1]) + obj_quat = utils.eulerXYZ_to_quatXYZW((0, 0, obj_euler[2])) + obj_pose = (obj_pose[0], obj_quat) + world_to_pick = utils.invert(pick_pose) + obj_to_pick = utils.multiply(world_to_pick, obj_pose) + pick_to_obj = utils.invert(obj_to_pick) + place_pose = utils.multiply(targ_pose, pick_to_obj) + + # Rotate end effector? + if not rotations: + place_pose = (place_pose[0], (0, 0, 0, 1)) + + place_pose = (np.asarray(place_pose[0]), np.asarray(place_pose[1])) + + return {'pose0': pick_pose, 'pose1': place_pose} + + return OracleAgent(act) + + # ------------------------------------------------------------------------- + # Reward Function and Task Completion Metrics + # ------------------------------------------------------------------------- + + def reward(self): + """Get delta rewards for current timestep. + + Returns: + A tuple consisting of the scalar (delta) reward, plus `extras` + dict which has extra task-dependent info from the process of + computing rewards that gives us finer-grained details. Use + `extras` for further data analysis. + """ + reward, info = 0, {} + + # Unpack next goal step. + objs, matches, targs, _, _, metric, params, max_reward = self.goals[0] + + # Evaluate by matching object poses. + if metric == 'pose': + step_reward = 0 + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + target_pose = targs[j] + if self.is_match(pose, target_pose, symmetry): + step_reward += max_reward / len(objs) + print(f"object {i} match with target {j} rew: {step_reward}") + break + + # Evaluate by measuring object intersection with zone. + elif metric == 'zone': + zone_pts, total_pts = 0, 0 + obj_pts, zones = params + for zone_idx, (zone_pose, zone_size) in enumerate(zones): + + # Count valid points in zone. + for obj_idx, obj_id in enumerate(obj_pts): + pts = obj_pts[obj_id] + obj_pose = p.getBasePositionAndOrientation(obj_id) + world_to_zone = utils.invert(zone_pose) + obj_to_zone = utils.multiply(world_to_zone, obj_pose) + pts = np.float32(utils.apply(obj_to_zone, pts)) + if len(zone_size) > 1: + valid_pts = np.logical_and.reduce([ + pts[0, :] > -zone_size[0] / 2, pts[0, :] < zone_size[0] / 2, + pts[1, :] > -zone_size[1] / 2, pts[1, :] < zone_size[1] / 2, + pts[2, :] < self.zone_bounds[2, 1]]) + + # if zone_idx == matches[obj_idx].argmax(): + zone_pts += np.sum(np.float32(valid_pts)) + total_pts += pts.shape[1] + step_reward = max_reward * (zone_pts / total_pts) + + # Get cumulative rewards and return delta. + reward = self.progress + step_reward - self._rewards + self._rewards = self.progress + step_reward + + # Move to next goal step if current goal step is complete. + if np.abs(max_reward - step_reward) < 0.01: + self.progress += max_reward # Update task progress. + self.goals.pop(0) + if len(self.lang_goals) > 0: + self.lang_goals.pop(0) + + return reward, info + + def done(self): + """Check if the task is done or has failed. + + Returns: + True if the episode should be considered a success, which we + use for measuring successes, which is particularly helpful for tasks + where one may get successes on the very last time step, e.g., getting + the cloth coverage threshold on the last alllowed action. + However, for bag-items-easy and bag-items-hard (which use the + 'bag-items' metric), it may be necessary to filter out demos that did + not attain sufficiently high reward in external code. Currently, this + is done in `main.py` and its ignore_this_demo() method. + """ + return (len(self.goals) == 0) or (self._rewards > 0.99) + # return zone_done or defs_done or goal_done + + # ------------------------------------------------------------------------- + # Environment Helper Functions + # ------------------------------------------------------------------------- + + def is_match(self, pose0, pose1, symmetry): + """Check if pose0 and pose1 match within a threshold.""" + + # Get translational error. + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) + dist_pos = np.linalg.norm(diff_pos) + + # Get rotational error around z-axis (account for symmetries). + diff_rot = 0 + if symmetry > 0: + rot0 = np.array(utils.quatXYZW_to_eulerXYZ(pose0[1]))[2] + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + diff_rot = np.abs(rot0 - rot1) % symmetry + if diff_rot > (symmetry / 2): + diff_rot = symmetry - diff_rot + + return (dist_pos < self.pos_eps) and (diff_rot < self.rot_eps) + + def get_random_pose(self, env, obj_size): + """Get random collision-free object pose within workspace bounds.""" + + # Get erosion size of object in pixels. + max_size = np.sqrt(obj_size[0] ** 2 + obj_size[1] ** 2) + erode_size = int(np.round(max_size / self.pix_size)) + + _, hmap, obj_mask = self.get_true_image(env) + + # Randomly sample an object pose within free-space pixels. + free = np.ones(obj_mask.shape, dtype=np.uint8) + for obj_ids in env.obj_ids.values(): + for obj_id in obj_ids: + free[obj_mask == obj_id] = 0 + free[0, :], free[:, 0], free[-1, :], free[:, -1] = 0, 0, 0, 0 + free = cv2.erode(free, np.ones((erode_size, erode_size), np.uint8)) + + # if np.sum(free) == 0: + # return None, None + + if np.sum(free) == 0: + # avoid returning None, None + # return None, None + pix = (obj_mask.shape[0] // 2, obj_mask.shape[1] // 2) + else: + pix = utils.sample_distribution(np.float32(free)) + pos = utils.pix_to_xyz(pix, hmap, self.bounds, self.pix_size) + pos = (pos[0], pos[1], obj_size[2] / 2) + theta = np.random.rand() * 2 * np.pi + rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + return pos, rot + + def get_lang_goal(self): + if len(self.lang_goals) == 0: + return self.task_completed_desc + else: + return self.lang_goals[0] + + def get_reward(self): + return float(self._rewards) + + # ------------------------------------------------------------------------- + # Helper Functions + # ------------------------------------------------------------------------- + + def fill_template(self, template, replace): + """Read a file and replace key strings.""" + full_template_path = os.path.join(self.assets_root, template) + with open(full_template_path, 'r') as file: + fdata = file.read() + for field in replace: + for i in range(len(replace[field])): + fdata = fdata.replace(f'{field}{i}', str(replace[field][i])) + alphabet = string.ascii_lowercase + string.digits + rname = ''.join(random.choices(alphabet, k=16)) + tmpdir = tempfile.gettempdir() + template_filename = os.path.split(template)[-1] + fname = os.path.join(tmpdir, f'{template_filename}.{rname}') + with open(fname, 'w') as file: + file.write(fdata) + return fname + + def get_random_size(self, min_x, max_x, min_y, max_y, min_z, max_z): + """Get random box size.""" + size = np.random.rand(3) + size[0] = size[0] * (max_x - min_x) + min_x + size[1] = size[1] * (max_y - min_y) + min_y + size[2] = size[2] * (max_z - min_z) + min_z + return tuple(size) + + def color_random_brown(self, obj): + shade = np.random.rand() + 0.5 + color = np.float32([shade * 156, shade * 117, shade * 95, 255]) / 255 + p.changeVisualShape(obj, -1, rgbaColor=color) + + """"" + + # Environment Class + def add_object(self, urdf, pose, category='rigid'): + """List of (fixed, rigid, or deformable) objects in env.""" + fixed_base = 1 if category == 'fixed' else 0 + obj_id = pybullet_utils.load_urdf( + p, + os.path.join(self.assets_root, urdf), + pose[0], + pose[1], + useFixedBase=fixed_base) + self.obj_ids[category].append(obj_id) + return obj_id +""" + +========= +Note that the objects need to obey physics and not collide with each other, and the object goal poses need to be above the table with lower bound x=0.25, y=-0.5 and upper bound x=0.75, y=0.5. When there are multiple objects for a multi-step pick-and-place task, there are often multiple subgoals. Once the task and environment are generated, an agent with a pick and place primitive will follow the defined goal to accomplish the tasks. + +Additionally, make sure you understand and summarize the ``self.goals`` variables, which has a list of 8-tuple with (objs, matches, targ_poses, replace, rotations, metric, params, step_max_reward, symmetries). +- objs (List of obj_id): object ID. +- matches (Binary Matrix): a binary matrix that denotes which object is matched with which target. This matrix has dimension len(objs) x len(targs). +- targ_poses (List of Poses [(translation, rotation)] ): a list of target poses of tuple (translation, rotation). +- replace (Boolean): whether each object can match with one unique target. This is important if we have one target and multiple objects. If it's set to be false, then any object matching with the target will satisfy. +- rotations (Boolean): whether the placement action has a rotation degree of freedom. +- metric (`pose` or `zone`): `pose` or `zone` that the object needs to be transported to. Example: `pose`. +- params (List of (zone_target, zone_size)): a list of (zone_target, zone_size) for each zone if the metric is `zone`. +- step_max_reward (float): the total reward of matching all the objects with all the target poses. It is not dependent on the number of objects but dependent on the number of goals. +- symmetries: the radians that the object is symmetric around z axis. +- language_goal: the low-level language instructions that denote the goal of this step. + diff --git a/prompts/vanilla_task_generation_prompt/cliport_prompt_code_split_template.txt b/prompts/vanilla_task_generation_prompt/cliport_prompt_code_split_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..33388797d32b78927f7495dc66357e24b51e2ceb --- /dev/null +++ b/prompts/vanilla_task_generation_prompt/cliport_prompt_code_split_template.txt @@ -0,0 +1,253 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. If you have only one goal, `step_max_reward` in `add_goal` should be 1. Use functions `make_piles` and `make_ropes` for creating piles and cables. To use spatula together with the push primitives, import the libraries +""" +from cliport.tasks import primitives; +from cliport.tasks.grippers import Spatula +""" +and then use `self.primitive = primitives.push` and `self.ee = Spatula`. +Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/vanilla_task_generation_prompt/cliport_prompt_common_errors_template.txt b/prompts/vanilla_task_generation_prompt/cliport_prompt_common_errors_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..098bbead4261558ef07f44ef550e42cbb01ed7e2 --- /dev/null +++ b/prompts/vanilla_task_generation_prompt/cliport_prompt_common_errors_template.txt @@ -0,0 +1,102 @@ +Before writing the code for the task "TASK_NAME_TEMPLATE". Here are some runtime errors that you do not want to make. Please confirm that you understand these runtime errors. + +""" +- environment.py, line 338, in info + pos, rot = p.getBasePositionAndOrientation(obj_id) +TypeError: an integer is required (got type NoneType) + +- task.py, line 118, in act + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] +IndexError: list index out of range + +- task.py, line 308, in is_match + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) +TypeError: 'float' object is not subscriptable + +- task.py", line 315, in is_match + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + +- utils.py", line 280, in quatXYZW_to_eulerXYZ + quaternion_wxyz = np.array([q[3], q[0], q[1], q[2]]) +IndexError: tuple index out of range + +- pallet_pose = self.get_random_pose(env, pallet_size) +pallet_surface_height = pallet_pose[0][2] +TypeError: 'NoneType' object is not subscriptable + +- No such file or directory: './cliport/environments/assets/circle/circle-template.urdf' + +- No such file or directory: './cliport/environments/assets/block/block-template.urdf' + +- task.py", line 308, in is_match + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) +IndexError: invalid index to scalar variable. + +-TypeError: get_random_size() missing 4 required positional arguments: 'min_y', 'max_y', 'min_z', and 'max_z' + +- task.py", line 195, in reward + obj_pts, zones = params +TypeError: cannot unpack non-iterable NoneType object + +- environment.py", line 230, in step + reward, info = self.task.reward() if action is not None else (0, {}) + File "task.py", line 200, in reward + pts = obj_pts[obj_id] +IndexError: arrays used as indices must be of integer (or boolean) type + +- generated_task.py", line 41, in reset + utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['light blue'], +KeyError: 'light blue' + +- environment.py", line 195, in reset + self.task.reset(self) + File "", line 38, in reset +TypeError: can only concatenate str (not "list") to str + +- environment.py", line 195, in reset + object_shape = np.random.choice(object_shapes) + in numpy.random.mtrand.RandomState.choice +ValueError: a must be 1-dimensional + +- No such file or directory: 'assets/box-template/box-template.urdf' + +- line 38, in reset.py +{'HALF': box_size / 2} +TypeError: unsupported operand type(s) for /: 'tuple' and 'int'. box_size is a tuple not a float. + +- line 38, in reset.py +IndexError: tuple index out of range +box_pose = (pallet_pose[0], pallet_pose[1], pallet_pose[2] + np.sum(box_sizes[:i+1])) + +- task.py", line 338, in fill_template + for i in range(len(replace[field])): +TypeError: object of type 'float' has no len(). + +- task.py", line 325, in get_random_pose + pos = (pos[0], pos[1], obj_size[2] / 2) +IndexError: tuple index out of range + +- task.py", line 206, in reward + for zone_idx, (zone_pose, zone_size) in enumerate(zones): +TypeError: 'NoneType' object is not iterable + +- task.py", +ball_pose = self.get_random_pose(env, ball_size) +ball_pose[0][2] += 0.02 +TypeError: 'tuple' object does not support item assignment +""" + + +You do not want to make mistakes such as +- using assets (urdfs) that do not exist +- use ambiguous language descriptions as goals. For instance, "place the colored blocks into the matching colored bowls" with one goal and sparse reward as the task instead of adding subgoal "place blue block into blue bowl" and give continuous reward. +- `matches` in the goal has wrong dimensions. It should have the same dimensions as number of objects (N) multiplied by the number of goal poses (M). Usually it is N by M. +- have vector dimension problem such as `np.random.choice(box_size)` or `box_size / 2` where `box_size` is a tuple and not an int +- make too large an object for stacking or make the task objects invisible for picking. +- accessing index out of bound `pallet_pose[2]` for `pallet_pose`. `pallet_pose=get_random_pose` returns a tuple (translation, rotation). It does not have 3rd component. Similarly accessing `container_pose[2]` or `box_pose[2]` would cause errors as well. Since it's a tuple, try to modify it in-place will also trigger errors. +- forget to replace str using `fill_template()` for urdfs with template such as `cylinder-template.urdf`. `ball-template.urdf`, `line-template.urf`. +- use `self.ee = Spatula()` as a function when doing pushing tasks, which is incorrect. It should be `self.ee = Spatula`. +- forget to compute target poses `targ_poses` for matching. Do not use object IDs for poses. +- change colors of complex objects such as `zone`. You can only change color of teomplate primitive such as `cylinder-template`. +- mistakenly use `random_pose` for target pose. Design target poses based on task objectives. +- add only one or fewer language goals which causes language-motion inconsistentcy. Note that the language goals usually are the same number as the pick and place goals. diff --git a/prompts/vanilla_task_generation_prompt/cliport_prompt_task.txt b/prompts/vanilla_task_generation_prompt/cliport_prompt_task.txt new file mode 100644 index 0000000000000000000000000000000000000000..df002cf17db03983beb230a96f0790cdea789dd6 --- /dev/null +++ b/prompts/vanilla_task_generation_prompt/cliport_prompt_task.txt @@ -0,0 +1,154 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task. My goal is to design diverse and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Use only these assets in the task and code design. +""" +insertion/: +ell.urdf fixture.urdf + +bowl/: +bowl.urdf + +box/: +box-template.urdf + +stacking/: +block.urdf stand.urdf + +zone/: +zone.obj zone.urdf + +pallet/: +pallet.obj pallet.urdf + +ball/: +ball-template.urdf + +cylinder/: +cylinder-template.urdf + +bowl/: +bowl.urdf + +# assets not for picking +corner/: +corner-template.urdf + +line/: +single-green-line-template.urdf + +container/: +container-template.urdf +""" + +========= +Here are some examples of good tasks. Try to learn from these structures but avoid overlapping wiht them. + +{"assets-used": ["zone/zone.urdf", "block/small.urdf"], + "task-description": "Push piles of small objects into a target goal zone marked on the tabletop.", + "task-name": "sweeping-piles"} + +{"assets-used": ["bowl/bowl.urdf", "stacking/block.urdf"], + "task-description": "Place all blocks of a specified color in a bowl of specified color.", + "task-name": "put-block-in-bowl"} + +{"assets-used": ["insertion/ell.urdf", "insertion/fixture.urdf"], + "task-description": "pick up the L-shaped red block and place it into the L-shaped fixture.", + "task-name": "block-insertion"} + +{"assets-used": ["kitting/kit.urdf", "kitting/object-template.urdf"], + "task-description": "pick up different objects and arrange them on a board marked with corresponding silhouettes.", + "task-name": "assembling-kits"} + +{"assets-used": ["pallet/pallet.urdf", "box/box-template.urdf"], + "task-description": "pick up homogeneous fixed-sized boxes and stack them in transposed layers on the pallet.", + "task-name": "palletizing-boxes"} + +{"assets-used": ["stacking/stand.urdf", "stacking/block.urdf"], + "task-description": "sequentially stack 6 blocks into a pyramid of 3-2-1 with rainbow colored ordering.", + "task-name": "stack-block-pyramid"} + +{"assets-used": ["container/container-template.urdf", "box/box-template.urdf"], + "task-description": "pick up randomly sized boxes and place them tightly into a container.", + "task-name": "packing-boxes"} + +{"assets-used": ["bowl/bowl.urdf", "stacking/block.urdf"], + "task-description": "pick up the red blocks and place them into the green bowls amidst other objects.", + "task-name": "place-red-in-green"} + +{"assets-used": ["box/box-template.urdf", "corner/corner-template.urdf"], + "task-description": "pick up the randomly sized box and align one of its corners to the L-shaped marker on the tabletop..", + "task-name": "align-box-corner"} + +{"assets-used": ["box/box-template.urdf", "corner/corner-template.urdf"], + "task-description": "pick up the randomly sized box and align one of its corners to the L-shaped marker on the tabletop..", + "task-name": "align-box-corner"} + +========= +Here are some tasks that you have come up with before. Try to learn from these structures but avoid overlapping with these tasks. For instance, `bowl_ball_placement` and `sort_balls_in_bowls` are the same task. `pile_boxes_in_corner` and `stack_blocks_into_pallet` are similar tasks, `align-cylinder-in-corner` and `align-cylinder-corner` are similar. +PAST_TASKNAME_TEMPLATE + + +========= +Here are some bad example task instances with explanations. +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "box/box-template.urdf], +} +reasons: not interesting because it overlaps with the current task `put-block-in-bowl`. + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], +} +reasons: the language descriptions are too ambiguous. Navigation is also hard to complete. Also maze.urf does not exist. + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], +} +reasons: this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere. Similarly tasks like `insert-ball-into-cylinder` and `cylinder-box-insertion` are invalid. + +{ + "task-name": "ball-box-obstacle-course", + "task-description": "Navigate a ball through an obstacle course created by randomly placed boxes and finally place it inside a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: Navigate the ball is not related to tabletop manipulation tasks. + +{ + "task-name": "ball-in-box", + "task-description": "Use a cable to guide a ball into an open box.", + "assets-used": ["cable/cable.urdf", "ball/ball-template.urdf", "box/box-template.urdf"] +} +reasons: This task is too hard since it involves interaction of the cable and the ball and cannot be easily completed. + +{ + "task-name": "ball-in-container", + "task-description": "Use the spatula to lift a ball over a wall of boxes and drop it into a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: The only action primitives as pick and place. One cannot use a spatula to lift an object. + +{ + "task-name": "line-ball-sorting", + "task-description": "Move balls of different colors along a single green line, placing each ball in a designated colored box at the end of the line. The challenge includes precision in maintaining the ball on the line and the correct identification of the box color corresponding to each ball.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf"] +} +reasons: Piling or stacking balls are physically infeasible in the simulation. + + + +========= +Now please describe the new task in natural languages and explain its novelty and challenges. Format the answer in a python dictionary with keys "task-name" and value type string, "task-description" (one specific sentence) and value type string, and "assets-used" and value type list of strings. Note that + +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. + + + diff --git a/prompts/vanilla_task_generation_prompt/cliport_prompt_task_reflection.txt b/prompts/vanilla_task_generation_prompt/cliport_prompt_task_reflection.txt new file mode 100644 index 0000000000000000000000000000000000000000..49de7f356ed859ca85f292d794a24363a9a429e1 --- /dev/null +++ b/prompts/vanilla_task_generation_prompt/cliport_prompt_task_reflection.txt @@ -0,0 +1,76 @@ + + +Do you think your task is sufficently interesting to be added as a new task for future task given that we already have the following task name and descriptions? Moreover, does the simulation code achieve the goal and the language descriptions in the task? Be as rigorous and high-standard as possible. + +Reminder: +your task: +TASK_STRING_TEMPLATE + +TASK_CODE_TEMPLATE + +current task list: +CURRENT_TASK_NAME_TEMPLATE + +========= +Reply explain your reasons and then say True or False, formatted in a python dictionary, do not miss commas or add extra spaces. Here are some examples. + +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "yellow-bowl.urdf", "box/box-template.urdf], + "reasons": "not interesting because it overlaps with the current task `put-block-in-bowl`" + "add_to_the_task_list": "False", +} + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], + "reasons": "the language descriptions are too ambiguous. Navigation is also hard to complete." + "add_to_the_task_list": "False", +} + +{ + "task-name": "sort-blocks-by-zone", + "task-description": "Sort different colored blocks into their corresponding colored zones marked on the tabletop.", + "assets-used": ["zone/zone.urdf", "stacking/block.urdf"], + "reasons": "the task is not covered in the current list and is an interesting combination of zone and block objects." + "add_to_the_task_list": "True", +} + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], + "reasons": "this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere.", + "add_to_the_task_list": "False", +} + +{ + "task-name": "arrange-blocks-on-pallet", + "task-description": "arrange the blocks on the pallet in the order: red, orange, yellow, green, blue, and purple.", + "assets-used": ["stacking/block.urdf", "pallet/pallelt.urdf"], + "reasons": "this task overlaps with `arrange-boxes-on-pallet` by merely changing the blocks to boxes.", + "add_to_the_task_list": "False", +} + +{ + "task-name": "cylinder-color-alignment", + "task-description": "Pick up cylinders of different colors and align them inside a box in the order of the colors displayed on the single green line on the tabletop.", + "assets-used": ["cylinder/cylinder-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf + "reasons": "the execution code only has one goal, i.e. it will only move one cylinder on top of the box, which is not that interesting.", + "add_to_the_task_list": "False","] +} + +{ + "task-name": "build-bridge", + "task-description": "Construct a bridge using two yellow blocks and three blue blocks. Firstly, place the two yellow blocks on each of the two bases parallel to each other with a fair amount of space in between. Then, place the blue block horizontally on top of the yellow blocks.", + "assets-used": ["block/block.urdf", "ball/ball-template.urdf"] + "reasons": "this task is an interesting and long-horizon task for pick and place primitives. Training manipulation agent with this skill will be a useful building block for next skills.", + "add_to_the_task_list": "True", +} + +========= + +Please incorporate these feedbacks when you design new task! + diff --git a/prompts/vanilla_task_generation_prompt_simple/cliport_prompt_code_split_template.txt b/prompts/vanilla_task_generation_prompt_simple/cliport_prompt_code_split_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..33388797d32b78927f7495dc66357e24b51e2ceb --- /dev/null +++ b/prompts/vanilla_task_generation_prompt_simple/cliport_prompt_code_split_template.txt @@ -0,0 +1,253 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. If you have only one goal, `step_max_reward` in `add_goal` should be 1. Use functions `make_piles` and `make_ropes` for creating piles and cables. To use spatula together with the push primitives, import the libraries +""" +from cliport.tasks import primitives; +from cliport.tasks.grippers import Spatula +""" +and then use `self.primitive = primitives.push` and `self.ee = Spatula`. +Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/vanilla_task_generation_prompt_simple/cliport_prompt_task.txt b/prompts/vanilla_task_generation_prompt_simple/cliport_prompt_task.txt new file mode 100644 index 0000000000000000000000000000000000000000..df002cf17db03983beb230a96f0790cdea789dd6 --- /dev/null +++ b/prompts/vanilla_task_generation_prompt_simple/cliport_prompt_task.txt @@ -0,0 +1,154 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task. My goal is to design diverse and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Use only these assets in the task and code design. +""" +insertion/: +ell.urdf fixture.urdf + +bowl/: +bowl.urdf + +box/: +box-template.urdf + +stacking/: +block.urdf stand.urdf + +zone/: +zone.obj zone.urdf + +pallet/: +pallet.obj pallet.urdf + +ball/: +ball-template.urdf + +cylinder/: +cylinder-template.urdf + +bowl/: +bowl.urdf + +# assets not for picking +corner/: +corner-template.urdf + +line/: +single-green-line-template.urdf + +container/: +container-template.urdf +""" + +========= +Here are some examples of good tasks. Try to learn from these structures but avoid overlapping wiht them. + +{"assets-used": ["zone/zone.urdf", "block/small.urdf"], + "task-description": "Push piles of small objects into a target goal zone marked on the tabletop.", + "task-name": "sweeping-piles"} + +{"assets-used": ["bowl/bowl.urdf", "stacking/block.urdf"], + "task-description": "Place all blocks of a specified color in a bowl of specified color.", + "task-name": "put-block-in-bowl"} + +{"assets-used": ["insertion/ell.urdf", "insertion/fixture.urdf"], + "task-description": "pick up the L-shaped red block and place it into the L-shaped fixture.", + "task-name": "block-insertion"} + +{"assets-used": ["kitting/kit.urdf", "kitting/object-template.urdf"], + "task-description": "pick up different objects and arrange them on a board marked with corresponding silhouettes.", + "task-name": "assembling-kits"} + +{"assets-used": ["pallet/pallet.urdf", "box/box-template.urdf"], + "task-description": "pick up homogeneous fixed-sized boxes and stack them in transposed layers on the pallet.", + "task-name": "palletizing-boxes"} + +{"assets-used": ["stacking/stand.urdf", "stacking/block.urdf"], + "task-description": "sequentially stack 6 blocks into a pyramid of 3-2-1 with rainbow colored ordering.", + "task-name": "stack-block-pyramid"} + +{"assets-used": ["container/container-template.urdf", "box/box-template.urdf"], + "task-description": "pick up randomly sized boxes and place them tightly into a container.", + "task-name": "packing-boxes"} + +{"assets-used": ["bowl/bowl.urdf", "stacking/block.urdf"], + "task-description": "pick up the red blocks and place them into the green bowls amidst other objects.", + "task-name": "place-red-in-green"} + +{"assets-used": ["box/box-template.urdf", "corner/corner-template.urdf"], + "task-description": "pick up the randomly sized box and align one of its corners to the L-shaped marker on the tabletop..", + "task-name": "align-box-corner"} + +{"assets-used": ["box/box-template.urdf", "corner/corner-template.urdf"], + "task-description": "pick up the randomly sized box and align one of its corners to the L-shaped marker on the tabletop..", + "task-name": "align-box-corner"} + +========= +Here are some tasks that you have come up with before. Try to learn from these structures but avoid overlapping with these tasks. For instance, `bowl_ball_placement` and `sort_balls_in_bowls` are the same task. `pile_boxes_in_corner` and `stack_blocks_into_pallet` are similar tasks, `align-cylinder-in-corner` and `align-cylinder-corner` are similar. +PAST_TASKNAME_TEMPLATE + + +========= +Here are some bad example task instances with explanations. +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "box/box-template.urdf], +} +reasons: not interesting because it overlaps with the current task `put-block-in-bowl`. + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], +} +reasons: the language descriptions are too ambiguous. Navigation is also hard to complete. Also maze.urf does not exist. + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], +} +reasons: this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere. Similarly tasks like `insert-ball-into-cylinder` and `cylinder-box-insertion` are invalid. + +{ + "task-name": "ball-box-obstacle-course", + "task-description": "Navigate a ball through an obstacle course created by randomly placed boxes and finally place it inside a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: Navigate the ball is not related to tabletop manipulation tasks. + +{ + "task-name": "ball-in-box", + "task-description": "Use a cable to guide a ball into an open box.", + "assets-used": ["cable/cable.urdf", "ball/ball-template.urdf", "box/box-template.urdf"] +} +reasons: This task is too hard since it involves interaction of the cable and the ball and cannot be easily completed. + +{ + "task-name": "ball-in-container", + "task-description": "Use the spatula to lift a ball over a wall of boxes and drop it into a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: The only action primitives as pick and place. One cannot use a spatula to lift an object. + +{ + "task-name": "line-ball-sorting", + "task-description": "Move balls of different colors along a single green line, placing each ball in a designated colored box at the end of the line. The challenge includes precision in maintaining the ball on the line and the correct identification of the box color corresponding to each ball.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf"] +} +reasons: Piling or stacking balls are physically infeasible in the simulation. + + + +========= +Now please describe the new task in natural languages and explain its novelty and challenges. Format the answer in a python dictionary with keys "task-name" and value type string, "task-description" (one specific sentence) and value type string, and "assets-used" and value type list of strings. Note that + +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. + + + diff --git a/prompts/vanilla_task_generation_prompt_simple_api/cliport_prompt_api_template.txt b/prompts/vanilla_task_generation_prompt_simple_api/cliport_prompt_api_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..9a2c4d8d1a32af3aa7affb1c8c773fa93fd36383 --- /dev/null +++ b/prompts/vanilla_task_generation_prompt_simple_api/cliport_prompt_api_template.txt @@ -0,0 +1,350 @@ +Before writing the code for the task "TASK_NAME_TEMPLATE". Here are some APIs that are defined. Please confirm that you understand these APIs. + +""" +class Task(): + """Base Task class.""" + + def __init__(self): + self.ee = Suction + self.mode = 'train' + self.sixdof = False + self.primitive = primitives.PickPlace() + self.oracle_cams = cameras.Oracle.CONFIG + + # Evaluation epsilons (for pose evaluation metric). + self.pos_eps = 0.01 + self.rot_eps = np.deg2rad(15) + + # Workspace bounds. + self.pix_size = 0.003125 + self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]]) + self.zone_bounds = np.copy(self.bounds) + + self.goals = [] + self.lang_goals = [] + self.task_completed_desc = "task completed." + self.progress = 0 + self._rewards = 0 + self.assets_root = None + + def reset(self, env): + if not self.assets_root: + raise ValueError('assets_root must be set for task, ' + 'call set_assets_root().') + self.goals = [] + self.lang_goals = [] + self.progress = 0 # Task progression metric in range [0, 1]. + self._rewards = 0 # Cumulative returned rewards. + + # ------------------------------------------------------------------------- + # Oracle Agent + # ------------------------------------------------------------------------- + + def oracle(self, env): + """Oracle agent.""" + OracleAgent = collections.namedtuple('OracleAgent', ['act']) + + def act(obs, info): + """Calculate action.""" + + # Oracle uses perfect RGB-D orthographic images and segmentation masks. + _, hmap, obj_mask = self.get_true_image(env) + + # Unpack next goal step. + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] + + # Match objects to targets without replacement. + if not replace: + + # Modify a copy of the match matrix. + matches = matches.copy() + + # Ignore already matched objects. + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + if self.is_match(pose, targs[j], symmetry): + matches[i, :] = 0 + matches[:, j] = 0 + + # Get objects to be picked (prioritize farthest from nearest neighbor). + nn_dists = [] + nn_targets = [] + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + xyz, _ = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + if len(targets_i) > 0: + targets_xyz = np.float32([targs[j][0] for j in targets_i]) + dists = np.linalg.norm( + targets_xyz - np.float32(xyz).reshape(1, 3), axis=1) + nn = np.argmin(dists) + nn_dists.append(dists[nn]) + nn_targets.append(targets_i[nn]) + + # Handle ignored objects. + else: + nn_dists.append(0) + nn_targets.append(-1) + order = np.argsort(nn_dists)[::-1] + + # Filter out matched objects. + order = [i for i in order if nn_dists[i] > 0] + + pick_mask = None + for pick_i in order: + pick_mask = np.uint8(obj_mask == objs[pick_i][0]) + + # Erode to avoid picking on edges. + # pick_mask = cv2.erode(pick_mask, np.ones((3, 3), np.uint8)) + + if np.sum(pick_mask) > 0: + break + + # Trigger task reset if no object is visible. + if pick_mask is None or np.sum(pick_mask) == 0: + self.goals = [] + self.lang_goals = [] + print('Object for pick is not visible. Skipping demonstration.') + return + + # Get picking pose. + pick_prob = np.float32(pick_mask) + pick_pix = utils.sample_distribution(pick_prob) + # For "deterministic" demonstrations on insertion-easy, use this: + # pick_pix = (160,80) + pick_pos = utils.pix_to_xyz(pick_pix, hmap, + self.bounds, self.pix_size) + pick_pose = (np.asarray(pick_pos), np.asarray((0, 0, 0, 1))) + + # Get placing pose. + targ_pose = targs[nn_targets[pick_i]] + obj_pose = p.getBasePositionAndOrientation(objs[pick_i][0]) + if not self.sixdof: + obj_euler = utils.quatXYZW_to_eulerXYZ(obj_pose[1]) + obj_quat = utils.eulerXYZ_to_quatXYZW((0, 0, obj_euler[2])) + obj_pose = (obj_pose[0], obj_quat) + world_to_pick = utils.invert(pick_pose) + obj_to_pick = utils.multiply(world_to_pick, obj_pose) + pick_to_obj = utils.invert(obj_to_pick) + place_pose = utils.multiply(targ_pose, pick_to_obj) + + # Rotate end effector? + if not rotations: + place_pose = (place_pose[0], (0, 0, 0, 1)) + + place_pose = (np.asarray(place_pose[0]), np.asarray(place_pose[1])) + + return {'pose0': pick_pose, 'pose1': place_pose} + + return OracleAgent(act) + + # ------------------------------------------------------------------------- + # Reward Function and Task Completion Metrics + # ------------------------------------------------------------------------- + + def reward(self): + """Get delta rewards for current timestep. + + Returns: + A tuple consisting of the scalar (delta) reward, plus `extras` + dict which has extra task-dependent info from the process of + computing rewards that gives us finer-grained details. Use + `extras` for further data analysis. + """ + reward, info = 0, {} + + # Unpack next goal step. + objs, matches, targs, _, _, metric, params, max_reward = self.goals[0] + + # Evaluate by matching object poses. + if metric == 'pose': + step_reward = 0 + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + target_pose = targs[j] + if self.is_match(pose, target_pose, symmetry): + step_reward += max_reward / len(objs) + print(f"object {i} match with target {j} rew: {step_reward}") + break + + # Evaluate by measuring object intersection with zone. + elif metric == 'zone': + zone_pts, total_pts = 0, 0 + obj_pts, zones = params + for zone_idx, (zone_pose, zone_size) in enumerate(zones): + + # Count valid points in zone. + for obj_idx, obj_id in enumerate(obj_pts): + pts = obj_pts[obj_id] + obj_pose = p.getBasePositionAndOrientation(obj_id) + world_to_zone = utils.invert(zone_pose) + obj_to_zone = utils.multiply(world_to_zone, obj_pose) + pts = np.float32(utils.apply(obj_to_zone, pts)) + if len(zone_size) > 1: + valid_pts = np.logical_and.reduce([ + pts[0, :] > -zone_size[0] / 2, pts[0, :] < zone_size[0] / 2, + pts[1, :] > -zone_size[1] / 2, pts[1, :] < zone_size[1] / 2, + pts[2, :] < self.zone_bounds[2, 1]]) + + # if zone_idx == matches[obj_idx].argmax(): + zone_pts += np.sum(np.float32(valid_pts)) + total_pts += pts.shape[1] + step_reward = max_reward * (zone_pts / total_pts) + + # Get cumulative rewards and return delta. + reward = self.progress + step_reward - self._rewards + self._rewards = self.progress + step_reward + + # Move to next goal step if current goal step is complete. + if np.abs(max_reward - step_reward) < 0.01: + self.progress += max_reward # Update task progress. + self.goals.pop(0) + if len(self.lang_goals) > 0: + self.lang_goals.pop(0) + + return reward, info + + def done(self): + """Check if the task is done or has failed. + + Returns: + True if the episode should be considered a success, which we + use for measuring successes, which is particularly helpful for tasks + where one may get successes on the very last time step, e.g., getting + the cloth coverage threshold on the last alllowed action. + However, for bag-items-easy and bag-items-hard (which use the + 'bag-items' metric), it may be necessary to filter out demos that did + not attain sufficiently high reward in external code. Currently, this + is done in `main.py` and its ignore_this_demo() method. + """ + return (len(self.goals) == 0) or (self._rewards > 0.99) + # return zone_done or defs_done or goal_done + + # ------------------------------------------------------------------------- + # Environment Helper Functions + # ------------------------------------------------------------------------- + + def is_match(self, pose0, pose1, symmetry): + """Check if pose0 and pose1 match within a threshold.""" + + # Get translational error. + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) + dist_pos = np.linalg.norm(diff_pos) + + # Get rotational error around z-axis (account for symmetries). + diff_rot = 0 + if symmetry > 0: + rot0 = np.array(utils.quatXYZW_to_eulerXYZ(pose0[1]))[2] + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + diff_rot = np.abs(rot0 - rot1) % symmetry + if diff_rot > (symmetry / 2): + diff_rot = symmetry - diff_rot + + return (dist_pos < self.pos_eps) and (diff_rot < self.rot_eps) + + def get_random_pose(self, env, obj_size): + """Get random collision-free object pose within workspace bounds.""" + + # Get erosion size of object in pixels. + max_size = np.sqrt(obj_size[0] ** 2 + obj_size[1] ** 2) + erode_size = int(np.round(max_size / self.pix_size)) + + _, hmap, obj_mask = self.get_true_image(env) + + # Randomly sample an object pose within free-space pixels. + free = np.ones(obj_mask.shape, dtype=np.uint8) + for obj_ids in env.obj_ids.values(): + for obj_id in obj_ids: + free[obj_mask == obj_id] = 0 + free[0, :], free[:, 0], free[-1, :], free[:, -1] = 0, 0, 0, 0 + free = cv2.erode(free, np.ones((erode_size, erode_size), np.uint8)) + + # if np.sum(free) == 0: + # return None, None + + if np.sum(free) == 0: + # avoid returning None, None + # return None, None + pix = (obj_mask.shape[0] // 2, obj_mask.shape[1] // 2) + else: + pix = utils.sample_distribution(np.float32(free)) + pos = utils.pix_to_xyz(pix, hmap, self.bounds, self.pix_size) + pos = (pos[0], pos[1], obj_size[2] / 2) + theta = np.random.rand() * 2 * np.pi + rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + return pos, rot + + def get_lang_goal(self): + if len(self.lang_goals) == 0: + return self.task_completed_desc + else: + return self.lang_goals[0] + + def get_reward(self): + return float(self._rewards) + + # ------------------------------------------------------------------------- + # Helper Functions + # ------------------------------------------------------------------------- + + def fill_template(self, template, replace): + """Read a file and replace key strings.""" + full_template_path = os.path.join(self.assets_root, template) + with open(full_template_path, 'r') as file: + fdata = file.read() + for field in replace: + for i in range(len(replace[field])): + fdata = fdata.replace(f'{field}{i}', str(replace[field][i])) + alphabet = string.ascii_lowercase + string.digits + rname = ''.join(random.choices(alphabet, k=16)) + tmpdir = tempfile.gettempdir() + template_filename = os.path.split(template)[-1] + fname = os.path.join(tmpdir, f'{template_filename}.{rname}') + with open(fname, 'w') as file: + file.write(fdata) + return fname + + def get_random_size(self, min_x, max_x, min_y, max_y, min_z, max_z): + """Get random box size.""" + size = np.random.rand(3) + size[0] = size[0] * (max_x - min_x) + min_x + size[1] = size[1] * (max_y - min_y) + min_y + size[2] = size[2] * (max_z - min_z) + min_z + return tuple(size) + + """"" + + # Environment Class + def add_object(self, urdf, pose, category='rigid'): + """List of (fixed, rigid, or deformable) objects in env.""" + fixed_base = 1 if category == 'fixed' else 0 + obj_id = pybullet_utils.load_urdf( + p, + os.path.join(self.assets_root, urdf), + pose[0], + pose[1], + useFixedBase=fixed_base) + self.obj_ids[category].append(obj_id) + return obj_id +""" + +========= +Note that the objects need to obey physics and not collide with each other, and the object goal poses need to be above the table with lower bound x=0.25, y=-0.5 and upper bound x=0.75, y=0.5. When there are multiple objects for a multi-step pick-and-place task, there are often multiple subgoals. Once the task and environment are generated, an agent with a pick and place primitive will follow the defined goal to accomplish the tasks. + +Additionally, make sure you understand and summarize the ``self.goals`` variables, which has a list of 8-tuple with (objs, matches, targ_poses, replace, rotations, metric, params, step_max_reward, symmetries). +- objs (List of obj_id): object ID. +- matches (Binary Matrix): a binary matrix that denotes which object is matched with which target. This matrix has dimension len(objs) x len(targs). +- targ_poses (List of Poses [(translation, rotation)] ): a list of target poses of tuple (translation, rotation). Don't pass in object IDs such as `bowls[i-1][0]` or `[stands[i][0]]`. +- replace (Boolean): whether each object can match with one unique target. This is important if we have one target and multiple objects. If it's set to be false, then any object matching with the target will satisfy. +- rotations (Boolean): whether the placement action has a rotation degree of freedom. +- metric (`pose` or `zone`): `pose` or `zone` that the object needs to be transported to. Example: `pose`. +- params (List of (zone_target, zone_size)): a list of (zone_target, zone_size) for each zone if the metric is `zone`. +- step_max_reward (float): subgoal reward threshold. +- symmetries: the radians that the object is symmetric around z axis. + diff --git a/prompts/vanilla_task_generation_prompt_simple_api/cliport_prompt_code_split_template.txt b/prompts/vanilla_task_generation_prompt_simple_api/cliport_prompt_code_split_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..33388797d32b78927f7495dc66357e24b51e2ceb --- /dev/null +++ b/prompts/vanilla_task_generation_prompt_simple_api/cliport_prompt_code_split_template.txt @@ -0,0 +1,253 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. If you have only one goal, `step_max_reward` in `add_goal` should be 1. Use functions `make_piles` and `make_ropes` for creating piles and cables. To use spatula together with the push primitives, import the libraries +""" +from cliport.tasks import primitives; +from cliport.tasks.grippers import Spatula +""" +and then use `self.primitive = primitives.push` and `self.ee = Spatula`. +Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/vanilla_task_generation_prompt_simple_api/cliport_prompt_task.txt b/prompts/vanilla_task_generation_prompt_simple_api/cliport_prompt_task.txt new file mode 100644 index 0000000000000000000000000000000000000000..df002cf17db03983beb230a96f0790cdea789dd6 --- /dev/null +++ b/prompts/vanilla_task_generation_prompt_simple_api/cliport_prompt_task.txt @@ -0,0 +1,154 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task. My goal is to design diverse and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Use only these assets in the task and code design. +""" +insertion/: +ell.urdf fixture.urdf + +bowl/: +bowl.urdf + +box/: +box-template.urdf + +stacking/: +block.urdf stand.urdf + +zone/: +zone.obj zone.urdf + +pallet/: +pallet.obj pallet.urdf + +ball/: +ball-template.urdf + +cylinder/: +cylinder-template.urdf + +bowl/: +bowl.urdf + +# assets not for picking +corner/: +corner-template.urdf + +line/: +single-green-line-template.urdf + +container/: +container-template.urdf +""" + +========= +Here are some examples of good tasks. Try to learn from these structures but avoid overlapping wiht them. + +{"assets-used": ["zone/zone.urdf", "block/small.urdf"], + "task-description": "Push piles of small objects into a target goal zone marked on the tabletop.", + "task-name": "sweeping-piles"} + +{"assets-used": ["bowl/bowl.urdf", "stacking/block.urdf"], + "task-description": "Place all blocks of a specified color in a bowl of specified color.", + "task-name": "put-block-in-bowl"} + +{"assets-used": ["insertion/ell.urdf", "insertion/fixture.urdf"], + "task-description": "pick up the L-shaped red block and place it into the L-shaped fixture.", + "task-name": "block-insertion"} + +{"assets-used": ["kitting/kit.urdf", "kitting/object-template.urdf"], + "task-description": "pick up different objects and arrange them on a board marked with corresponding silhouettes.", + "task-name": "assembling-kits"} + +{"assets-used": ["pallet/pallet.urdf", "box/box-template.urdf"], + "task-description": "pick up homogeneous fixed-sized boxes and stack them in transposed layers on the pallet.", + "task-name": "palletizing-boxes"} + +{"assets-used": ["stacking/stand.urdf", "stacking/block.urdf"], + "task-description": "sequentially stack 6 blocks into a pyramid of 3-2-1 with rainbow colored ordering.", + "task-name": "stack-block-pyramid"} + +{"assets-used": ["container/container-template.urdf", "box/box-template.urdf"], + "task-description": "pick up randomly sized boxes and place them tightly into a container.", + "task-name": "packing-boxes"} + +{"assets-used": ["bowl/bowl.urdf", "stacking/block.urdf"], + "task-description": "pick up the red blocks and place them into the green bowls amidst other objects.", + "task-name": "place-red-in-green"} + +{"assets-used": ["box/box-template.urdf", "corner/corner-template.urdf"], + "task-description": "pick up the randomly sized box and align one of its corners to the L-shaped marker on the tabletop..", + "task-name": "align-box-corner"} + +{"assets-used": ["box/box-template.urdf", "corner/corner-template.urdf"], + "task-description": "pick up the randomly sized box and align one of its corners to the L-shaped marker on the tabletop..", + "task-name": "align-box-corner"} + +========= +Here are some tasks that you have come up with before. Try to learn from these structures but avoid overlapping with these tasks. For instance, `bowl_ball_placement` and `sort_balls_in_bowls` are the same task. `pile_boxes_in_corner` and `stack_blocks_into_pallet` are similar tasks, `align-cylinder-in-corner` and `align-cylinder-corner` are similar. +PAST_TASKNAME_TEMPLATE + + +========= +Here are some bad example task instances with explanations. +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "box/box-template.urdf], +} +reasons: not interesting because it overlaps with the current task `put-block-in-bowl`. + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], +} +reasons: the language descriptions are too ambiguous. Navigation is also hard to complete. Also maze.urf does not exist. + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], +} +reasons: this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere. Similarly tasks like `insert-ball-into-cylinder` and `cylinder-box-insertion` are invalid. + +{ + "task-name": "ball-box-obstacle-course", + "task-description": "Navigate a ball through an obstacle course created by randomly placed boxes and finally place it inside a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: Navigate the ball is not related to tabletop manipulation tasks. + +{ + "task-name": "ball-in-box", + "task-description": "Use a cable to guide a ball into an open box.", + "assets-used": ["cable/cable.urdf", "ball/ball-template.urdf", "box/box-template.urdf"] +} +reasons: This task is too hard since it involves interaction of the cable and the ball and cannot be easily completed. + +{ + "task-name": "ball-in-container", + "task-description": "Use the spatula to lift a ball over a wall of boxes and drop it into a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: The only action primitives as pick and place. One cannot use a spatula to lift an object. + +{ + "task-name": "line-ball-sorting", + "task-description": "Move balls of different colors along a single green line, placing each ball in a designated colored box at the end of the line. The challenge includes precision in maintaining the ball on the line and the correct identification of the box color corresponding to each ball.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf"] +} +reasons: Piling or stacking balls are physically infeasible in the simulation. + + + +========= +Now please describe the new task in natural languages and explain its novelty and challenges. Format the answer in a python dictionary with keys "task-name" and value type string, "task-description" (one specific sentence) and value type string, and "assets-used" and value type list of strings. Note that + +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. + + + diff --git a/prompts/vanilla_task_generation_prompt_simple_codeonly/cliport_prompt_code_split_template.txt b/prompts/vanilla_task_generation_prompt_simple_codeonly/cliport_prompt_code_split_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..33388797d32b78927f7495dc66357e24b51e2ceb --- /dev/null +++ b/prompts/vanilla_task_generation_prompt_simple_codeonly/cliport_prompt_code_split_template.txt @@ -0,0 +1,253 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. If you have only one goal, `step_max_reward` in `add_goal` should be 1. Use functions `make_piles` and `make_ropes` for creating piles and cables. To use spatula together with the push primitives, import the libraries +""" +from cliport.tasks import primitives; +from cliport.tasks.grippers import Spatula +""" +and then use `self.primitive = primitives.push` and `self.ee = Spatula`. +Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/vanilla_task_generation_prompt_simple_codeonly/cliport_prompt_task.txt b/prompts/vanilla_task_generation_prompt_simple_codeonly/cliport_prompt_task.txt new file mode 100644 index 0000000000000000000000000000000000000000..26668a81acfc4fd5ca1d5a694a27d900b96e17a7 --- /dev/null +++ b/prompts/vanilla_task_generation_prompt_simple_codeonly/cliport_prompt_task.txt @@ -0,0 +1,195 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task. My goal is to design diverse and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Use only these assets in the task and code design. +""" +insertion/: +ell.urdf fixture.urdf + +bowl/: +bowl.urdf + +box/: +box-template.urdf + +stacking/: +block.urdf stand.urdf + +zone/: +zone.obj zone.urdf + +pallet/: +pallet.obj pallet.urdf + +ball/: +ball-template.urdf + +cylinder/: +cylinder-template.urdf + +bowl/: +bowl.urdf + +# assets not for picking +corner/: +corner-template.urdf + +line/: +single-green-line-template.urdf + +container/: +container-template.urdf +""" + + +========= +Now I will provide you some reference code and you can write the code for the task. + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick) + + self.lang_goals.append(self.lang_template.format(obj=shapes[obj_shapes[i]])) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1) + self.lang_goals.append(self.lang_template) + + # Colors of distractor objects. + # IMPORTANT: RETRIEVE THE ACTUAL COLOR VALUES + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" + +========= +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. + diff --git a/prompts/vanilla_task_generation_prompt_simple_singleprompt/cliport_prompt_code_split_template.txt b/prompts/vanilla_task_generation_prompt_simple_singleprompt/cliport_prompt_code_split_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..33388797d32b78927f7495dc66357e24b51e2ceb --- /dev/null +++ b/prompts/vanilla_task_generation_prompt_simple_singleprompt/cliport_prompt_code_split_template.txt @@ -0,0 +1,253 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. If you have only one goal, `step_max_reward` in `add_goal` should be 1. Use functions `make_piles` and `make_ropes` for creating piles and cables. To use spatula together with the push primitives, import the libraries +""" +from cliport.tasks import primitives; +from cliport.tasks.grippers import Spatula +""" +and then use `self.primitive = primitives.push` and `self.ee = Spatula`. +Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/vanilla_task_generation_prompt_simple_singleprompt/cliport_prompt_task.txt b/prompts/vanilla_task_generation_prompt_simple_singleprompt/cliport_prompt_task.txt new file mode 100644 index 0000000000000000000000000000000000000000..c6c986544eae6600625c067ca77b67fe132d5c0c --- /dev/null +++ b/prompts/vanilla_task_generation_prompt_simple_singleprompt/cliport_prompt_task.txt @@ -0,0 +1,202 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task. My goal is to design diverse and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Use only these assets in the task and code design. +""" +insertion/: +ell.urdf fixture.urdf + +bowl/: +bowl.urdf + +box/: +box-template.urdf + +stacking/: +block.urdf stand.urdf + +zone/: +zone.obj zone.urdf + +pallet/: +pallet.obj pallet.urdf + +ball/: +ball-template.urdf + +cylinder/: +cylinder-template.urdf + +bowl/: +bowl.urdf + +# assets not for picking +corner/: +corner-template.urdf + +line/: +single-green-line-template.urdf + +container/: +container-template.urdf +""" + + +========= +Now I will provide you some reference code and you can write the code for the task. + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick) + + self.lang_goals.append(self.lang_template.format(obj=shapes[obj_shapes[i]])) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1) + self.lang_goals.append(self.lang_template) + + # Colors of distractor objects. + # IMPORTANT: RETRIEVE THE ACTUAL COLOR VALUES + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +========= +Now please describe the new task in natural languages and explain its novelty and challenges. Format the answer in a python dictionary with keys "task-name" and value type string, "task-description" (one specific sentence) and value type string, and "assets-used" and value type list of strings. Note that + +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. + +========= +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. + diff --git a/prompts/vanilla_task_generation_prompt_simple_zeroshot/cliport_prompt_code_split_template.txt b/prompts/vanilla_task_generation_prompt_simple_zeroshot/cliport_prompt_code_split_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..33388797d32b78927f7495dc66357e24b51e2ceb --- /dev/null +++ b/prompts/vanilla_task_generation_prompt_simple_zeroshot/cliport_prompt_code_split_template.txt @@ -0,0 +1,253 @@ +Now I will provide you some reference code and you can write the code for the task "TASK_NAME_TEMPLATE". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. If you have only one goal, `step_max_reward` in `add_goal` should be 1. Use functions `make_piles` and `make_ropes` for creating piles and cables. To use spatula together with the push primitives, import the libraries +""" +from cliport.tasks import primitives; +from cliport.tasks.grippers import Spatula +""" +and then use `self.primitive = primitives.push` and `self.ee = Spatula`. +Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE diff --git a/prompts/vanilla_task_generation_prompt_simple_zeroshot/cliport_prompt_task.txt b/prompts/vanilla_task_generation_prompt_simple_zeroshot/cliport_prompt_task.txt new file mode 100644 index 0000000000000000000000000000000000000000..ea48623e33fb84c75932c4d7ab6b60572f409a05 --- /dev/null +++ b/prompts/vanilla_task_generation_prompt_simple_zeroshot/cliport_prompt_task.txt @@ -0,0 +1,45 @@ +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task. My goal is to design diverse and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Use only these assets in the task and code design. +""" +insertion/: +ell.urdf fixture.urdf + +bowl/: +bowl.urdf + +box/: +box-template.urdf + +stacking/: +block.urdf stand.urdf + +zone/: +zone.obj zone.urdf + +pallet/: +pallet.obj pallet.urdf + +ball/: +ball-template.urdf + +cylinder/: +cylinder-template.urdf + +bowl/: +bowl.urdf + +# assets not for picking +corner/: +corner-template.urdf + +line/: +single-green-line-template.urdf + +container/: +container-template.urdf +""" + +========= +Now please describe the new task in natural languages and explain its novelty and challenges. Format the answer in a python dictionary with keys "task-name" and value type string, "task-description" (one specific sentence) and value type string, and "assets-used" and value type list of strings. diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..e962990d60fc441ac1ebc43d3888aa0384e2df88 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,23 @@ +absl-py +gym +numpy +pybullet +matplotlib +opencv-python +meshcat +scipy +scikit-image +torch==1.13 +hydra-core +ftfy +torchvision +transforms3d +wandb +kornia +gym +transformers +meshcat +openai +seaborn +trimesh +rtree \ No newline at end of file diff --git a/scripts/docker_build.py b/scripts/docker_build.py new file mode 100644 index 0000000000000000000000000000000000000000..5c504d2b3869ee3615a8656ab0d077c5a0470a7a --- /dev/null +++ b/scripts/docker_build.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python + +######### +# Credit: https://github.com/RobotLocomotion/pytorch-dense-correspondence/blob/master/docker/docker_build.py +######### + +from __future__ import print_function + +import argparse +import os +import getpass + +if __name__=="__main__": + + print("building docker container . . . ") + user_name = getpass.getuser() + default_image_name = user_name + "-cliport" + + + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--image", type=str, + help="name for the newly created docker image", default=default_image_name) + + parser.add_argument("-dr", "--dry_run", action='store_true', help="(optional) perform a dry_run, print the command that would have been executed but don't execute it.") + + parser.add_argument("-pw", "--password", type=str, + help="(optional) password for the user", default="password") + + parser.add_argument('-uid','--user_id', type=int, help="(optional) user id for this user", default=os.getuid()) + parser.add_argument('-gid','--group_id', type=int, help="(optional) user gid for this user", default=os.getgid()) + + parser.add_argument('-p', "--passthrough", type=str, help="(optional) passthrough arguments to add to the docker build") + + args = parser.parse_args() + print("building docker image named ", args.image) + cmd = "docker build --build-arg USER_NAME=%(user_name)s --build-arg USER_PASSWORD=%(password)s --build-arg USER_ID=%(user_id)s --build-arg USER_GID=%(group_id)s" \ + %{'user_name': user_name, 'password': args.password, 'user_id': args.user_id, 'group_id': args.group_id} + + if args.passthrough: + cmd += " " + args.passthrough + + cmd += " -t %s -f Dockerfile ." % args.image + + + print("command = \n \n", cmd) + print("") + + # build the docker image + if not args.dry_run: + print("executing shell command") + os.system(cmd) + else: + print("dry run, not executing command") \ No newline at end of file diff --git a/scripts/docker_run.py b/scripts/docker_run.py new file mode 100644 index 0000000000000000000000000000000000000000..74fc8d235d4de6ff2d33681ac07ed565098e6c6c --- /dev/null +++ b/scripts/docker_run.py @@ -0,0 +1,112 @@ +#!/usr/bin/env python +from __future__ import print_function + +######### +# Credit: https://github.com/RobotLocomotion/pytorch-dense-correspondence/blob/master/docker/docker_run.py +######### + +import argparse +import os +import socket +import getpass +import yaml + +if __name__=="__main__": + user_name = getpass.getuser() + default_image_name = user_name + '-cliport' + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--image", type=str, + help="(required) name of the image that this container is derived from", default=default_image_name) + + parser.add_argument("-nd", "--nvidia_docker", action='store_true', help="(optional) use nvidia-docker instead of docker") + + parser.add_argument("-c", "--container", type=str, default="cliport", help="(optional) name of the container") + + parser.add_argument("-d", "--data", type=str, default="data/", help="(optional) external data directory") + + parser.add_argument("-hl", "--headless", action='store_true', help="(optional) run in headless mode") + + parser.add_argument("-r", "--root", action='store_true', help="(optional) login as root instead of user") + + parser.add_argument("-g", "--gpus", type=str, default="all", help="(optional) gpus for nvidia docker") + + parser.add_argument("-dr", "--dry_run", action='store_true', help="(optional) perform a dry_run, print the command that would have been executed but don't execute it.") + + parser.add_argument("-p", "--passthrough", type=str, default="", help="(optional) extra string that will be tacked onto the docker run command, allows you to pass extra options. Make sure to put this in quotes and leave a space before the first character") + + args = parser.parse_args() + print("running docker container derived from image %s" %args.image) + source_dir = os.getcwd() + + image_name = args.image + home_directory = '/home/' + user_name + + cmd = "" + cmd += "xhost +local:root \n" if not args.headless else "" + cmd += "docker run " + if args.container: + cmd += " --name %(container_name)s " % {'container_name': args.container} + + # gpus + if args.nvidia_docker: + cmd += "--gpus all " + else: + cmd += " --gpus %s" % (args.gpus) + + # display + if args.headless: + cmd += " -v /usr/bin/nvidia-xconfig:/usr/bin/nvidia-xconfig " + else: # enable graphics + cmd += " --env DISPLAY=unix$DISPLAY"\ + " --env XAUTHORITY"\ + " --env NVIDIA_DRIVER_CAPABILITIES=all"\ + " --volume /tmp/.X11-unix:/tmp/.X11-unix"\ + " --volume /dev/input:/dev/input" + + + # bindings + cmd += " -v %(source_dir)s:%(home_directory)s/cliport " \ + % {'source_dir': source_dir, 'home_directory': home_directory} # mount source + cmd += " -v ~/.ssh:%(home_directory)s/.ssh " % {'home_directory': home_directory} # mount ssh keys + cmd += " -v ~/.torch:%(home_directory)s/.torch " % {'home_directory': home_directory} # mount torch folder + + cmd += " --user %s " % ("root" if args.root else user_name) # login + + # custom data path + cmd += " -v %s:/data " %(os.path.join(source_dir, args.data)) + + # expose UDP ports + cmd += " -p 8888:8888 " + cmd += " --ipc=host " + + # share host machine network + cmd += " --network=host " + + cmd += " " + args.passthrough + " " + + cmd += " --privileged" + + cmd += " --rm " # remove the image when you exit + + cmd += "-it " + cmd += args.image + cmd_endxhost = "xhost -local:root" + print("command:\n", cmd) + print("command = \n \n", cmd, "\n", cmd_endxhost) + print("") + + # build the docker image + if not args.dry_run: + print("executing shell command") + code = os.system(cmd) + print("Executed with code ", code) + if not args.headless: + os.system(cmd_endxhost) + # Squash return code to 0/1, as + # Docker's very large return codes + # were tricking Jenkins' failure + # detection + exit(code != 0) + else: + print("dry run, not executing command") + exit(0) \ No newline at end of file diff --git a/scripts/generate_datasets.sh b/scripts/generate_datasets.sh new file mode 100644 index 0000000000000000000000000000000000000000..c506fc61fccdafadc58c0f9766ac4664b186b89e --- /dev/null +++ b/scripts/generate_datasets.sh @@ -0,0 +1,36 @@ +#!/bin/bash + +DATA_DIR=$1 +DISP=False + +echo "Generating dataset... Folder: $DATA_DIR" + +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks + +LANG_TASKS=$2 + +for task in $LANG_TASKS + do + python cliport/demos.py n=100 task=$task mode=train data_dir=$DATA_DIR disp=$DISP & + python cliport/demos.py n=100 task=$task mode=val data_dir=$DATA_DIR disp=$DISP & + python cliport/demos.py n=100 task=$task mode=test data_dir=$DATA_DIR disp=$DISP + done +echo "Finished Language Tasks." + + +######################### +## Demo-Conditioned Tasks +# LANG_TASKS='align-rope assembling-kits-seq-seen-colors assembling-kits-seq-unseen-colors packing-shapes packing-boxes-pairs-seen-colors packing-boxes-pairs-unseen-colors packing-seen-google-objects-seq packing-unseen-google-objects-seq packing-seen-google-objects-group packing-unseen-google-objects-group put-block-in-bowl-seen-colors put-block-in-bowl-unseen-colors stack-block-pyramid-seq-seen-colors stack-block-pyramid-seq-unseen-colors separating-piles-seen-colors separating-piles-unseen-colors towers-of-hanoi-seq-seen-colors towers-of-hanoi-seq-unseen-colors' +DEMO_TASKS=$2 +for task in $DEMO_TASKS + do + python cliport/demos.py n=100 task=$task mode=train data_dir=$DATA_DIR disp=$DISP & + python cliport/demos.py n=100 task=$task mode=val data_dir=$DATA_DIR disp=$DISP & + python cliport/demos.py n=100 task=$task mode=test data_dir=$DATA_DIR disp=$DISP + done +echo "Finished Demo Tasks." + + diff --git a/scripts/generate_gpt_datasets.sh b/scripts/generate_gpt_datasets.sh new file mode 100644 index 0000000000000000000000000000000000000000..008219df8a9bfe1e02305f00d1e34f61ecccfac2 --- /dev/null +++ b/scripts/generate_gpt_datasets.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +DATA_DIR=/home/yzc/shared/project/GPT-CLIPort/data +TASK='put-block-in-bowl align-box-corner stack-block-pyramid-seq align-pair-colored-blocks-along-line vertical-insertion-blocks stack-blocks-in-container' +DISP=False + +echo "Generating dataset... Folder: $DATA_DIR" + +# sh scripts/generate_gpt_datasets.sh data "align-rope assembling-kits-seq-seen-colors assembling-kits-seq-unseen-colors packing-shapes packing-boxes-pairs-seen-colors packing-boxes-pairs-unseen-colors packing-seen-google-objects-seq packing-unseen-google-objects-seq packing-seen-google-objects-group packing-unseen-google-objects-group put-block-in-bowl-seen-colors put-block-in-bowl-unseen-colors stack-block-pyramid-seq-seen-colors stack-block-pyramid-seq-unseen-colors separating-piles-seen-colors separating-piles-unseen-colors towers-of-hanoi-seq-seen-colors towers-of-hanoi-seq-unseen-colors +# sh scripts/generate_gpt_datasets.sh data "assemble-single-car stack-color-coordinated-blocks color-structured-block-tower insert-blocks-into-fixture construct-corner-building colored-cylinder-in-square color-coordinated-block-tower build-house align-pair-colored-blocks-along-line insert-sphere-into-container build-wheel build-two-circles build-car build-bridge manipulating-two-ropes rainbow-stack mix-piles stack-blocks-in-container" +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks + +# LANG_TASKS='align-rope assembling-kits-seq-seen-colors' +# trap "kill 0" SIGINT + +# LANG_TASKS='place_red_in_green' +LANG_TASKS='rainbow-stack' + + +for task in $LANG_TASKS + do + python cliport/demos.py n=200 task=$task mode=train data_dir=$DATA_DIR disp=$DISP & + python cliport/demos.py n=50 task=$task mode=val data_dir=$DATA_DIR disp=$DISP & + python cliport/demos.py n=100 task=$task mode=test data_dir=$DATA_DIR disp=$DISP & + done +wait + +echo "Finished Language Tasks." + + diff --git a/scripts/generate_interesting_task.sh b/scripts/generate_interesting_task.sh new file mode 100644 index 0000000000000000000000000000000000000000..3939e5cd9631ecdf0a33d99373253c51551e542a --- /dev/null +++ b/scripts/generate_interesting_task.sh @@ -0,0 +1,7 @@ + + +for task in "rope-disentange" "move-piles-along-line" "rope-along-line" "rope-connect-cylinder" "rope-connect-corners" + do + python gensim/run_simulation.py disp=False prompt_folder=cliport_multistep_collaborative_prompt trials=20 \ + save_memory=True load_memory=True task_description_candidate_num=10 use_template=True target_task_name=$task + done diff --git a/scripts/google_objects_download.sh b/scripts/google_objects_download.sh new file mode 100644 index 0000000000000000000000000000000000000000..b697aedc4afe48aea2fe4fd5cfcfc9eec65b90c9 --- /dev/null +++ b/scripts/google_objects_download.sh @@ -0,0 +1,4 @@ +wget https://github.com/cliport/cliport/releases/download/v1.0.0/google.zip +unzip google.zip +mv google cliport/environments/assets +rm google.zip \ No newline at end of file diff --git a/scripts/install_deps.sh b/scripts/install_deps.sh new file mode 100755 index 0000000000000000000000000000000000000000..1483a9ba0d8a79c9e5cb070c1f7a1be8fdeb722e --- /dev/null +++ b/scripts/install_deps.sh @@ -0,0 +1,50 @@ +#!/bin/bash + +set -euxo pipefail + +# update +apt-get update + +# common +apt-get -y install software-properties-common + +# python source list +add-apt-repository -y ppa:deadsnakes/ppa + +# dependencies +apt-get update +DEBIAN_FRONTEND=noninteractive apt install --no-install-recommends \ + curl \ + terminator \ + tmux \ + vim \ + gedit \ + git \ + openssh-client \ + openssh-server \ + unzip \ + htop \ + apt-utils \ + usbutils \ + dialog \ + python3.8-venv \ + python3.8-dev \ + ffmpeg \ + nvidia-settings \ + libffi-dev \ + libfreetype6-dev \ + libgl1-mesa-dev \ + flex \ + bison \ + build-essential \ + gcc \ + git \ + wget \ + module-init-tools \ + pciutils \ + xserver-xorg \ + xserver-xorg-video-fbdev \ + xauth \ + python3-pip \ + python3-ipdb \ + python3-tk \ No newline at end of file diff --git a/scripts/metascripts/gen10_build_car.sh b/scripts/metascripts/gen10_build_car.sh new file mode 100644 index 0000000000000000000000000000000000000000..580e5ad7d6b9e46d78fa6208f80fa3dc7975ef75 --- /dev/null +++ b/scripts/metascripts/gen10_build_car.sh @@ -0,0 +1,14 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ +"[align-rope,sweeping-piles,align-box-corner,towers-of-hanoi-seq-seen-colors,assembling-kits-seq-seen-colors,block-insertion,palletizing-boxes,place-red-in-green,manipulating-rope,packing-boxes]" \ +"[build-car]" 10taskgen_unrelated $STEPS + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ +"[build-two-circles,build-wheel,build-bridge,towers-of-hanoi-seq-seen-colors,stack-block-pyramid-seq-seen-colors,create-pyramid-blocks-and-container,palletizing-boxes,assemble-single-car,rainbow-stack]" \ +"[build-car]" 10taskgen_related $STEPS diff --git a/scripts/metascripts/gen3_build_car.sh b/scripts/metascripts/gen3_build_car.sh new file mode 100644 index 0000000000000000000000000000000000000000..088313a69501eebf842206e2288834ff9cd6fc8d --- /dev/null +++ b/scripts/metascripts/gen3_build_car.sh @@ -0,0 +1,11 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ +"[align-rope,sweeping-piles,align-box-corner]" "[build-car]" 3taskgen_unrelated $STEPS + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ +"[build-two-circles,build-wheel,build-bridge]" "[build-car]" 3taskgen_related $STEPS diff --git a/scripts/metascripts/gen5_build_car.sh b/scripts/metascripts/gen5_build_car.sh new file mode 100644 index 0000000000000000000000000000000000000000..0770a5f946691a4930793d89e694740b807c0ce7 --- /dev/null +++ b/scripts/metascripts/gen5_build_car.sh @@ -0,0 +1,15 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'15000'} + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ + "[align-rope,sweeping-piles,align-box-corner,towers-of-hanoi-seq-seen-colors,assembling-kits-seq-seen-colors]" "[build-car]" \ + 5taskgen_unrelated $STEPS + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ +"[build-two-circles,build-wheel,build-bridge,towers-of-hanoi-seq-seen-colors,stack-block-pyramid-seq-seen-colors]" "[build-car]" \ + 5taskgen_related $STEPS diff --git a/scripts/metascripts/train10_cliport_indomain.sh b/scripts/metascripts/train10_cliport_indomain.sh new file mode 100644 index 0000000000000000000000000000000000000000..335be9efaf9c529a918ada89e5e1c9bec3eda79b --- /dev/null +++ b/scripts/metascripts/train10_cliport_indomain.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data \ +"[align-rope,sweeping-piles,align-box-corner,towers-of-hanoi-seq-seen-colors,assembling-kits-seq-seen-colors,block-insertion,palletizing-boxes,place-red-in-green,manipulating-rope,packing-boxes]" \ + cliport10_task_indomain $STEPS + diff --git a/scripts/metascripts/train10_gpt_finetune_generalization.sh b/scripts/metascripts/train10_gpt_finetune_generalization.sh new file mode 100644 index 0000000000000000000000000000000000000000..61b5d39f2cbf18ca5f6fc23156ea9f8832d8649b --- /dev/null +++ b/scripts/metascripts/train10_gpt_finetune_generalization.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'50000'} + +sh scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh data \ +"[mix-piles,rainbow-stack,manipulating-two-ropes,insert-sphere-into-container,align-pair-colored-blocks-along-line,construct-corner-building,colorful_block-tower-on-cylinder-base,build-bridge,push_piles-into-letter]"\ +"[sorting-blocks-into-pallets,build-two-circles,align-cylinders-in-square,Four-corner-pyramid-challenge,corner-sort-cylinders]" \ +gpt10task_gen_finetune $STEPS \ No newline at end of file diff --git a/scripts/metascripts/train10_gpt_generalization.sh b/scripts/metascripts/train10_gpt_generalization.sh new file mode 100644 index 0000000000000000000000000000000000000000..79cd3381fe46511bffc0ecd6317613c21ab6caa9 --- /dev/null +++ b/scripts/metascripts/train10_gpt_generalization.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ +"[mix-piles,rainbow-stack,manipulating-two-ropes,insert-sphere-into-container,align-pair-colored-blocks-along-line,construct-corner-building,colorful_block-tower-on-cylinder-base,build-bridge,push_piles-into-letter]"\ +"[sorting-blocks-into-pallets,build-two-circles,align-cylinders-in-square,Four-corner-pyramid-challenge,corner-sort-cylinders]" \ +gpt10task_gen $STEPS diff --git a/scripts/metascripts/train10_gpt_indomain.sh b/scripts/metascripts/train10_gpt_indomain.sh new file mode 100644 index 0000000000000000000000000000000000000000..cc436d4afc83cc2b71c962c5c21987f790a55957 --- /dev/null +++ b/scripts/metascripts/train10_gpt_indomain.sh @@ -0,0 +1,11 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'50000'} + +sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data \ + "[mix-piles,rainbow-stack,manipulating-two-ropes,insert-sphere-into-container,align-pair-colored-blocks-along-line,construct-corner-building,colorful_block-tower-on-cylinder-base,build-bridge,push_piles-into-letter]"\ + gpt10_task_indomain $STEPS diff --git a/scripts/metascripts/train10_gptmixcliport2_smaller.sh b/scripts/metascripts/train10_gptmixcliport2_smaller.sh new file mode 100644 index 0000000000000000000000000000000000000000..a8ff83c815320f7eaca262aef4c08c3fecabac1c --- /dev/null +++ b/scripts/metascripts/train10_gptmixcliport2_smaller.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal_smaller.sh data \ + "[put-block-in-bowl,align-box-corner,color-sorted-container-stack,color-sorted-block-race,Four-corner-pyramid-challenge,triangle-block-arrangement,sort-and-stack-clr-blocks,color-coordinated-sphere-insertion,rainbow-stack,align-pair-colored-blocks-along-line,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[put-block-in-bowl,align-box-corner]" \ + gpt5_mixcliport3_task $STEPS diff --git a/scripts/metascripts/train10_gptmixcliport3.sh b/scripts/metascripts/train10_gptmixcliport3.sh new file mode 100644 index 0000000000000000000000000000000000000000..0728981a26bd2e5c0113bbd80ddd15e76dddd84d --- /dev/null +++ b/scripts/metascripts/train10_gptmixcliport3.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ + "[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq,color-sorted-container-stack,color-sorted-block-race,Four-corner-pyramid-challenge,triangle-block-arrangement,sort-and-stack-clr-blocks,color-coordinated-sphere-insertion,rainbow-stack,align-pair-colored-blocks-along-line,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq]" \ + gpt5_mixcliport3_task $STEPS diff --git a/scripts/metascripts/train10_gptmixcliport3_new_pickplace_demo10.sh b/scripts/metascripts/train10_gptmixcliport3_new_pickplace_demo10.sh new file mode 100644 index 0000000000000000000000000000000000000000..439fcc987fd0f0b221ca34731aa377d15bde720e --- /dev/null +++ b/scripts/metascripts/train10_gptmixcliport3_new_pickplace_demo10.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal_demo10.sh data \ + "[place-red-in-green,stack-block-pyramid,align-box-corner,packing-boxes,block-insertion,color_linked_ball_bowl_ordering,color_specific_container_fill,insert_blocks_into_fixture,sort_insert_color_coordinated_blocks,color_ordered_blocks_on_pallet,color-coordinated-sphere-insertion,rainbow-stack,put-block-in-bowl,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[place-red-in-green,stack-block-pyramid,put-block-in-bowl,align-box-corner,packing-boxes,block-insertion]" \ + gpt5_mixcliport2_task_new \ No newline at end of file diff --git a/scripts/metascripts/train10_gptmixcliport3_small.sh b/scripts/metascripts/train10_gptmixcliport3_small.sh new file mode 100644 index 0000000000000000000000000000000000000000..5276fd7c1a1a024eb05f9cdec5fcc9da88f1bf45 --- /dev/null +++ b/scripts/metascripts/train10_gptmixcliport3_small.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal_small.sh data \ + "[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq,color-sorted-container-stack,color-sorted-block-race,Four-corner-pyramid-challenge,triangle-block-arrangement,sort-and-stack-clr-blocks,color-coordinated-sphere-insertion,rainbow-stack,align-pair-colored-blocks-along-line,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq]" \ + gpt5_mixcliport3_task $STEPS diff --git a/scripts/metascripts/train10_gptmixcliport5_new_pickplace_demo10.sh b/scripts/metascripts/train10_gptmixcliport5_new_pickplace_demo10.sh new file mode 100644 index 0000000000000000000000000000000000000000..7e36cd7688b092b053a693f33f96244e5a91449d --- /dev/null +++ b/scripts/metascripts/train10_gptmixcliport5_new_pickplace_demo10.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal_demo10.sh data \ + "[stack-block-pyramid,align-box-corner,put-block-in-bowl,packing-boxes,block-insertion,color_linked_ball_bowl_ordering,color_specific_container_fill,insert_blocks_into_fixture,sort_insert_color_coordinated_blocks,color_ordered_blocks_on_pallet,color-coordinated-sphere-insertion,rainbow-stack,put-block-in-bowl,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[stack-block-pyramid,put-block-in-bowl,align-box-corner,packing-boxes,block-insertion]" \ + gpt10_mixcliport5_task_new diff --git a/scripts/metascripts/train15_gpt_indomain.sh b/scripts/metascripts/train15_gpt_indomain.sh new file mode 100644 index 0000000000000000000000000000000000000000..31f1d94d49265f76b6e108e0e280aa747d2eb313 --- /dev/null +++ b/scripts/metascripts/train15_gpt_indomain.sh @@ -0,0 +1,10 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'10000'} + +sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data '[manipulating-two-ropes,construct-corner-building,color-coordinated-container-sorting,construct-corner-blocks,sort-insert-color-coordinated-blocks,insert-blocks-into-fixture,color-ordered-container-arrangement,symmetric-block-bridge-construction,connect-boxes-with-rope,vertical-insertion-blocks,cylinder-stand-alignment,insert-blocks-lineup,create-pyramid-blocks-and-container,mix-piles,multi-level-pyramid-construction,rainbow-stack,align-cylinders-in-square,align-balls-in-colored-zones,multicolor-block-bridge,align-spheres-in-colored-zones,color-blocks-in-cylinder-maze,sort-and-stack-clr-blocks,corner-block-challenge,stack-color-coordinated-blocks,assemble-single-car,color-structured-block-tower,color-sorted-block-race,sphere-align-stand,color-coordinated-block-tower,color-sorted-container-stack,color-ordered-insertion,block-pyramid-with-limited-space,sorting-blocks-into-pallets,place-ball-in-elevated-bowl,Four-corner-pyramid-challenge,color-coordinated-cylinder-tower,build-two-circles]' \ + gpt15_task_indomain \ No newline at end of file diff --git a/scripts/metascripts/train1_cliport_indomain.sh b/scripts/metascripts/train1_cliport_indomain.sh new file mode 100644 index 0000000000000000000000000000000000000000..275fbf908ee3b7528c3f8b874d5ba99d3512c453 --- /dev/null +++ b/scripts/metascripts/train1_cliport_indomain.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'61000'} + +bash scripts/traintest_scripts/train_test_single_task_indistribution.sh data \ +"place_red_in_green" \ + $STEPS + diff --git a/scripts/metascripts/train20_gptmixcliport5_new_pickplace_demo10.sh b/scripts/metascripts/train20_gptmixcliport5_new_pickplace_demo10.sh new file mode 100644 index 0000000000000000000000000000000000000000..05dc4c65d971c214036ba642772e0f639607fd6b --- /dev/null +++ b/scripts/metascripts/train20_gptmixcliport5_new_pickplace_demo10.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal_demo10.sh data \ + "[stack-block-pyramid,align-box-corner,put-block-in-bowl,packing-boxes,block-insertion,color_linked_ball_bowl_ordering,color_specific_container_fill,insert_blocks_into_fixture,sort_insert_color_coordinated_blocks,color_ordered_blocks_on_pallet,color-coordinated-sphere-insertion,rainbow-stack,put-block-in-bowl,vertical-insertion-blocks,stack-blocks-in-container,'Four-corner-pyramid-challenge','create-pyramid-with-color-coded-ells','align-balls-in-colored-zones','construct-corner-blocks','color-linked-ball-bowl-ordering','create-pyramid-blocks-and-container','color-specific-container-fill','color-ordered-container-arrangement','pyramid-blocks-assemble']" \ + "[stack-block-pyramid,put-block-in-bowl,align-box-corner,packing-boxes,block-insertion]" \ + gpt10_mixcliport5_task_new diff --git a/scripts/metascripts/train2_cliport_indomain_smaller.sh b/scripts/metascripts/train2_cliport_indomain_smaller.sh new file mode 100644 index 0000000000000000000000000000000000000000..eee155c541b88d107aadc0786917adbc646bd96d --- /dev/null +++ b/scripts/metascripts/train2_cliport_indomain_smaller.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'15000'} + +sh scripts/traintest_scripts/train_test_multi_task_indistribution_smaller.sh data \ +"[put-block-in-bowl,align-box-corner]" \ + cliport3_task_indomain $STEPS + diff --git a/scripts/metascripts/train30_gpt_indomain.sh b/scripts/metascripts/train30_gpt_indomain.sh new file mode 100644 index 0000000000000000000000000000000000000000..892bdbd3c6b3632b1409b669708bfd380e19f409 --- /dev/null +++ b/scripts/metascripts/train30_gpt_indomain.sh @@ -0,0 +1,10 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'10000'} + +sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data '[color-linked-ball-bowl-ordering,build-cylinder-structure,corner-sort-cylinders,align-pair-colored-blocks-along-line,color-coordinated-cylinders-in-boxes,insert-sphere-into-container,build-wheel,push-piles-into-letter,create-pyramid-with-color-coded-ells,color-coordinated-sphere-insertion,move-piles-along-line,multi-level-block-construction,build-car,color-coordinated-insertion,triangle-block-arrangement,colorful-block-tower-on-cylinder-base,manipulating-two-ropes,construct-corner-building,color-coordinated-container-sorting,construct-corner-blocks,sort-insert-color-coordinated-blocks,insert-blocks-into-fixture,color-ordered-container-arrangement,symmetric-block-bridge-construction,connect-boxes-with-rope,vertical-insertion-blocks,cylinder-stand-alignment,insert-blocks-lineup,create-pyramid-blocks-and-container,mix-piles,multi-level-pyramid-construction,rainbow-stack,align-cylinders-in-square,align-balls-in-colored-zones,multicolor-block-bridge,align-spheres-in-colored-zones,color-blocks-in-cylinder-maze,sort-and-stack-clr-blocks,corner-block-challenge,stack-color-coordinated-blocks,assemble-single-car,color-structured-block-tower,color-sorted-block-race,sphere-align-stand,color-coordinated-block-tower,color-sorted-container-stack,color-ordered-insertion,block-pyramid-with-limited-space,sorting-blocks-into-pallets,place-ball-in-elevated-bowl,Four-corner-pyramid-challenge,color-coordinated-cylinder-tower,build-two-circles]' \ + gpt30_task_indomain \ No newline at end of file diff --git a/scripts/metascripts/train3_cliport_indomain.sh b/scripts/metascripts/train3_cliport_indomain.sh new file mode 100644 index 0000000000000000000000000000000000000000..fd9ea6ce4695e0c2d29332070f17d2fb3efe2559 --- /dev/null +++ b/scripts/metascripts/train3_cliport_indomain.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'15000'} + +sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data \ +"[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq]" \ + cliport3_task_indomain $STEPS + diff --git a/scripts/metascripts/train3_cliport_indomain_small.sh b/scripts/metascripts/train3_cliport_indomain_small.sh new file mode 100644 index 0000000000000000000000000000000000000000..250de044eb27a54505b3ec8c72fa56c2a917d0e6 --- /dev/null +++ b/scripts/metascripts/train3_cliport_indomain_small.sh @@ -0,0 +1,13 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'15000'} +now=$(date "+%Y-%m-%d_%H-%M-%S") + +sh scripts/traintest_scripts/train_test_multi_task_indistribution_small.sh data \ +"[stack-block-pyramid,put-block-in-bowl,place-red-in-green]" \ + cliport3_task_indomain_demo50_2023-07-27_23-08-25 $STEPS + diff --git a/scripts/metascripts/train3_cliport_indomain_small_demo10.sh b/scripts/metascripts/train3_cliport_indomain_small_demo10.sh new file mode 100644 index 0000000000000000000000000000000000000000..7e02cbd4eae5e44a89de04a9b6d155101d22541b --- /dev/null +++ b/scripts/metascripts/train3_cliport_indomain_small_demo10.sh @@ -0,0 +1,14 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'15000'} +now=$(date "+%Y-%m-%d_%H-%M-%S") + +sh scripts/traintest_scripts/train_test_multi_task_goal_demo10.sh data \ +"[stack-block-pyramid,put-block-in-bowl]" \ +"[stack-block-pyramid,put-block-in-bowl]" \ + cliport3_task_indomain_demo10_${now} $STEPS + diff --git a/scripts/metascripts/train3_cliport_indomain_small_demo50.sh b/scripts/metascripts/train3_cliport_indomain_small_demo50.sh new file mode 100644 index 0000000000000000000000000000000000000000..145d6bc978d426e351bbc150f4abdf87471e23fc --- /dev/null +++ b/scripts/metascripts/train3_cliport_indomain_small_demo50.sh @@ -0,0 +1,14 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'15000'} +now=$(date "+%Y-%m-%d_%H-%M-%S") + +sh scripts/traintest_scripts/train_test_multi_task_goal_demo50.sh data \ +"[stack-block-pyramid,put-block-in-bowl]" \ +"[stack-block-pyramid,put-block-in-bowl]" \ + cliport3_task_indomain_demo50_${now} $STEPS + diff --git a/scripts/metascripts/train3_gptmixcliport0_new_pickplace.sh b/scripts/metascripts/train3_gptmixcliport0_new_pickplace.sh new file mode 100644 index 0000000000000000000000000000000000000000..44483c363a142224b98a920f3ab97578f88791fa --- /dev/null +++ b/scripts/metascripts/train3_gptmixcliport0_new_pickplace.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ + "[place-red-in-green,stack-block-pyramid]" \ + "[place-red-in-green,stack-block-pyramid]" \ + gpt0_mixcliport2_task_new diff --git a/scripts/metascripts/train3_gptmixcliport2_new_pickplace.sh b/scripts/metascripts/train3_gptmixcliport2_new_pickplace.sh new file mode 100644 index 0000000000000000000000000000000000000000..bdb5ba8a1559d26e240e1b3f6d5945daf28bbdf9 --- /dev/null +++ b/scripts/metascripts/train3_gptmixcliport2_new_pickplace.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} +now=$(date "+%Y-%m-%d_%H-%M-%S") + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ + "[stack-block-pyramid,put-block-in-bowl,color-coordinated-sphere-insertion,rainbow-stack,vertical-insertion-blocks]" \ + "[stack-block-pyramid,put-block-in-bowl]" \ + gpt3_mixcliport2_task_new_${now} \ No newline at end of file diff --git a/scripts/metascripts/train3_gptmixcliport2_new_pickplace_demo10.sh b/scripts/metascripts/train3_gptmixcliport2_new_pickplace_demo10.sh new file mode 100644 index 0000000000000000000000000000000000000000..64eb770312460bc46798b596b1b5bfd4c0581aab --- /dev/null +++ b/scripts/metascripts/train3_gptmixcliport2_new_pickplace_demo10.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal_demo10.sh data \ + "[stack-block-pyramid,color-coordinated-sphere-insertion,rainbow-stack,put-block-in-bowl,vertical-insertion-blocks]" \ + "[put-block-in-bowl,stack-block-pyramid]" \ + gpt3_mixcliport2_task_new diff --git a/scripts/metascripts/train3_gptmixcliport3.sh b/scripts/metascripts/train3_gptmixcliport3.sh new file mode 100644 index 0000000000000000000000000000000000000000..02a1d14cfa3f5c1a04d511f66c8bc14c2e864a1d --- /dev/null +++ b/scripts/metascripts/train3_gptmixcliport3.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ + "[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq,align-pair-colored-blocks-along-line,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq]" \ + gpt3_mixcliport3_task $STEPS diff --git a/scripts/metascripts/train3_gptmixcliport3_small.sh b/scripts/metascripts/train3_gptmixcliport3_small.sh new file mode 100644 index 0000000000000000000000000000000000000000..8a6d0311fb48c598836814349a207af362f608a9 --- /dev/null +++ b/scripts/metascripts/train3_gptmixcliport3_small.sh @@ -0,0 +1,13 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} +now=$(date "+%Y-%m-%d_%H-%M-%S") + + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ + "[place-red-in-green,stack-block-pyramid,put-block-in-bowl,color-coordinated-sphere-insertion,rainbow-stack,vertical-insertion-blocks]" \ + "[place-red-in-green,stack-block-pyramid,put-block-in-bowl]" \ + gpt3_mixcliport3_${now} \ No newline at end of file diff --git a/scripts/metascripts/train50_gpt_and_cliport_indomain.sh b/scripts/metascripts/train50_gpt_and_cliport_indomain.sh new file mode 100644 index 0000000000000000000000000000000000000000..2b8d0b520269041ae610aa6d6ff4d0860f9e7205 --- /dev/null +++ b/scripts/metascripts/train50_gpt_and_cliport_indomain.sh @@ -0,0 +1,9 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'10000'} + +sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data '[align-rope,assembling-kits-seq,palletizing-boxes,towers-of-hanoi,assembling-kits,manipulating-rope,packing-boxes,place-red-in-green,put-block-in-bowl,packing-boxes-pairs,sweeping-piles,separating-piles,stack-block-pyramid-seq,towers-of-hanoi-seq,packing-shapes,stack-block-pyramid,block-insertion,packing-google-objects,color-ordered-blocks-on-pallet,color-linked-ball-bowl-ordering,build-cylinder-structure,build-bridge,pyramid-blocks-assemble,sort-and-assemble-block-castle,stack-blocks-in-container,corner-sort-cylinders,align-pair-colored-blocks-along-line,color-specific-container-fill,colored-cylinder-in-square,construct-colorful-arch,color-coordinated-cylinders-in-boxes,insert-sphere-into-container,build-wheel,push-piles-into-letter,create-pyramid-with-color-coded-ells,color-coordinated-sphere-insertion,move-piles-along-line,multi-level-block-construction,build-car,color-coordinated-insertion,triangle-block-arrangement,colorful-block-tower-on-cylinder-base,manipulating-two-ropes,construct-corner-building,color-coordinated-container-sorting,construct-corner-blocks,sort-insert-color-coordinated-blocks,insert-blocks-into-fixture,color-ordered-container-arrangement,symmetric-block-bridge-construction,connect-boxes-with-rope,vertical-insertion-blocks,cylinder-stand-alignment,insert-blocks-lineup,create-pyramid-blocks-and-container,mix-piles,multi-level-pyramid-construction,rainbow-stack,align-cylinders-in-square,align-balls-in-colored-zones,multicolor-block-bridge,align-spheres-in-colored-zones,color-blocks-in-cylinder-maze,sort-and-stack-clr-blocks,corner-block-challenge,stack-color-coordinated-blocks,assemble-single-car,color-structured-block-tower,color-sorted-block-race,sphere-align-stand,color-coordinated-block-tower,color-sorted-container-stack,color-ordered-insertion,block-pyramid-with-limited-space,sorting-blocks-into-pallets,place-ball-in-elevated-bowl,Four-corner-pyramid-challenge,color-coordinated-cylinder-tower,build-two-circles]' gpt50_task_indomain \ No newline at end of file diff --git a/scripts/metascripts/train5_cliport_indomain.sh b/scripts/metascripts/train5_cliport_indomain.sh new file mode 100644 index 0000000000000000000000000000000000000000..361bf820bec015b8086adc58fa3e6f2c4978b9f7 --- /dev/null +++ b/scripts/metascripts/train5_cliport_indomain.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'20000'} + +sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data \ +"[align-rope,put-block-in-bowl,align-box-corner,stack-block-pyramid-seq,assembling-kits-seq-seen-colors]" \ + cliport5_task_indomain $STEPS + diff --git a/scripts/metascripts/train5_gpt_indomain_hard.sh b/scripts/metascripts/train5_gpt_indomain_hard.sh new file mode 100644 index 0000000000000000000000000000000000000000..901b1c57e44df6e6776ca1f037009faa37e5911e --- /dev/null +++ b/scripts/metascripts/train5_gpt_indomain_hard.sh @@ -0,0 +1,11 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data \ + "[build-wheel,build-two-circles,connect-boxes-with-rope,push-piles-into-letter,build-bridge]"\ + gpt5_task_indomain_hard $STEPS diff --git a/scripts/metascripts/train5_gpt_indomain_medium.sh b/scripts/metascripts/train5_gpt_indomain_medium.sh new file mode 100644 index 0000000000000000000000000000000000000000..6229c822baf4cc08034a2ccde8dd37129eb3f617 --- /dev/null +++ b/scripts/metascripts/train5_gpt_indomain_medium.sh @@ -0,0 +1,11 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data \ + "[mix-piles,rainbow-stack,align-pair-colored-blocks-along-line,construct-corner-blocks,stack-blocks-in-container]"\ + gpt5_task_indomain_medium $STEPS diff --git a/scripts/metascripts/train5_gpt_indomain_simple.sh b/scripts/metascripts/train5_gpt_indomain_simple.sh new file mode 100644 index 0000000000000000000000000000000000000000..b47034a08ebf51424bfb5adea77f6ed3f2752e2a --- /dev/null +++ b/scripts/metascripts/train5_gpt_indomain_simple.sh @@ -0,0 +1,11 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +STEPS=${1-'50000'} + +sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data \ + "[sorting-blocks-into-pallets,colorful-block-tower-on-cylinder-base,align-cylinders-in-square,color-coordinated-block-tower,insert-sphere-into-container]"\ + gpt5_task_indomain_simple $STEPS diff --git a/scripts/metascripts/train5_gptmixcliport2_new_pickplace.sh b/scripts/metascripts/train5_gptmixcliport2_new_pickplace.sh new file mode 100644 index 0000000000000000000000000000000000000000..54d705899f2751c9c8436938417a73d2f81d710a --- /dev/null +++ b/scripts/metascripts/train5_gptmixcliport2_new_pickplace.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ + "[stack-block-pyramid,color-coordinated-sphere-insertion,rainbow-stack,put-block-in-bowl,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[put-block-in-bowl,stack-block-pyramid]" \ + gpt5_mixcliport2_task_new diff --git a/scripts/metascripts/train5_gptmixcliport2_new_pickplace_demo10.sh b/scripts/metascripts/train5_gptmixcliport2_new_pickplace_demo10.sh new file mode 100644 index 0000000000000000000000000000000000000000..96a11bace36182223f89daba2963c4966c4debae --- /dev/null +++ b/scripts/metascripts/train5_gptmixcliport2_new_pickplace_demo10.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal_demo10.sh data \ + "[stack-block-pyramid,put-block-in-bowl,color-coordinated-sphere-insertion,rainbow-stack,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[stack-block-pyramid,put-block-in-bowl]" \ + gpt5_mixcliport2_task_new diff --git a/scripts/metascripts/train5_gptmixcliport2_new_pickplace_demo10_distant.sh b/scripts/metascripts/train5_gptmixcliport2_new_pickplace_demo10_distant.sh new file mode 100644 index 0000000000000000000000000000000000000000..77adac0b1f807bbcad22bdfb2428f1f8b4a700d5 --- /dev/null +++ b/scripts/metascripts/train5_gptmixcliport2_new_pickplace_demo10_distant.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal_demo10.sh data \ + "[stack-block-pyramid,put-block-in-bowl,manipulating-two-ropes,color-coordinated-sphere-insertion,assemble-single-car,connect-boxes-with-rope,move-piles-along-line,push-piles-into-letter]" \ + "[stack-block-pyramid,put-block-in-bowl]" \ + gpt5_mixcliport2_task_new_distant diff --git a/scripts/metascripts/train5_gptmixcliport2_smaller.sh b/scripts/metascripts/train5_gptmixcliport2_smaller.sh new file mode 100644 index 0000000000000000000000000000000000000000..c4453cde6d4a278e90cc1462ffaf243996926b7a --- /dev/null +++ b/scripts/metascripts/train5_gptmixcliport2_smaller.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal_smaller.sh data \ + "[put-block-in-bowl,align-box-corner,color-coordinated-sphere-insertion,rainbow-stack,align-pair-colored-blocks-along-line,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[put-block-in-bowl,align-box-corner]" \ + gpt5_mixcliport3_task $STEPS diff --git a/scripts/metascripts/train5_gptmixcliport3.sh b/scripts/metascripts/train5_gptmixcliport3.sh new file mode 100644 index 0000000000000000000000000000000000000000..f32b5f70965c26211dc1326dc678b10bd0c31d8c --- /dev/null +++ b/scripts/metascripts/train5_gptmixcliport3.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ + "[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq,color-coordinated-sphere-insertion,rainbow-stack,align-pair-colored-blocks-along-line,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq]" \ + gpt5_mixcliport3_task $STEPS diff --git a/scripts/metascripts/train5_gptmixcliport3_new_pickplace_demo10.sh b/scripts/metascripts/train5_gptmixcliport3_new_pickplace_demo10.sh new file mode 100644 index 0000000000000000000000000000000000000000..50cf40c1d466724c8f4123fbfb1f59d6bd4a9718 --- /dev/null +++ b/scripts/metascripts/train5_gptmixcliport3_new_pickplace_demo10.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} +now=$(date "+%Y-%m-%d_%H-%M-%S") + +sh scripts/traintest_scripts/train_test_multi_task_goal_demo10.sh data \ + "[stack-block-pyramid,color-coordinated-sphere-insertion,rainbow-stack,put-block-in-bowl,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[stack-block-pyramid,put-block-in-bowl]" \ + gpt5_mixcliport2_task_new_demo10_${now} \ No newline at end of file diff --git a/scripts/metascripts/train5_gptmixcliport3_small.sh b/scripts/metascripts/train5_gptmixcliport3_small.sh new file mode 100644 index 0000000000000000000000000000000000000000..49022f183428a4c92fe611cfa81f5def7215e5a5 --- /dev/null +++ b/scripts/metascripts/train5_gptmixcliport3_small.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal_small.sh data \ + "[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq,color-coordinated-sphere-insertion,rainbow-stack,align-pair-colored-blocks-along-line,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq]" \ + gpt5_mixcliport3_task $STEPS diff --git a/scripts/metascripts/train5_gptmixcliport5_new_pickplace_demo10.sh b/scripts/metascripts/train5_gptmixcliport5_new_pickplace_demo10.sh new file mode 100644 index 0000000000000000000000000000000000000000..a373dd26854feedc2e5536e2f006707a8c91b9a6 --- /dev/null +++ b/scripts/metascripts/train5_gptmixcliport5_new_pickplace_demo10.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal_demo10.sh data \ + "[stack-block-pyramid,align-box-corner,put-block-in-bowl,packing-boxes,block-insertion,color-coordinated-sphere-insertion,rainbow-stack,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[stack-block-pyramid,put-block-in-bowl,align-box-corner,packing-boxes,block-insertion]" \ + gpt5_mixcliport5_task_new diff --git a/scripts/metascripts/train5_gptmixcliport5_new_pickplace_demo10_distant.sh b/scripts/metascripts/train5_gptmixcliport5_new_pickplace_demo10_distant.sh new file mode 100644 index 0000000000000000000000000000000000000000..e3df14f4f104501c8a2b0ad23b50772a41432f25 --- /dev/null +++ b/scripts/metascripts/train5_gptmixcliport5_new_pickplace_demo10_distant.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_goal_demo10.sh data \ + "[stack-block-pyramid,align-box-corner,put-block-in-bowl,packing-boxes,block-insertion,manipulating-two-ropes,color-coordinated-sphere-insertion,assemble-single-car,connect-boxes-with-rope,move-piles-along-line,push-piles-into-letter]" \ + "[stack-block-pyramid,put-block-in-bowl,align-box-corner,packing-boxes,block-insertion]" \ + gpt5_mixcliport5_task_new_distant diff --git a/scripts/metascripts/train7_gptmixcliport3_new_pickplace.sh b/scripts/metascripts/train7_gptmixcliport3_new_pickplace.sh new file mode 100644 index 0000000000000000000000000000000000000000..d59dd812e719db4ce269c007a623f5b622a6444c --- /dev/null +++ b/scripts/metascripts/train7_gptmixcliport3_new_pickplace.sh @@ -0,0 +1,13 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} +now=$(date "+%Y-%m-%d_%H-%M-%S") + + +sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \ + "[stack-block-pyramid,put-block-in-bowl,color-coordinated-sphere-insertion,rainbow-stack,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[stack-block-pyramid,put-block-in-bowl]" \ + train7_gpt3_mixcliport3_task_new_demo50_${now} \ No newline at end of file diff --git a/scripts/metascripts_finetuning/gen10_build_car.sh b/scripts/metascripts_finetuning/gen10_build_car.sh new file mode 100644 index 0000000000000000000000000000000000000000..ee42ef4ef8e66b94b51a2015d407b4f550c63f04 --- /dev/null +++ b/scripts/metascripts_finetuning/gen10_build_car.sh @@ -0,0 +1,10 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + + +sh scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh data "[align-rope,sweeping-piles,align-box-corner,towers-of-hanoi-seq-seen-colors,assembling-kits-seq-seen,block-insertion,palletizing-boxes,place-red-in-green,manipulating-rope,packing-boxes]" "[build-car]" 10taskgen_unrelated_finetune + +sh scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh data "[build-two-circles,build-wheel,build-bridge,towers-of-hanoi-seq-seen-colors,stack-block-pyramid,construct-corner-blocks,create-pyramid-blocks-and-container,palletizing-boxes,assemble-single-car,rainbow-stack]" "[build-car]" 10taskgen_related_finetune diff --git a/scripts/metascripts_finetuning/gen3_build_car.sh b/scripts/metascripts_finetuning/gen3_build_car.sh new file mode 100644 index 0000000000000000000000000000000000000000..48d9aafe338dce2f239a0747c1a0f305f92aea28 --- /dev/null +++ b/scripts/metascripts_finetuning/gen3_build_car.sh @@ -0,0 +1,9 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +sh scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh data "[align-rope,sweeping-piles,align-box-corner]" "[build-car]" 3taskgen_unrelated_finetune + +sh scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh data "[build-two-circles,build-wheel,build-bridge]" "[build-car]" 3taskgen_related_finetune diff --git a/scripts/metascripts_finetuning/gen5_build_car.sh b/scripts/metascripts_finetuning/gen5_build_car.sh new file mode 100644 index 0000000000000000000000000000000000000000..b0a5302919b8a57d8c6c589a2e01af8c975fcd16 --- /dev/null +++ b/scripts/metascripts_finetuning/gen5_build_car.sh @@ -0,0 +1,10 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + + +sh scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh data "[align-rope,sweeping-piles,align-box-corner,towers-of-hanoi-seq-seen-colors,assembling-kits-seq-seen]" "[build-car]" 5taskgen_unrelated_finetune + +sh scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh data "[build-two-circles,build-wheel,build-bridge,towers-of-hanoi-seq-seen-colors,stack-block-pyramid]" "[build-car]" 5taskgen_related_finetune diff --git a/scripts/metascripts_finetuning/pretrain0_finetune_2.sh b/scripts/metascripts_finetuning/pretrain0_finetune_2.sh new file mode 100644 index 0000000000000000000000000000000000000000..b719b40eb34812e1a637316aefd4ba0d0c7c20e0 --- /dev/null +++ b/scripts/metascripts_finetuning/pretrain0_finetune_2.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh data \ + "[]" \ + "[place-red-in-green,stack-block-pyramid]" \ + gpt0_mixcliport2_finetune diff --git a/scripts/metascripts_finetuning/pretrain10_finetune_2.sh b/scripts/metascripts_finetuning/pretrain10_finetune_2.sh new file mode 100644 index 0000000000000000000000000000000000000000..c06b833772fe1368673e7a3c854edaf5f7b34b53 --- /dev/null +++ b/scripts/metascripts_finetuning/pretrain10_finetune_2.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh data \ + "[color_linked_ball_bowl_ordering,color_specific_container_fill,insert_blocks_into_fixture,sort_insert_color_coordinated_blocks,color_ordered_blocks_on_pallet,color-coordinated-sphere-insertion,rainbow-stack,put-block-in-bowl,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[stack-block-pyramid,put-block-in-bowl,align-box-corner,packing-boxes,block-insertion]" \ + gpt10_mixcliport2_finetune diff --git a/scripts/metascripts_finetuning/pretrain20_finetune_2.sh b/scripts/metascripts_finetuning/pretrain20_finetune_2.sh new file mode 100644 index 0000000000000000000000000000000000000000..9c75e0893ecd17bca59ea771eab11af506792cbd --- /dev/null +++ b/scripts/metascripts_finetuning/pretrain20_finetune_2.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh data \ + "[color_linked_ball_bowl_ordering,color_specific_container_fill,insert_blocks_into_fixture,sort_insert_color_coordinated_blocks,color_ordered_blocks_on_pallet,color-coordinated-sphere-insertion,rainbow-stack,put-block-in-bowl,vertical-insertion-blocks,stack-blocks-in-container,'Four-corner-pyramid-challenge','create-pyramid-with-color-coded-ells','align-balls-in-colored-zones','construct-corner-blocks','color-linked-ball-bowl-ordering','create-pyramid-blocks-and-container','color-specific-container-fill','color-ordered-container-arrangement','pyramid-blocks-assemble']" \ + "[stack-block-pyramid,put-block-in-bowl,align-box-corner,packing-boxes,block-insertion]" \ + gpt20_mixcliport2_finetune diff --git a/scripts/metascripts_finetuning/pretrain3_finetune_2.sh b/scripts/metascripts_finetuning/pretrain3_finetune_2.sh new file mode 100644 index 0000000000000000000000000000000000000000..b48cc7b091f1c898809312cbd1d9c3bbc191f547 --- /dev/null +++ b/scripts/metascripts_finetuning/pretrain3_finetune_2.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh data \ + "[color-coordinated-sphere-insertion,rainbow-stack,put-block-in-bowl]" \ + "[place-red-in-green,stack-block-pyramid]" \ + gpt3_mixcliport2_finetune diff --git a/scripts/metascripts_finetuning/pretrain5_finetune_2.sh b/scripts/metascripts_finetuning/pretrain5_finetune_2.sh new file mode 100644 index 0000000000000000000000000000000000000000..40c6e3bcb30c02473ce4d6bd6807642bb06be273 --- /dev/null +++ b/scripts/metascripts_finetuning/pretrain5_finetune_2.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive +STEPS=${1-'50000'} + + +sh scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh data \ + "[color-coordinated-sphere-insertion,rainbow-stack,put-block-in-bowl,vertical-insertion-blocks,stack-blocks-in-container]" \ + "[place-red-in-green,stack-block-pyramid]" \ + gpt5_mixcliport2_finetune diff --git a/scripts/quickstart_download.sh b/scripts/quickstart_download.sh new file mode 100644 index 0000000000000000000000000000000000000000..a121c574c8978839a044669d10a88e226cb1f666 --- /dev/null +++ b/scripts/quickstart_download.sh @@ -0,0 +1,3 @@ +wget https://github.com/cliport/cliport/releases/download/v1.0.0/cliport_quickstart.zip +unzip cliport_quickstart.zip +rm cliport_quickstart.zip \ No newline at end of file diff --git a/scripts/regenerate_gpt_datasets.sh b/scripts/regenerate_gpt_datasets.sh new file mode 100644 index 0000000000000000000000000000000000000000..e4da318dd92eca0af03771320d3831db08d340c7 --- /dev/null +++ b/scripts/regenerate_gpt_datasets.sh @@ -0,0 +1,31 @@ +#!/bin/bash + +DATA_DIR=$1 +TASK=$2 +DISP=False + +echo "Generating dataset... Folder: $DATA_DIR" + +# sh scripts/generate_gpt_datasets.sh data "align-rope assembling-kits-seq-seen-colors assembling-kits-seq-unseen-colors packing-shapes packing-boxes-pairs-seen-colors packing-boxes-pairs-unseen-colors packing-seen-google-objects-seq packing-unseen-google-objects-seq packing-seen-google-objects-group packing-unseen-google-objects-group put-block-in-bowl-seen-colors put-block-in-bowl-unseen-colors stack-block-pyramid-seq-seen-colors stack-block-pyramid-seq-unseen-colors separating-piles-seen-colors separating-piles-unseen-colors towers-of-hanoi-seq-seen-colors towers-of-hanoi-seq-unseen-colors +# sh scripts/generate_gpt_datasets.sh data "assemble-single-car stack-color-coordinated-blocks color-structured-block-tower insert-blocks-into-fixture construct-corner-building colored-cylinder-in-square color-coordinated-block-tower build-house align-pair-colored-blocks-along-line insert-sphere-into-container build-wheel build-two-circles build-car build-bridge manipulating-two-ropes rainbow-stack mix-piles stack-blocks-in-container" +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks + +# LANG_TASKS='align-rope assembling-kits-seq-seen-colors' +trap "kill 0" SIGINT + +LANG_TASKS=$2 + +for task in $LANG_TASKS + do + python cliport/demos.py n=200 task=$task mode=train data_dir=$DATA_DIR disp=$DISP record.save_video=False +regenerate_data=True & + python cliport/demos.py n=50 task=$task mode=val data_dir=$DATA_DIR disp=$DISP record.save_video=False +regenerate_data=True & + python cliport/demos.py n=100 task=$task mode=test data_dir=$DATA_DIR disp=$DISP record.save_video=False +regenerate_data=True & + done +wait + +echo "Finished Language Tasks." + + diff --git a/scripts/supercloud/run_interactive_script.sh b/scripts/supercloud/run_interactive_script.sh new file mode 100644 index 0000000000000000000000000000000000000000..6c4b8291cd2cb8e8c0dd8afe027fd129e0ca1b7f --- /dev/null +++ b/scripts/supercloud/run_interactive_script.sh @@ -0,0 +1,7 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +CMD=$1 diff --git a/scripts/supercloud/run_job_script.sh b/scripts/supercloud/run_job_script.sh new file mode 100644 index 0000000000000000000000000000000000000000..0c66194297d55f1130ea0fb9ff487790591d3262 --- /dev/null +++ b/scripts/supercloud/run_job_script.sh @@ -0,0 +1,7 @@ +#!/bin/bash +#SBATCH -c 10 +#SBATCH -n 1 +#SBATCH -o logs/%j.out +#SBATCH --exclusive + +eval $CMD \ No newline at end of file diff --git a/scripts/test_all_singletask.sh b/scripts/test_all_singletask.sh new file mode 100644 index 0000000000000000000000000000000000000000..bcca5fd6b4b72e126764b5638940ea384158a40a --- /dev/null +++ b/scripts/test_all_singletask.sh @@ -0,0 +1 @@ +sh scripts/test_singletask.sh data "align-rope assembling-kits-seq palletizing-boxes towers-of-hanoi assembling-kits align-box-corner manipulating-rope packing-boxes place-red-in-green put-block-in-bowl task packing-boxes-pairs sweeping-piles separating-piles stack-block-pyramid-seq towers-of-hanoi-seq packing-shapes stack-block-pyramid block-insertion packing-google-objects color-coordinated-ball-stacking cylinder-ring-stack stack-three-layer-red-wall color-ordered-blocks-on-pallet build-cylinder-structure build-bridge pyramid-blocks-assemble sort-and-assemble-block-castle stack-blocks-in-container block-on-cylinder-on-pallet corner-sort-cylinders align-pair-colored-blocks-along-line color-specific-container-fill colored-cylinder-in-square construct-colorful-arch color-coordinated-ball-insertion insert-sphere-into-container build-wheel color-coordinated-sphere-and-cylinder-assembly push-piles-into-letter color-coordinated-zone-stacking create-pyramid-with-color-coded-ells color-coordinated-arch-construction color-coordinated-sphere-insertion put-kit-in-bowl move-piles-along-line insert-ell-along-square-path multi-level-block-construction build-car color-coded-blocks-on-corner move-kit-from-zone-to-cylinder multi-level-insertion-and-zone-matching color-coordinated-insertion ball-in-bowl-obstacle-course-new colorful-block-tower-on-cylinder-base manipulating-two-ropes construct-corner-building color-coordinated-block-bridge ball-on-box-on-container color-sequenced-sphere-placement construct-corner-blocks sort-insert-color-coordinated-blocks color-ordered-container-arrangement symmetric-block-bridge-construction connect-boxes-with-rope align-rope-cross-zone vertical-insertion-blocks cylinder-stand-alignment color-coordinated-zone-arrangement insert-blocks-lineup create-pyramid-blocks-and-container mix-piles put-blues-around-red color-sequenced-pyramid-packing put-blocks-between-zones color-coordinated-cylinder-pyramid sweep-and-sort-blocks multi-level-pyramid-construction guided-block-path rainbow-stack color-ordered-insertion-new mixed-color-block-barrier-insertion color-coordinated-block-shifting align-balls-in-colored-zones multicolor-block-bridge sequential-insertion-and-stacking move-bowl-from-pallet-to-corner insertion-in-color-sequenced-zones align-spheres-in-colored-zones color-blocks-in-cylinder-maze color-coordinated-sphere-on-pallet-pyramid sort-and-stack-clr-blocks corner-block-challenge sequential-block-insertion place-blue-on-line-ends kit-in-bowl-in-zone align-rope-along-line sphere-container-color-match stack-color-coordinated-blocks assemble-single-car color-structured-block-tower color-sorted-block-race align-balls-in-colored-boxes color-coordinated-cylinder-ball-match build-house align-cylinders-in-zones sphere-align-stand ball-in-bowl-obstacle-course color-coordinated-block-tower color-sorted-container-stack color-coordinated-cylinder-stand-assembly color-ordered-insertion block-pyramid-with-limited-space color-cued-ball-corner-sorting sorting-blocks-into-pallets place-ball-in-elevated-bowl Four-corner-pyramid-challenge colored-balls-sorting-in-corner color-coordinated-box-ball-matching color-coordinated-cylinder-tower ball-sorting-with-blocks-barrier build-two-circles cylinder-balancing-and-placement" \ No newline at end of file diff --git a/scripts/test_singletask.sh b/scripts/test_singletask.sh new file mode 100644 index 0000000000000000000000000000000000000000..a72104f4a6e086d93080a0259250fde3242a23c8 --- /dev/null +++ b/scripts/test_singletask.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +DATA_DIR=$1 +TASK=$2 +DISP=False + +echo "Training dataset... Folder: $DATA_DIR Task $TASK" + +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +trap "kill 0" SIGINT +LANG_TASKS=$2 + + +for task in $LANG_TASKS + do + # Generate data + # TEST + python cliport/eval.py eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=200 \ + checkpoint_type=test_best \ + exp_folder=exps/exps-singletask \ + update_results=True + done + +python notebooks/print_results.py -r=exps/exps-singletask + +echo "Finished Training." diff --git a/scripts/train_all_single_task.sh b/scripts/train_all_single_task.sh new file mode 100644 index 0000000000000000000000000000000000000000..52c4ac073dce10bc587e26dcf61eba7eba58fb31 --- /dev/null +++ b/scripts/train_all_single_task.sh @@ -0,0 +1,112 @@ +sh scripts/train_test_single_task.sh data align-rope +sh scripts/train_test_single_task.sh data assembling-kits-seq +sh scripts/train_test_single_task.sh data palletizing-boxes +sh scripts/train_test_single_task.sh data towers-of-hanoi +sh scripts/train_test_single_task.sh data assembling-kits +sh scripts/train_test_single_task.sh data align-box-corner +sh scripts/train_test_single_task.sh data manipulating-rope +sh scripts/train_test_single_task.sh data packing-boxes +sh scripts/train_test_single_task.sh data place-red-in-green +sh scripts/train_test_single_task.sh data put-block-in-bowl +sh scripts/train_test_single_task.sh data task +sh scripts/train_test_single_task.sh data packing-boxes-pairs +sh scripts/train_test_single_task.sh data sweeping-piles +sh scripts/train_test_single_task.sh data separating-piles +sh scripts/train_test_single_task.sh data stack-block-pyramid-seq +sh scripts/train_test_single_task.sh data towers-of-hanoi-seq +sh scripts/train_test_single_task.sh data packing-shapes +sh scripts/train_test_single_task.sh data stack-block-pyramid +sh scripts/train_test_single_task.sh data block-insertion +sh scripts/train_test_single_task.sh data packing-google-objects +sh scripts/train_test_single_task.sh data color-coordinated-ball-stacking +sh scripts/train_test_single_task.sh data cylinder-ring-stack +sh scripts/train_test_single_task.sh data color-ordered-blocks-on-pallet +sh scripts/train_test_single_task.sh data build-cylinder-structure +sh scripts/train_test_single_task.sh data build-bridge +sh scripts/train_test_single_task.sh data pyramid-blocks-assemble +sh scripts/train_test_single_task.sh data sort-and-assemble-block-castle +sh scripts/train_test_single_task.sh data stack-blocks-in-container +sh scripts/train_test_single_task.sh data corner-sort-cylinders +sh scripts/train_test_single_task.sh data align-pair-colored-blocks-along-line +sh scripts/train_test_single_task.sh data color-specific-container-fill +sh scripts/train_test_single_task.sh data colored-cylinder-in-square +sh scripts/train_test_single_task.sh data construct-colorful-arch +sh scripts/train_test_single_task.sh data color-coordinated-ball-insertion +sh scripts/train_test_single_task.sh data insert-sphere-into-container +sh scripts/train_test_single_task.sh data build-wheel +sh scripts/train_test_single_task.sh data color-coordinated-sphere-and-cylinder-assembly +sh scripts/train_test_single_task.sh data push-piles-into-letter +sh scripts/train_test_single_task.sh data color-coordinated-zone-stacking +sh scripts/train_test_single_task.sh data create-pyramid-with-color-coded-ells +sh scripts/train_test_single_task.sh data color-coordinated-arch-construction +sh scripts/train_test_single_task.sh data color-coordinated-sphere-insertion +sh scripts/train_test_single_task.sh data move-piles-along-line +sh scripts/train_test_single_task.sh data insert-ell-along-square-path +sh scripts/train_test_single_task.sh data multi-level-block-construction +sh scripts/train_test_single_task.sh data build-car +sh scripts/train_test_single_task.sh data color-coded-blocks-on-corner +sh scripts/train_test_single_task.sh data multi-level-insertion-and-zone-matching +sh scripts/train_test_single_task.sh data color-coordinated-insertion +sh scripts/train_test_single_task.sh data triangle-block-arrangement +sh scripts/train_test_single_task.sh data ball-in-bowl-obstacle-course-new +sh scripts/train_test_single_task.sh data colorful-block-tower-on-cylinder-base +sh scripts/train_test_single_task.sh data manipulating-two-ropes +sh scripts/train_test_single_task.sh data construct-corner-building +sh scripts/train_test_single_task.sh data color-coordinated-block-bridge +sh scripts/train_test_single_task.sh data color-sequenced-sphere-placement +sh scripts/train_test_single_task.sh data construct-corner-blocks +sh scripts/train_test_single_task.sh data sort-insert-color-coordinated-blocks +sh scripts/train_test_single_task.sh data color-ordered-container-arrangement +sh scripts/train_test_single_task.sh data symmetric-block-bridge-construction +sh scripts/train_test_single_task.sh data connect-boxes-with-rope +sh scripts/train_test_single_task.sh data vertical-insertion-blocks +sh scripts/train_test_single_task.sh data cylinder-stand-alignment +sh scripts/train_test_single_task.sh data color-coordinated-zone-arrangement +sh scripts/train_test_single_task.sh data insert-blocks-lineup +sh scripts/train_test_single_task.sh data create-pyramid-blocks-and-container +sh scripts/train_test_single_task.sh data mix-piles +sh scripts/train_test_single_task.sh data color-sequenced-pyramid-packing +sh scripts/train_test_single_task.sh data color-coordinated-cylinder-pyramid +sh scripts/train_test_single_task.sh data sweep-and-sort-blocks +sh scripts/train_test_single_task.sh data multi-level-pyramid-construction +sh scripts/train_test_single_task.sh data guided-block-path +sh scripts/train_test_single_task.sh data rainbow-stack +sh scripts/train_test_single_task.sh data color-ordered-insertion-new +sh scripts/train_test_single_task.sh data mixed-color-block-barrier-insertion +sh scripts/train_test_single_task.sh data color-coordinated-block-shifting +sh scripts/train_test_single_task.sh data align-balls-in-colored-zones +sh scripts/train_test_single_task.sh data multicolor-block-bridge +sh scripts/train_test_single_task.sh data sequential-insertion-and-stacking +sh scripts/train_test_single_task.sh data insertion-in-color-sequenced-zones +sh scripts/train_test_single_task.sh data align-spheres-in-colored-zones +sh scripts/train_test_single_task.sh data color-blocks-in-cylinder-maze +sh scripts/train_test_single_task.sh data color-coordinated-sphere-on-pallet-pyramid +sh scripts/train_test_single_task.sh data sort-and-stack-clr-blocks +sh scripts/train_test_single_task.sh data corner-block-challenge +sh scripts/train_test_single_task.sh data sequential-block-insertion +sh scripts/train_test_single_task.sh data sphere-container-color-match +sh scripts/train_test_single_task.sh data stack-color-coordinated-blocks +sh scripts/train_test_single_task.sh data assemble-single-car +sh scripts/train_test_single_task.sh data color-structured-block-tower +sh scripts/train_test_single_task.sh data color-sorted-block-race +sh scripts/train_test_single_task.sh data align-balls-in-colored-boxes +sh scripts/train_test_single_task.sh data color-coordinated-cylinder-ball-match +sh scripts/train_test_single_task.sh data build-house +sh scripts/train_test_single_task.sh data align-cylinders-in-zones +sh scripts/train_test_single_task.sh data sphere-align-stand +sh scripts/train_test_single_task.sh data ball-in-bowl-obstacle-course +sh scripts/train_test_single_task.sh data color-coordinated-block-tower +sh scripts/train_test_single_task.sh data color-sorted-container-stack +sh scripts/train_test_single_task.sh data color-coordinated-cylinder-stand-assembly +sh scripts/train_test_single_task.sh data color-ordered-insertion +sh scripts/train_test_single_task.sh data block-pyramid-with-limited-space +sh scripts/train_test_single_task.sh data color-cued-ball-corner-sorting +sh scripts/train_test_single_task.sh data sorting-blocks-into-pallets +sh scripts/train_test_single_task.sh data place-ball-in-elevated-bowl +sh scripts/train_test_single_task.sh data Four-corner-pyramid-challenge +sh scripts/train_test_single_task.sh data colored-balls-sorting-in-corner +sh scripts/train_test_single_task.sh data color-coordinated-box-ball-matching +sh scripts/train_test_single_task.sh data color-coordinated-cylinder-tower +sh scripts/train_test_single_task.sh data ball-sorting-with-blocks-barrier +sh scripts/train_test_single_task.sh data build-two-circles +sh scripts/train_test_single_task.sh data cylinder-balancing-and-placement \ No newline at end of file diff --git a/scripts/train_test_single_task.sh b/scripts/train_test_single_task.sh new file mode 100644 index 0000000000000000000000000000000000000000..5df9baca576b1ab4bda2b431fcac0cba86c8f1f6 --- /dev/null +++ b/scripts/train_test_single_task.sh @@ -0,0 +1,55 @@ +#!/bin/bash + +DATA_DIR=$1 +TASK=$2 +DISP=False + +echo "Training dataset... Folder: $DATA_DIR Task $TASK" + +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +trap "kill 0" SIGINT +LANG_TASKS=$2 + + +for task in $LANG_TASKS + do + # Generate data + bash scripts/regenerate_gpt_datasets.sh data $task + + # TRAIN + python cliport/train.py train.task=$task \ + train.agent=cliport \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=200 \ + train.n_steps=5000 \ + train.exp_folder=exps/exps-singletask \ + dataset.cache=True + + # EVAL + # python cliport/eval.py eval_task=$task \ + # agent=cliport \ + # mode=val \ + # n_demos=100 \ + # train_demos=100 \ + # checkpoint_type=val_missing \ + # exp_folder=exps + + # TEST + python cliport/eval.py eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=200 \ + checkpoint_type=test_best \ + exp_folder=exps/exps-singletask \ + update_results=True + done + +python notebooks/print_results.py -r=exps/exps-singletask + +echo "Finished Training." diff --git a/scripts/train_test_single_task_sim2real.sh b/scripts/train_test_single_task_sim2real.sh new file mode 100644 index 0000000000000000000000000000000000000000..17e05a97f06e4f80bbfaed464f1446ef9a6f6183 --- /dev/null +++ b/scripts/train_test_single_task_sim2real.sh @@ -0,0 +1,49 @@ +#!/bin/bash + +DATA_DIR=$1 +TASK=$2 +DISP=False + +echo "Training dataset... Folder: $DATA_DIR Task $TASK" + +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +trap "kill 0" SIGINT +LANG_TASKS=$2 + + +for task in $LANG_TASKS + do + # Generate data + bash scripts/generate_gpt_datasets.sh data $task + + # TRAIN + python cliport/train.py train.task=$task \ + train.agent=cliport \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=200 \ + train.n_steps=5000 \ + train.exp_folder=exps/exps-singletask-sim2real \ + dataset.cache=True \ + train.data_augmentation=True + + + + # TEST + python cliport/eval.py eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=200 \ + checkpoint_type=test_best \ + exp_folder=exps/exps-singletask-sim2real \ + update_results=True + done + +python notebooks/print_results.py -r=exps/exps-singletask + +echo "Finished Training." diff --git a/scripts/train_test_single_task_singlebatch.sh b/scripts/train_test_single_task_singlebatch.sh new file mode 100644 index 0000000000000000000000000000000000000000..28249d6615bf95188a4c943396b63a962b293c86 --- /dev/null +++ b/scripts/train_test_single_task_singlebatch.sh @@ -0,0 +1,58 @@ +#!/bin/bash + +DATA_DIR=$1 +TASK=$2 +DISP=False + +echo "Training dataset... Folder: $DATA_DIR Task $TASK" + +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +trap "kill 0" SIGINT +LANG_TASKS=$2 + + +for task in $LANG_TASKS + do + # Generate data + bash scripts/generate_gpt_datasets.sh data $task + + # TRAIN + python cliport/train.py train.task=$task \ + train.agent=cliport \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=200 \ + train.n_steps=10000 \ + train.exp_folder=exps/exps-singletask \ + dataset.cache=True \ + train.batch_size=1 \ + train.log=True + + # EVAL + # python cliport/eval.py eval_task=$task \ + # agent=cliport \ + # mode=val \ + # n_demos=100 \ + # train_demos=100 \ + # checkpoint_type=val_missing \ + # exp_folder=exps + + # TEST + python cliport/eval.py eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=200 \ + checkpoint_type=test_best \ + exp_folder=exps/exps-singletask \ + update_results=True \ + disp=True + done + +python notebooks/print_results.py -r=exps-singletask + +echo "Finished Training." diff --git a/scripts/train_test_single_task_statistics.sh b/scripts/train_test_single_task_statistics.sh new file mode 100644 index 0000000000000000000000000000000000000000..0c34e00a5d7e425c2416e2eb80107baa7420bfe4 --- /dev/null +++ b/scripts/train_test_single_task_statistics.sh @@ -0,0 +1,58 @@ +#!/bin/bash + +DATA_DIR=$1 +TASK=$2 +DISP=False + +echo "Training dataset... Folder: $DATA_DIR Task $TASK" + +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +trap "kill 0" SIGINT +LANG_TASKS=$2 + + +for task in $LANG_TASKS + do + # Generate data + bash scripts/regenerate_gpt_datasets.sh data $task + + # TRAIN + python cliport/train.py train.task=$task \ + train.agent=cliport \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=200 \ + train.n_steps=5000 \ + train.exp_folder=exps/exps-singletask \ + dataset.cache=True \ + train.batch=2 \ + record.save_video=False + + # EVAL + # python cliport/eval.py eval_task=$task \ + # agent=cliport \ + # mode=val \ + # n_demos=100 \ + # train_demos=100 \ + # checkpoint_type=val_missing \ + # exp_folder=exps + + # TEST + python cliport/eval.py eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=200 \ + checkpoint_type=test_best \ + exp_folder=exps/exps-singletask \ + update_results=True \ + record.save_video=False + done + +python notebooks/print_results.py -r=exps/exps-singletask + +echo "Finished Training." diff --git a/scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh b/scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh new file mode 100644 index 0000000000000000000000000000000000000000..82c556954a88623308d6c27f9e1cd3acce4dfe6f --- /dev/null +++ b/scripts/traintest_scripts/train_test_multi_task_finetune_goal.sh @@ -0,0 +1,85 @@ +#!/bin/bash + +DATA_DIR=$1 +TRAINTASK=${2-'[rainbow-stack,bowl-ball-placement]'} +TESTTASK=${3-'[rainbow-stack,bowl-ball-placement]'} +TASKNAME=${4-'mix-two'} +STEPS=${5-'10000'} +DISP=False + +echo "Training multi-task dataset... Folder: $DATA_DIR Task $TRAINTASK" + +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +# [align-rope,assembling-kits-seq-seen-colors,assembling-kits-seq-unseen-colors,packing-shapes,stack-block-pyramid-seq-unseen-colors, +# separating-piles-seen-colors,separating-piles-unseen-colors,towers-of-hanoi-seq-seen-colors,towers-of-hanoi-seq-unseen-colors] + +# example: sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" 6taskindomain +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" "[towers-of-hanoi]" 6taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner]" "[towers-of-hanoi]" 3taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope]" "[towers-of-hanoi]" 1taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" "[towers-of-hanoi]" 10taskgen + +trap "kill 0" SIGINT + +python cliport/train.py train.task=$TRAINTASK \ + train.agent=cliport \ + train.model_task=$TASKNAME \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=200 \ + train.n_steps=$STEPS \ + dataset.cache=True \ + train.exp_folder=exps/exp-$TASKNAME \ + dataset.type=multi \ + train.load_from_last_ckpt=False + + +# finetuning. todo: check if model loading is done properly. +# check if smaller lr is necessary. +python cliport/train.py train.task=$TESTTASK \ + train.agent=cliport \ + train.model_task=$TASKNAME \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=10 \ + train.lr=1e-5 \ + dataset.cache=True \ + train.exp_folder=exps/exp-$TASKNAME \ + dataset.type=multi + + + +# Convert Python list to Bash array +bash_array=$(python3 -c "import sys; print(' '.join((sys.argv[1])[1:-1].split(',')))" "$TESTTASK") + +# Convert the space-separated string to a bash array +echo "Testing multi-task dataset... Folder: $DATA_DIR Task $TESTTASK" + + +for task in $bash_array + do + echo "Testing $task" + # TEST + bash scripts/generate_gpt_datasets.sh data $task + + python cliport/eval.py model_task=$TASKNAME \ + eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=200 \ + checkpoint_type=test_best \ + type=single \ + exp_folder=exps/exp-$TASKNAME \ + update_results=True & + done +wait + +python notebooks/print_results.py -r=exps/exp-$TASKNAME + +echo "Finished Training." \ No newline at end of file diff --git a/scripts/traintest_scripts/train_test_multi_task_goal.sh b/scripts/traintest_scripts/train_test_multi_task_goal.sh new file mode 100644 index 0000000000000000000000000000000000000000..43c4a348c0712b07dfd6e679a09f5a73349d0752 --- /dev/null +++ b/scripts/traintest_scripts/train_test_multi_task_goal.sh @@ -0,0 +1,70 @@ +#!/bin/bash + +DATA_DIR=$1 +TRAINTASK=${2-'[rainbow-stack,bowl-ball-placement]'} +TESTTASK=${3-'[rainbow-stack,bowl-ball-placement]'} +TASKNAME=${4-'mix-two'} +STEPS=${5-'10000'} + +DISP=False + +echo "Training multi-task dataset... Folder: $DATA_DIR Task $TRAINTASK" + +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +# [align-rope,assembling-kits-seq-seen-colors,assembling-kits-seq-unseen-colors,packing-shapes,stack-block-pyramid-seq-unseen-colors, +# separating-piles-seen-colors,separating-piles-unseen-colors,towers-of-hanoi-seq-seen-colors,towers-of-hanoi-seq-unseen-colors] + +# example: sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" 6taskindomain +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" "[towers-of-hanoi]" 6taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner]" "[towers-of-hanoi]" 3taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope]" "[towers-of-hanoi]" 1taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" "[towers-of-hanoi]" 10taskgen + +trap "kill 0" SIGINT + +python cliport/train.py train.task=$TRAINTASK \ + train.agent=cliport \ + train.model_task=$TASKNAME \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=50 \ + train.n_steps=${STEPS} \ + dataset.cache=True \ + train.exp_folder=exps/exp-$TASKNAME \ + dataset.type=multi \ + train.load_from_last_ckpt=False + + +# Convert Python list to Bash array +bash_array=$(python3 -c "import sys; print(' '.join((sys.argv[1])[1:-1].split(',')))" "$TESTTASK") + +# Convert the space-separated string to a bash array +echo "Testing multi-task dataset... Folder: $DATA_DIR Task $TESTTASK" + + +for task in $bash_array + do + echo "Testing $task" + # TEST + # bash scripts/generate_gpt_datasets.sh data $task + + python cliport/eval.py model_task=$TASKNAME \ + eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=50 \ + checkpoint_type=test_best \ + type=single \ + exp_folder=exps/exp-$TASKNAME \ + update_results=True & + done +wait + +python notebooks/print_results.py -r=exps/exp-$TASKNAME + +echo "Finished Training." \ No newline at end of file diff --git a/scripts/traintest_scripts/train_test_multi_task_goal_demo10.sh b/scripts/traintest_scripts/train_test_multi_task_goal_demo10.sh new file mode 100644 index 0000000000000000000000000000000000000000..aebc7e8f9ac6b536d765fc9f5c566871811f9994 --- /dev/null +++ b/scripts/traintest_scripts/train_test_multi_task_goal_demo10.sh @@ -0,0 +1,74 @@ +#!/bin/bash + +DATA_DIR=$1 +TRAINTASK=${2-'[rainbow-stack,bowl-ball-placement]'} +TESTTASK=${3-'[rainbow-stack,bowl-ball-placement]'} +TASKNAME=${4-'mix-two'} +STEPS=${5-'10000'} + +DISP=False + +echo "Training multi-task dataset... Folder: $DATA_DIR Task $TRAINTASK" + +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +# [align-rope,assembling-kits-seq-seen-colors,assembling-kits-seq-unseen-colors,packing-shapes,stack-block-pyramid-seq-unseen-colors, +# separating-piles-seen-colors,separating-piles-unseen-colors,towers-of-hanoi-seq-seen-colors,towers-of-hanoi-seq-unseen-colors] + +# example: sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" 6taskindomain +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" "[towers-of-hanoi]" 6taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner]" "[towers-of-hanoi]" 3taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope]" "[towers-of-hanoi]" 1taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" "[towers-of-hanoi]" 10taskgen + +trap "kill 0" SIGINT + +python cliport/train.py train.task=$TRAINTASK \ + train.agent=cliport \ + train.model_task=$TASKNAME \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=10 \ + train.n_steps=${STEPS} \ + dataset.cache=True \ + train.exp_folder=exps/exp-$TASKNAME-smaller \ + dataset.type=multi \ + train.load_from_last_ckpt=False \ + train.training_step_scale=500 # scale up training steps + + +# Convert Python list to Bash array + +bash_array=$(python3 -c "import sys; print(' '.join((sys.argv[1])[1:-1].split(',')))" "$TRAINTASK") + + +# Convert the space-separated string to a bash array +echo "Testing multi-task dataset... Folder: $DATA_DIR Task $TESTTASK" + + +for task in $bash_array + do + echo "Testing $task" + # TEST + + # bash scripts/generate_gpt_datasets.sh data $task + + python cliport/eval.py model_task=$TASKNAME \ + eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=10 \ + checkpoint_type=test_best \ + type=single \ + exp_folder=exps/exp-$TASKNAME-smaller \ + update_results=True & + done +wait + +python notebooks/print_results.py -r=exps/exp-$TASKNAME-smaller + +echo "Finished Training." \ No newline at end of file diff --git a/scripts/traintest_scripts/train_test_multi_task_goal_demo50.sh b/scripts/traintest_scripts/train_test_multi_task_goal_demo50.sh new file mode 100644 index 0000000000000000000000000000000000000000..14e59601e733180df5578942656f0073434d9a8c --- /dev/null +++ b/scripts/traintest_scripts/train_test_multi_task_goal_demo50.sh @@ -0,0 +1,71 @@ +#!/bin/bash + +DATA_DIR=$1 +TRAINTASK=${2-'[rainbow-stack,bowl-ball-placement]'} +TESTTASK=${3-'[rainbow-stack,bowl-ball-placement]'} +TASKNAME=${4-'mix-two'} +STEPS=${5-'10000'} + +DISP=False + +echo "Training multi-task dataset... Folder: $DATA_DIR Task $TRAINTASK" + +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +# [align-rope,assembling-kits-seq-seen-colors,assembling-kits-seq-unseen-colors,packing-shapes,stack-block-pyramid-seq-unseen-colors, +# separating-piles-seen-colors,separating-piles-unseen-colors,towers-of-hanoi-seq-seen-colors,towers-of-hanoi-seq-unseen-colors] + +# example: sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" 6taskindomain +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" "[towers-of-hanoi]" 6taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner]" "[towers-of-hanoi]" 3taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope]" "[towers-of-hanoi]" 1taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" "[towers-of-hanoi]" 10taskgen + +trap "kill 0" SIGINT + +python cliport/train.py train.task=$TRAINTASK \ + train.agent=cliport \ + train.model_task=$TASKNAME \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=50 \ + train.n_steps=${STEPS} \ + dataset.cache=True \ + train.exp_folder=exps/exp-$TASKNAME-smaller \ + dataset.type=multi \ + train.load_from_last_ckpt=False \ + train.training_step_scale=200 # scale up training steps + + +# Convert Python list to Bash array +bash_array=$(python3 -c "import sys; print(' '.join((sys.argv[1])[1:-1].split(',')))" "$TRAINTASK") + +# Convert the space-separated string to a bash array +echo "Testing multi-task dataset... Folder: $DATA_DIR Task $TRAINTASK" + + +for task in $bash_array + do + echo "Testing $task" + # TEST + # bash scripts/generate_gpt_datasets.sh data $task + + python cliport/eval.py model_task=$TASKNAME \ + eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=50 \ + checkpoint_type=test_best \ + type=single \ + exp_folder=exps/exp-$TASKNAME-smaller \ + update_results=True & + done +wait + +python notebooks/print_results.py -r=exps/exp-$TASKNAME-smaller + +echo "Finished Training." \ No newline at end of file diff --git a/scripts/traintest_scripts/train_test_multi_task_goal_small.sh b/scripts/traintest_scripts/train_test_multi_task_goal_small.sh new file mode 100644 index 0000000000000000000000000000000000000000..5a2fd6d956012f8e014fd90625c5a64e34bb3889 --- /dev/null +++ b/scripts/traintest_scripts/train_test_multi_task_goal_small.sh @@ -0,0 +1,70 @@ +#!/bin/bash + +DATA_DIR=$1 +TRAINTASK=${2-'[rainbow-stack,bowl-ball-placement]'} +TESTTASK=${3-'[rainbow-stack,bowl-ball-placement]'} +TASKNAME=${4-'mix-two'} +STEPS=${5-'10000'} + +DISP=False + +echo "Training multi-task dataset... Folder: $DATA_DIR Task $TRAINTASK" + +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +# [align-rope,assembling-kits-seq-seen-colors,assembling-kits-seq-unseen-colors,packing-shapes,stack-block-pyramid-seq-unseen-colors, +# separating-piles-seen-colors,separating-piles-unseen-colors,towers-of-hanoi-seq-seen-colors,towers-of-hanoi-seq-unseen-colors] + +# example: sh scripts/traintest_scripts/train_test_multi_task_indistribution.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" 6taskindomain +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" "[towers-of-hanoi]" 6taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner]" "[towers-of-hanoi]" 3taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope]" "[towers-of-hanoi]" 1taskgen +# sh scripts/traintest_scripts/train_test_multi_task_goal.sh data "[align-rope,sweeping-piles,align-box-corner,block-insertion,manipulating-rope,place-red-in-green]" "[towers-of-hanoi]" 10taskgen + +trap "kill 0" SIGINT +# increase the nums of epoch +python cliport/train.py train.task=$TRAINTASK \ + train.agent=cliport \ + train.model_task=$TASKNAME \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=50 \ + train.n_steps=${STEPS} \ + dataset.cache=True \ + train.exp_folder=exps/exp-$TASKNAME-small \ + dataset.type=multi \ + train.load_from_last_ckpt=False + + +# Convert Python list to Bash array +bash_array=$(python3 -c "import sys; print(' '.join((sys.argv[1])[1:-1].split(',')))" "$TESTTASK") + +# Convert the space-separated string to a bash array +echo "Testing multi-task dataset... Folder: $DATA_DIR Task $TESTTASK" + + +for task in $bash_array + do + echo "Testing $task" + # TEST + # bash scripts/generate_gpt_datasets.sh data $task + + python cliport/eval.py model_task=$TASKNAME \ + eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=50 \ + checkpoint_type=test_best \ + type=single \ + exp_folder=exps/exp-$TASKNAME-small \ + update_results=True & + done +wait + +python notebooks/print_results.py -r=exps/exp-$TASKNAME-small + +echo "Finished Training." \ No newline at end of file diff --git a/scripts/traintest_scripts/train_test_multi_task_indistribution.sh b/scripts/traintest_scripts/train_test_multi_task_indistribution.sh new file mode 100644 index 0000000000000000000000000000000000000000..2aa5c62485f29a6516f7c5a3451cb7d80c11159d --- /dev/null +++ b/scripts/traintest_scripts/train_test_multi_task_indistribution.sh @@ -0,0 +1,60 @@ +#!/bin/bash + +DATA_DIR=$1 +TRAINTASK=${2-'[rainbow-stack,bowl-ball-placement]'} +TASKNAME=${3-'mix-two'} +STEPS=${4-'20000'} + +DISP=False + +echo "Training multi-task dataset... Folder: $DATA_DIR Task $TASK" +trap "kill 0" SIGINT +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +# [align-rope,assembling-kits-seq-seen-colors,assembling-kits-seq-unseen-colors,packing-shapes] + + +# TRAIN +python cliport/train.py train.task=$TRAINTASK \ + train.agent=cliport \ + train.model_task=$TASKNAME \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=200 \ + train.n_steps=${STEPS} \ + dataset.cache=True \ + train.exp_folder=exps/exp-$TASKNAME \ + dataset.type=multi \ + train.load_from_last_ckpt=False + +# Convert Python list to Bash array +bash_array=$(python3 -c "import sys; print(' '.join((sys.argv[1])[1:-1].split(',')))" "$TRAINTASK") + +# Convert the space-separated string to a bash array +echo "Testing multi-task dataset... Folder: $DATA_DIR Task $TASK" + + +for task in $bash_array + do + echo "Testing $task" + # TEST + # bash scripts/generate_gpt_datasets.sh data $task + + python cliport/eval.py model_task=$TASKNAME \ + eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=200 \ + checkpoint_type=test_best \ + type=single \ + exp_folder=exps/exp-$TASKNAME \ + update_results=True & + done +wait + +python notebooks/print_results.py -r=exps/exp-$TASKNAME +echo "Finished Training." \ No newline at end of file diff --git a/scripts/traintest_scripts/train_test_multi_task_indistribution_bn.sh b/scripts/traintest_scripts/train_test_multi_task_indistribution_bn.sh new file mode 100644 index 0000000000000000000000000000000000000000..431482e3f11169b372e96f1e361479c922fa5060 --- /dev/null +++ b/scripts/traintest_scripts/train_test_multi_task_indistribution_bn.sh @@ -0,0 +1,62 @@ +#!/bin/bash + +DATA_DIR=$1 +TRAINTASK=${2-'[rainbow-stack,bowl-ball-placement]'} +TASKNAME=${3-'mix-two'} +STEPS=${4-'20000'} + +DISP=False + +echo "Training multi-task dataset... Folder: $DATA_DIR Task $TASK" +trap "kill 0" SIGINT +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +# [align-rope,assembling-kits-seq-seen-colors,assembling-kits-seq-unseen-colors,packing-shapes] + + +# TRAIN +python cliport/train.py train.task=$TRAINTASK \ + train.agent=cliport \ + train.model_task=$TASKNAME \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=200 \ + train.n_steps=${STEPS} \ + dataset.cache=True \ + train.exp_folder=exps/exp-$TASKNAME \ + dataset.type=multi \ + train.load_from_last_ckpt=False \ + train.batchnorm=True + +# Convert Python list to Bash array +bash_array=$(python3 -c "import sys; print(' '.join((sys.argv[1])[1:-1].split(',')))" "$TRAINTASK") + +# Convert the space-separated string to a bash array +echo "Testing multi-task dataset... Folder: $DATA_DIR Task $TASK" + + +for task in $bash_array + do + echo "Testing $task" + # TEST + bash scripts/generate_gpt_datasets.sh data $task + + python cliport/eval.py model_task=$TASKNAME \ + eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=200 \ + checkpoint_type=test_best \ + type=single \ + exp_folder=exps/exp-$TASKNAME \ + update_results=True \ + train.batchnorm=True & + done +wait + +python notebooks/print_results.py -r=exps/exp-$TASKNAME +echo "Finished Training." \ No newline at end of file diff --git a/scripts/traintest_scripts/train_test_multi_task_indistribution_small.sh b/scripts/traintest_scripts/train_test_multi_task_indistribution_small.sh new file mode 100644 index 0000000000000000000000000000000000000000..babc0cd1742005157b1bd42f3a397ae21c8ea0d2 --- /dev/null +++ b/scripts/traintest_scripts/train_test_multi_task_indistribution_small.sh @@ -0,0 +1,59 @@ +#!/bin/bash + +DATA_DIR=$1 +TRAINTASK=${2-'[rainbow-stack,bowl-ball-placement]'} +TASKNAME=${3-'mix-two'} +STEPS=${4-'20000'} + +DISP=False + +echo "Training multi-task dataset... Folder: $DATA_DIR Task $TASK" +trap "kill 0" SIGINT +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +# [align-rope,assembling-kits-seq-seen-colors,assembling-kits-seq-unseen-colors,packing-shapes] + + +# TRAIN +# python cliport/train.py train.task=$TRAINTASK \ +# train.agent=cliport \ +# train.model_task=$TASKNAME \ +# train.attn_stream_fusion_type=add \ +# train.trans_stream_fusion_type=conv \ +# train.lang_fusion_type=mult \ +# train.n_demos=50 \ +# train.n_steps=${STEPS} \ +# dataset.cache=True \ +# train.exp_folder=exps/exp-$TASKNAME-small \ +# dataset.type=multi \ +# train.load_from_last_ckpt=False + +# # Convert Python list to Bash array +bash_array=$(python3 -c "import sys; print(' '.join((sys.argv[1])[1:-1].split(',')))" "$TRAINTASK") + +# Convert the space-separated string to a bash array +echo "Testing multi-task dataset... Folder: $DATA_DIR Task $TASK" + +for task in $bash_array + do + echo "Testing $task" + # TEST + # bash scripts/generate_gpt_datasets.sh data $task + + python cliport/eval.py model_task=$TASKNAME \ + eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=50 \ + checkpoint_type=test_best \ + type=single \ + exp_folder=exps/exp-$TASKNAME-small \ + update_results=True & + done +wait + +python notebooks/print_results.py -r=exps/exp-$TASKNAME-small +echo "Finished Training." \ No newline at end of file diff --git a/scripts/traintest_scripts/train_test_multi_task_indistribution_smaller.sh b/scripts/traintest_scripts/train_test_multi_task_indistribution_smaller.sh new file mode 100644 index 0000000000000000000000000000000000000000..1a8d19dee770804f1b3cb9ebefbcb4fcaca91f7f --- /dev/null +++ b/scripts/traintest_scripts/train_test_multi_task_indistribution_smaller.sh @@ -0,0 +1,61 @@ +#!/bin/bash + +DATA_DIR=$1 +TRAINTASK=${2-'[rainbow-stack,bowl-ball-placement]'} +TASKNAME=${3-'mix-two'} +STEPS=${4-'20000'} + +DISP=False + +echo "Training multi-task dataset... Folder: $DATA_DIR Task $TASK" +trap "kill 0" SIGINT +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +# [align-rope,assembling-kits-seq-seen-colors,assembling-kits-seq-unseen-colors,packing-shapes] + + +# TRAIN +python cliport/train.py train.task=$TRAINTASK \ + train.agent=cliport \ + train.model_task=$TASKNAME \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=50 \ + train.n_steps=${STEPS} \ + dataset.cache=True \ + train.exp_folder=exps/exp-$TASKNAME-smaller \ + dataset.type=multi \ + train.load_from_last_ckpt=False \ + train.training_step_scale=300 + +# Convert Python list to Bash array +bash_array=$(python3 -c "import sys; print(' '.join((sys.argv[1])[1:-1].split(',')))" "$TRAINTASK") + +# Convert the space-separated string to a bash array +echo "Testing multi-task dataset... Folder: $DATA_DIR Task $TASK" + + +for task in $bash_array + do + echo "Testing $task" + # TEST + # bash scripts/generate_gpt_datasets.sh data $task + + python cliport/eval.py model_task=$TASKNAME \ + eval_task=$task \ + agent=cliport \ + mode=test \ + n_demos=100 \ + train_demos=10 \ + checkpoint_type=test_best \ + type=single \ + exp_folder=exps/exp-$TASKNAME-smaller \ + update_results=True & + done +wait + +python notebooks/print_results.py -r=exps/exp-$TASKNAME-smaller +echo "Finished Training." \ No newline at end of file diff --git a/scripts/traintest_scripts/train_test_single_task_indistribution.sh b/scripts/traintest_scripts/train_test_single_task_indistribution.sh new file mode 100644 index 0000000000000000000000000000000000000000..016d5ae0e8409e0db30b36c49fc745ae244a14b4 --- /dev/null +++ b/scripts/traintest_scripts/train_test_single_task_indistribution.sh @@ -0,0 +1,58 @@ +#!/bin/bash + +DATA_DIR=$1 +TRAINTASK=${2-'[rainbow-stack,bowl-ball-placement]'} +STEPS=${3-'61000'} + +DISP=False + +echo "Training single-task dataset... Folder: $DATA_DIR Task $TRAINTASK" +trap "kill 0" SIGINT +# You can parallelize these depending on how much resources you have + +############################# +## Language-Conditioned Tasks +# [align-rope,assembling-kits-seq-seen-colors,assembling-kits-seq-unseen-colors,packing-shapes] + + +# TRAIN +python cliport/train.py train.task=$TRAINTASK \ + train.agent=cliport \ + train.model_task=$TRAINTASK \ + train.attn_stream_fusion_type=add \ + train.trans_stream_fusion_type=conv \ + train.lang_fusion_type=mult \ + train.n_demos=200 \ + train.n_steps=${STEPS} \ + dataset.cache=True \ + train.exp_folder=exps/exp-$TRAINTASK \ + dataset.type=single \ + train.load_from_last_ckpt=False + +# Convert Python list to Bash array +bash_array=$(python3 -c "import sys; print(' '.join((sys.argv[1])[1:-1].split(',')))" "$TRAINTASK") + +# # Convert the space-separated string to a bash array +# echo "Testing single-task dataset... Folder: $DATA_DIR Task $TASK" + + + +# echo "Testing $TASK" +# # TEST +# # bash scripts/generate_gpt_datasets.sh $DATA_DIR $task + +# python cliport/eval.py model_task=$TRAINTASK \ +# eval_task=$TRAINTASK \ +# agent=cliport \ +# mode=test \ +# n_demos=100 \ +# train_demos=200 \ +# checkpoint_type=test_best \ +# type=single \ +# exp_folder=exps/exp-$TRAINTASK \ +# update_results=True + +# # wait + +# python notebooks/print_results.py -r=exps/exp-$TRAINTASK/ --single +# echo "Finished Training." \ No newline at end of file diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..77a408f0440655686eeea540c5568e9e3fa309ea --- /dev/null +++ b/setup.py @@ -0,0 +1,16 @@ +from setuptools import setup, find_packages + +setup( + name='cliport', + version='0.1.0', + packages=find_packages(), + include_package_data=True, + license=open('LICENSE').read(), + zip_safe=False, + description="CLIPort - What and Where Pathways for Robotic Manipulation.", + author='Mohit Shridhar', + author_email='mshr@cs.washington.edu', + url='https://cliport.github.io/', + # install_requires=[line for line in open('requirements.txt').readlines() if "@" not in line], + keywords=['CLIP', 'Vision Language Grounding', 'Robotics', 'Manipulation'], +) \ No newline at end of file diff --git a/temp/BuildCircle_code_output.txt b/temp/BuildCircle_code_output.txt new file mode 100644 index 0000000000000000000000000000000000000000..dae9a5dc2bc30e7c7703c83e7b8b0627775067e2 --- /dev/null +++ b/temp/BuildCircle_code_output.txt @@ -0,0 +1,50 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildCircle(Task): + """Pick up six blocks of different colors (red, blue, green, yellow, orange, and purple) + and place them on a tabletop in a circle arrangement. The arrangement should start with + red at the top and continue clockwise in this order: blue, green, yellow, orange, and finally purple.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the {color} block at the {position} of the circle" + self.task_completed_desc = "done building circle." + self.colors = ['red', 'blue', 'green', 'yellow', 'orange', 'purple'] + self.positions = ['top', 'top right', 'bottom right', 'bottom', 'bottom left', 'top left'] + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[self.colors[i]]) + blocks.append(block_id) + + # Define target poses for the blocks in a circle arrangement. + radius = 0.1 + center = (0.5, 0.5, 0) + angles = np.linspace(0, 2*np.pi, 7)[:-1] + targ_poses = [(center[0] + radius*np.cos(angle), center[1] + radius*np.sin(angle), block_size[2]/2) for angle in angles] + targ_poses = [(pose, (0, 0, 0, 1)) for pose in targ_poses] # add default quaternion for orientation + + # Add goals. + for i in range(6): + language_goal = self.lang_template.format(color=self.colors[i], position=self.positions[i]) + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targ_poses[i]], replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1/6, language_goal=language_goal) \ No newline at end of file diff --git a/temp/BuildWheel_error.txt b/temp/BuildWheel_error.txt new file mode 100644 index 0000000000000000000000000000000000000000..549402d5e3229177cef5a13cbd99f671fa810e24 --- /dev/null +++ b/temp/BuildWheel_error.txt @@ -0,0 +1,4 @@ +Traceback (most recent call last): + File "/home/baochen/Desktop/projects/GPT-CLIPort/gensim/sim_runner.py", line 293, in simulate_task + yield "Task Generated ==> Asset Generated ==> Code Generated ==> Running Simulation", self.generated_code, self.video_path +AttributeError: 'SimulationRunner' object has no attribute 'video_path' diff --git a/temp/ContainerPyramidConstruction_code_output.txt b/temp/ContainerPyramidConstruction_code_output.txt new file mode 100644 index 0000000000000000000000000000000000000000..08503727d736831dcb2adcf64dcded185ebd6079 --- /dev/null +++ b/temp/ContainerPyramidConstruction_code_output.txt @@ -0,0 +1,63 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ContainerPyramidConstruction(Task): + """Construct a pyramid of containers with specific color and shape arrangement.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "stack the {color} {shape} container on the {level} level" + self.task_completed_desc = "done constructing container pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.35, 0.35, 0.01) + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_urdf = 'pallet/pallet.urdf' + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Define container shapes and colors. + shapes = ['square', 'circle', 'triangle'] + colors = ['red', 'blue', 'green'] + levels = ['bottom', 'middle', 'top'] + + # Add containers. + container_urdf = 'container/container-template.urdf' + containers = [] + for i in range(3): + for j in range(3 - i): + # Define container size and color. + container_size = (0.1, 0.1, 0.1) + container_color = colors[i] + + # Define container pose. + x_offset = 0.05 * (j - (3 - i - 1) / 2) + z_offset = 0.1 * i + container_pose = (pallet_pose[0] + x_offset, pallet_pose[1], pallet_pose[2] + z_offset) + + # Add container to the environment. + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf_filled = self.fill_template(container_urdf, replace) + container_id = env.add_object(container_urdf_filled, container_pose, color=container_color) + containers.append(container_id) + + # Add goals. + for i in range(3): + for j in range(3 - i): + idx = int(i * (i + 1) / 2 + j) + language_goal = self.lang_template.format(color=colors[i], shape=shapes[j], level=levels[i]) + self.add_goal(objs=[containers[idx]], matches=np.ones((1, 1)), targ_poses=[pallet_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, language_goal=language_goal) \ No newline at end of file diff --git a/temp/InsertAndStack_code_output.txt b/temp/InsertAndStack_code_output.txt new file mode 100644 index 0000000000000000000000000000000000000000..b1eaeb7ea47df0fa1da53d7a9ba0ef9585c4ee97 --- /dev/null +++ b/temp/InsertAndStack_code_output.txt @@ -0,0 +1,60 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertAndStack(Task): + """Insert the ell into the fixture and then stack blocks on top of it.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.metric = 'pose' + self.lang_template = "insert the ell into the fixture and then stack blocks on top of it" + self.task_completed_desc = "done insert-and-stack." + + def reset(self, env): + super().reset(env) + + # Add ell + ell_size = (0.1, 0.1, 0.1) + ell_pose = self.get_random_pose(env, ell_size) + ell_urdf = 'insertion/ell.urdf' + ell_id = env.add_object(ell_urdf, ell_pose) + self.color_random_bright(ell_id) + + # Add fixture + fixture_size = (0.12, 0.12, 0.1) + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_urdf = 'insertion/fixture.urdf' + fixture_id = env.add_object(fixture_urdf, fixture_pose) + self.color_random_bright(fixture_id) + + # Add blocks + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + blocks = [] + for _ in range(3): # We want 3 blocks + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose) + self.color_random_bright(block_id) # Randomly color the blocks + blocks.append(block_id) + + # Define the zone + zone_size = (0.1, 0.1, 0.1) + zone_pose = self.get_random_pose(env, zone_size) + zone_urdf = 'zone/zone.urdf' + env.add_object(zone_urdf, zone_pose, 'fixed') # Zone is static + + # Set task goals + objs = [ell_id] + blocks + goal_poses = [fixture_pose] * (len(objs)) + self.add_goal(objs=objs, matches=np.ones((len(objs), 1)), targ_poses=goal_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) \ No newline at end of file diff --git a/temp/SweepRedBlocksIntoZone_code_output.txt b/temp/SweepRedBlocksIntoZone_code_output.txt new file mode 100644 index 0000000000000000000000000000000000000000..55831d072a56587f532e1e223b1c396366b8b460 --- /dev/null +++ b/temp/SweepRedBlocksIntoZone_code_output.txt @@ -0,0 +1,44 @@ +import numpy as np +import os +import pybullet as p +import random +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class SweepRedBlocksIntoZone(Task): + """Sweep a pile of red blocks into a designated zone.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "sweep the pile of red blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().__init__(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of red blocks with `make_piles` function + block_urdf = 'stacking/block.urdf' + block_size = (0.04, 0.04, 0.04) + block_color = utils.COLORS['red'] + pile_pose = self.get_random_pose(env, block_size) + pile_ids = self.make_piles(env, block_urdf, block_size, block_color, pile_pose, num_piles=1, num_objs=5) + + # Add goal + self.add_goal(objs=pile_ids, matches=np.ones((5, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) \ No newline at end of file diff --git a/temp/TASK_NAME_TEMPLATE_full_output.txt b/temp/TASK_NAME_TEMPLATE_full_output.txt new file mode 100644 index 0000000000000000000000000000000000000000..58a0e90253fb560760b4e04fe7287ddc4483f4ac --- /dev/null +++ b/temp/TASK_NAME_TEMPLATE_full_output.txt @@ -0,0 +1,3 @@ + + +================= TRIAL: 1 \ No newline at end of file diff --git a/temp/build-circle_full_output.txt b/temp/build-circle_full_output.txt new file mode 100644 index 0000000000000000000000000000000000000000..4c2a006d42e791a530ff516aeba9290dac0ae15d --- /dev/null +++ b/temp/build-circle_full_output.txt @@ -0,0 +1,966 @@ + + +================= Task and Asset Design! + +>>> Prompt: +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task `build circle`. My goal is to design creative and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Please try to come up with tasks using only these assets. +""" +- stacking: ['stacking/block.urdf', 'stacking/stand.urdf'] +- corner: ['corner/corner-template.urdf'] +- ball: ['ball/ball-template.urdf', 'ball/ball.urdf'] +- sphere: ['sphere/sphere.urdf', 'sphere/sphere-template.urdf'] +- zone: ['zone/zone.urdf', 'zone/zone.obj'] +- block: ['block/block.urdf', 'block/block_for_anchors.urdf', 'block/small.urdf'] +- pallet: ['pallet/pallet.urdf', 'pallet/pallet.obj'] +- cylinder: ['cylinder/cylinder-template.urdf'] +- container: ['container/container-template.urdf'] +- bowl: ['bowl/bowl.urdf'] +- square: ['square/square-template.urdf'] +- box: ['box/box-template.urdf'] +- line: ['line/line-template.urdf'] +- insertion: ['insertion/ell.urdf', 'insertion/fixture.urdf'] + + + +""" + +There are certain rules on the asset usage. +1. Sweeping piles task must have small blocks `block/small.urdf` and zones `zone.urdf`. Only the piles can be swept in all assets +2. Insertion tasks must have `insertion/ell.urdf` and `insertion/fixture.urdf`. Only the fixture can be inserted in all assets. + + +========= +Here are some examples of good tasks. Try to be creative and high standard, and avoid overlapping with these tasks. + +- insert-ell-in-fixture: {'task-name': 'insert-ell-in-fixture', 'task-description': 'Pick up an Ell shaped block and insert it into a fixture on the tabletop.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf']} +- stack-blocks-in-container: {'task-name': 'stack-blocks-in-container', 'task-description': 'Pick up five blocks of different colors (red, blue, green, yellow, and orange) and stack them in a container in a specific sequence. The bottom of the stack should start with a red block followed by a blue, green, yellow and finally an orange block at the top.', 'assets-used': ['block/block.urdf', 'container/container-template.urdf']} +- sorting-blocks-into-pallets: {'task-name': 'sorting-blocks-into-pallets', 'task-description': 'Pick up blocks of four different colors (red, blue, green, yellow) and place them into four separate pallets of matching color. The pallets are placed in a row and the blocks are scattered randomly on the table.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} +- color-ordered-container-arrangement: {'task-name': 'color-ordered-container-arrangement', 'task-description': 'On the tabletop, there are six containers and six blocks of different colors (red, blue, green, yellow, orange, purple). The task is to pick up each block and place it into a container of the same color, then arrange the containers in a line in the following color order: red, blue, green, yellow, orange, and purple.', 'assets-used': ['block/block.urdf', 'container/container-template.urdf']} +- color-coordinated-cylinder-pyramid: {'task-name': 'color-coordinated-cylinder-pyramid', 'task-description': 'Construct a pyramid on a pallet using four cylinders of different colors (red, blue, green, and yellow). The first level should consist of a red cylinder and a blue cylinder side by side. The second level should consist of a green cylinder placed on top of the red and blue cylinders. The third and final level should consist of a yellow cylinder placed on top of the green cylinder. The challenge lies in the precise placement of cylinders, maintaining the balance of the structure, and correct color arrangement.', 'assets-used': ['cylinder/cylinder-template.urdf', 'pallet/pallet.urdf']} +- insert-ell-along-square-path: {'task-name': 'insert-ell-along-square-path', 'task-description': 'On the tabletop, there is a square path marked by small blocks. Along the path, there are four colored ell-shaped blocks (red, blue, green, and yellow) and four fixtures of matching colors. The task is to pick up each ell block and insert it into the fixture of the same color. However, the robot must move each ell block along the marked square path to reach the fixture. The task is challenging because it requires precise navigation along the path, color coordination, and insertion accuracy.', 'assets-used': ['block/small.urdf', 'insertion/ell.urdf', 'insertion/fixture.urdf']} +- color-coordinated-zone-arrangement: {'task-name': 'color-coordinated-zone-arrangement', 'task-description': 'On the tabletop, there are nine blocks of three different colors (three red, three blue, and three green) and three pallets of matching colors (one red, one blue, one green). The task is to pick up each block and place it on the pallet of the same color, arranging the blocks on each pallet in a line. However, there are a few small blocks randomly scattered on the tabletop, which the robot has to navigate around without knocking them over while transporting the blocks to the corresponding pallets. The challenge lies in the precise navigation, placement of the blocks, color matching, and maintaining the balance on the pallets.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'block/small.urdf']} +- color-coded-blocks-on-corner: {'task-name': 'color-coded-blocks-on-corner', 'task-description': 'On a tabletop, there are four blocks of different colors (red, blue, green, and yellow) and a corner structure. The task involves picking up each block and placing it in the corner structure in a specific color sequence: from left to right, place red, blue, green, and finally yellow. The blocks must be arranged such that they form a straight line along the corner. The challenge lies in the precise placement, color coordination, and maintaining the balance of the blocks along the corner.', 'assets-used': ['block/block.urdf', 'corner/corner-template.urdf']} +- stack-three-layer-red-wall: {'task-name': 'block-on-cylinder-on-pallet', 'task-description': 'On the tabletop, there are three cylinders of different colors (red, blue, and green) and three blocks of the same colors. The task involves picking up each block and placing it on the corresponding colored cylinder, which are located in specific positions on a pallet. Starting with the red block and cylinder, followed by blue and finally green. The challenge lies in the precise placement of the blocks on the cylinders, while maintaining color coordination.', 'assets-used': ['block/block.urdf', 'cylinder/cylinder-template.urdf', 'pallet/pallet.urdf']} +- align-rope-along-line: {'task-name': 'align-rope-along-line', 'assets-used': ['line/line-template.urdf'], 'task-description': 'Align a deformable rope along a straight line marked on the tabletop.'} + + + + + +========= +Here are some bad example task instances with reasons. +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "box/box-template.urdf], +} +reasons: not interesting because it overlaps with the current task `put-block-in-bowl`. + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], +} +reasons: the language descriptions are too ambiguous. Navigation is also hard to complete. Also maze.urf does not exist. + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], +} +reasons: this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere. Similarly you cannot create task like `insert-ball-into-cylinder`. + +{ + "task-name": "ball-box-obstacle-course", + "task-description": "Navigate a ball through an obstacle course created by randomly placed boxes and finally place it inside a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: Navigate the ball is not related to tabletop manipulation tasks. + +{ + "task-name": "ball-in-box", + "task-description": "Use a cable to guide a ball into an open box.", + "assets-used": ["cable/cable.urdf", "ball/ball-template.urdf", "box/box-template.urdf"] +} +reasons: This task is too hard since it involves interaction of the cable and the ball and cannot be easily completed. + +{ + "task-name": "ball-in-container", + "task-description": "Use the spatula to lift a ball over a wall of boxes and drop it into a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: The only action primitives as pick and place. One cannot use a spatula to lift an object. + +{ + "task-name": "line-ball-sorting", + "task-description": "Move balls of different colors along a single green line, placing each ball in a designated colored box at the end of the line. The challenge includes precision in maintaining the ball on the line and the correct identification of the box color corresponding to each ball.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf"] +} +reasons: Piling or stacking balls are physically infeasible in the simulation. + + +{ + "task-name": "sweep-and-stack-blocks", + "task-description": "Sweep a pile of small red and blue blocks into two separate zones marked on the tabletop. Then pick up these blocks in each zone and stack them in two towers according to their colors, with the red tower higher than the blue.", + "assets-used": ["zone/zone.urdf", "block/small.urdf"] +} +reasons: Cannot do sweeping and stacking in the same task. + + +========= +Now let's design the task `build circle`. Please describe the new task `build circle` in natural languages in a clear and detailed way. Format the answer in a python dictionary with keys "task-name" and value type string with lower-case and separated by hyphens, "task-description" (one sentence and do not mention urdf paths) and value type string, and "assets-used" and value type list of strings. + +Note: +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. +- Blocks and boxes are easier to stack than cylinders or balls. Specifically, you can update dimensions of a "box/box-template.urdf". +- Try to use `box-template.urdf` for modifiable blocks instead of `block.urdf'. + + +>>> Answer: +Here is the task description for `build circle`: + +```python +{ + "task-name": "build-circle", + "task-description": "Pick up six blocks of different colors (red, blue, green, yellow, orange, and purple) and place them on a tabletop in a circle arrangement. The arrangement should start with red at the top and continue clockwise in this order: blue, green, yellow, orange, and finally purple.", + "assets-used": ["block/block.urdf"] +} +``` + +In this task, the robot is required to pick up blocks of different colors and place them in a specific arrangement on the tabletop. The block arrangement in this task is in the form of a circle, with each block representing a segment of the circle. The placement of the blocks should be done in a specific color order, starting with the red block at the top of the circle and continuing clockwise with the other colors. This task combines the skills of object manipulation, color recognition, and spatial arrangement. + +================= API Preview! + +>>> Prompt: +Before writing the code for the task "build-circle". Here are some APIs that are defined. Please confirm that you understand these APIs. + +""" +class Task(): + """Base Task class.""" + + def __init__(self): + self.ee = Suction + self.mode = 'train' + self.sixdof = False + self.primitive = primitives.PickPlace() + self.oracle_cams = cameras.Oracle.CONFIG + + # Evaluation epsilons (for pose evaluation metric). + self.pos_eps = 0.01 + self.rot_eps = np.deg2rad(15) + + # Workspace bounds. + self.pix_size = 0.003125 + self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]]) + self.zone_bounds = np.copy(self.bounds) + + self.goals = [] + self.lang_goals = [] + self.task_completed_desc = "task completed." + self.progress = 0 + self._rewards = 0 + self.assets_root = None + + def reset(self, env): + if not self.assets_root: + raise ValueError('assets_root must be set for task, ' + 'call set_assets_root().') + self.goals = [] + self.lang_goals = [] + self.progress = 0 # Task progression metric in range [0, 1]. + self._rewards = 0 # Cumulative returned rewards. + + # ------------------------------------------------------------------------- + # Oracle Agent + # ------------------------------------------------------------------------- + + def oracle(self, env): + """Oracle agent.""" + OracleAgent = collections.namedtuple('OracleAgent', ['act']) + + def act(obs, info): + """Calculate action.""" + + # Oracle uses perfect RGB-D orthographic images and segmentation masks. + _, hmap, obj_mask = self.get_true_image(env) + + # Unpack next goal step. + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] + + # Match objects to targets without replacement. + if not replace: + + # Modify a copy of the match matrix. + matches = matches.copy() + + # Ignore already matched objects. + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + if self.is_match(pose, targs[j], symmetry): + matches[i, :] = 0 + matches[:, j] = 0 + + # Get objects to be picked (prioritize farthest from nearest neighbor). + nn_dists = [] + nn_targets = [] + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + xyz, _ = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + if len(targets_i) > 0: + targets_xyz = np.float32([targs[j][0] for j in targets_i]) + dists = np.linalg.norm( + targets_xyz - np.float32(xyz).reshape(1, 3), axis=1) + nn = np.argmin(dists) + nn_dists.append(dists[nn]) + nn_targets.append(targets_i[nn]) + + # Handle ignored objects. + else: + nn_dists.append(0) + nn_targets.append(-1) + order = np.argsort(nn_dists)[::-1] + + # Filter out matched objects. + order = [i for i in order if nn_dists[i] > 0] + + pick_mask = None + for pick_i in order: + pick_mask = np.uint8(obj_mask == objs[pick_i][0]) + + # Erode to avoid picking on edges. + # pick_mask = cv2.erode(pick_mask, np.ones((3, 3), np.uint8)) + + if np.sum(pick_mask) > 0: + break + + # Trigger task reset if no object is visible. + if pick_mask is None or np.sum(pick_mask) == 0: + self.goals = [] + self.lang_goals = [] + print('Object for pick is not visible. Skipping demonstration.') + return + + # Get picking pose. + pick_prob = np.float32(pick_mask) + pick_pix = utils.sample_distribution(pick_prob) + # For "deterministic" demonstrations on insertion-easy, use this: + # pick_pix = (160,80) + pick_pos = utils.pix_to_xyz(pick_pix, hmap, + self.bounds, self.pix_size) + pick_pose = (np.asarray(pick_pos), np.asarray((0, 0, 0, 1))) + + # Get placing pose. + targ_pose = targs[nn_targets[pick_i]] + obj_pose = p.getBasePositionAndOrientation(objs[pick_i][0]) + if not self.sixdof: + obj_euler = utils.quatXYZW_to_eulerXYZ(obj_pose[1]) + obj_quat = utils.eulerXYZ_to_quatXYZW((0, 0, obj_euler[2])) + obj_pose = (obj_pose[0], obj_quat) + world_to_pick = utils.invert(pick_pose) + obj_to_pick = utils.multiply(world_to_pick, obj_pose) + pick_to_obj = utils.invert(obj_to_pick) + place_pose = utils.multiply(targ_pose, pick_to_obj) + + # Rotate end effector? + if not rotations: + place_pose = (place_pose[0], (0, 0, 0, 1)) + + place_pose = (np.asarray(place_pose[0]), np.asarray(place_pose[1])) + + return {'pose0': pick_pose, 'pose1': place_pose} + + return OracleAgent(act) + + # ------------------------------------------------------------------------- + # Reward Function and Task Completion Metrics + # ------------------------------------------------------------------------- + + def reward(self): + """Get delta rewards for current timestep. + + Returns: + A tuple consisting of the scalar (delta) reward, plus `extras` + dict which has extra task-dependent info from the process of + computing rewards that gives us finer-grained details. Use + `extras` for further data analysis. + """ + reward, info = 0, {} + + # Unpack next goal step. + objs, matches, targs, _, _, metric, params, max_reward = self.goals[0] + + # Evaluate by matching object poses. + if metric == 'pose': + step_reward = 0 + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + target_pose = targs[j] + if self.is_match(pose, target_pose, symmetry): + step_reward += max_reward / len(objs) + print(f"object {i} match with target {j} rew: {step_reward}") + break + + # Evaluate by measuring object intersection with zone. + elif metric == 'zone': + zone_pts, total_pts = 0, 0 + obj_pts, zones = params + for zone_idx, (zone_pose, zone_size) in enumerate(zones): + + # Count valid points in zone. + for obj_idx, obj_id in enumerate(obj_pts): + pts = obj_pts[obj_id] + obj_pose = p.getBasePositionAndOrientation(obj_id) + world_to_zone = utils.invert(zone_pose) + obj_to_zone = utils.multiply(world_to_zone, obj_pose) + pts = np.float32(utils.apply(obj_to_zone, pts)) + if len(zone_size) > 1: + valid_pts = np.logical_and.reduce([ + pts[0, :] > -zone_size[0] / 2, pts[0, :] < zone_size[0] / 2, + pts[1, :] > -zone_size[1] / 2, pts[1, :] < zone_size[1] / 2, + pts[2, :] < self.zone_bounds[2, 1]]) + + # if zone_idx == matches[obj_idx].argmax(): + zone_pts += np.sum(np.float32(valid_pts)) + total_pts += pts.shape[1] + step_reward = max_reward * (zone_pts / total_pts) + + # Get cumulative rewards and return delta. + reward = self.progress + step_reward - self._rewards + self._rewards = self.progress + step_reward + + # Move to next goal step if current goal step is complete. + if np.abs(max_reward - step_reward) < 0.01: + self.progress += max_reward # Update task progress. + self.goals.pop(0) + if len(self.lang_goals) > 0: + self.lang_goals.pop(0) + + return reward, info + + def done(self): + """Check if the task is done or has failed. + + Returns: + True if the episode should be considered a success, which we + use for measuring successes, which is particularly helpful for tasks + where one may get successes on the very last time step, e.g., getting + the cloth coverage threshold on the last alllowed action. + However, for bag-items-easy and bag-items-hard (which use the + 'bag-items' metric), it may be necessary to filter out demos that did + not attain sufficiently high reward in external code. Currently, this + is done in `main.py` and its ignore_this_demo() method. + """ + return (len(self.goals) == 0) or (self._rewards > 0.99) + # return zone_done or defs_done or goal_done + + # ------------------------------------------------------------------------- + # Environment Helper Functions + # ------------------------------------------------------------------------- + + def is_match(self, pose0, pose1, symmetry): + """Check if pose0 and pose1 match within a threshold.""" + + # Get translational error. + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) + dist_pos = np.linalg.norm(diff_pos) + + # Get rotational error around z-axis (account for symmetries). + diff_rot = 0 + if symmetry > 0: + rot0 = np.array(utils.quatXYZW_to_eulerXYZ(pose0[1]))[2] + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + diff_rot = np.abs(rot0 - rot1) % symmetry + if diff_rot > (symmetry / 2): + diff_rot = symmetry - diff_rot + + return (dist_pos < self.pos_eps) and (diff_rot < self.rot_eps) + + def get_random_pose(self, env, obj_size): + """Get random collision-free object pose within workspace bounds.""" + + # Get erosion size of object in pixels. + max_size = np.sqrt(obj_size[0] ** 2 + obj_size[1] ** 2) + erode_size = int(np.round(max_size / self.pix_size)) + + _, hmap, obj_mask = self.get_true_image(env) + + # Randomly sample an object pose within free-space pixels. + free = np.ones(obj_mask.shape, dtype=np.uint8) + for obj_ids in env.obj_ids.values(): + for obj_id in obj_ids: + free[obj_mask == obj_id] = 0 + free[0, :], free[:, 0], free[-1, :], free[:, -1] = 0, 0, 0, 0 + free = cv2.erode(free, np.ones((erode_size, erode_size), np.uint8)) + + # if np.sum(free) == 0: + # return None, None + + if np.sum(free) == 0: + # avoid returning None, None + # return None, None + pix = (obj_mask.shape[0] // 2, obj_mask.shape[1] // 2) + else: + pix = utils.sample_distribution(np.float32(free)) + pos = utils.pix_to_xyz(pix, hmap, self.bounds, self.pix_size) + pos = (pos[0], pos[1], obj_size[2] / 2) + theta = np.random.rand() * 2 * np.pi + rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + return pos, rot + + def get_lang_goal(self): + if len(self.lang_goals) == 0: + return self.task_completed_desc + else: + return self.lang_goals[0] + + def get_reward(self): + return float(self._rewards) + + # ------------------------------------------------------------------------- + # Helper Functions + # ------------------------------------------------------------------------- + + def fill_template(self, template, replace): + """Read a file and replace key strings.""" + full_template_path = os.path.join(self.assets_root, template) + with open(full_template_path, 'r') as file: + fdata = file.read() + for field in replace: + for i in range(len(replace[field])): + fdata = fdata.replace(f'{field}{i}', str(replace[field][i])) + alphabet = string.ascii_lowercase + string.digits + rname = ''.join(random.choices(alphabet, k=16)) + tmpdir = tempfile.gettempdir() + template_filename = os.path.split(template)[-1] + fname = os.path.join(tmpdir, f'{template_filename}.{rname}') + with open(fname, 'w') as file: + file.write(fdata) + return fname + + def get_random_size(self, min_x, max_x, min_y, max_y, min_z, max_z): + """Get random box size.""" + size = np.random.rand(3) + size[0] = size[0] * (max_x - min_x) + min_x + size[1] = size[1] * (max_y - min_y) + min_y + size[2] = size[2] * (max_z - min_z) + min_z + return tuple(size) + + def color_random_brown(self, obj): + shade = np.random.rand() + 0.5 + color = np.float32([shade * 156, shade * 117, shade * 95, 255]) / 255 + p.changeVisualShape(obj, -1, rgbaColor=color) + + """"" + + # Environment Class + def add_object(self, urdf, pose, category='rigid'): + """List of (fixed, rigid, or deformable) objects in env.""" + fixed_base = 1 if category == 'fixed' else 0 + obj_id = pybullet_utils.load_urdf( + p, + os.path.join(self.assets_root, urdf), + pose[0], + pose[1], + useFixedBase=fixed_base) + self.obj_ids[category].append(obj_id) + return obj_id +""" + +========= +Note that the objects need to obey physics and not collide with each other, and the object goal poses need to be above the table with lower bound x=0.25, y=-0.5 and upper bound x=0.75, y=0.5. When there are multiple objects for a multi-step pick-and-place task, there are often multiple subgoals. Once the task and environment are generated, an agent with a pick and place primitive will follow the defined goal to accomplish the tasks. + +Additionally, make sure you understand and summarize the ``self.goals`` variables, which has a list of 8-tuple with (objs, matches, targ_poses, replace, rotations, metric, params, step_max_reward, symmetries). +- objs (List of obj_id): object ID. +- matches (Binary Matrix): a binary matrix that denotes which object is matched with which target. This matrix has dimension len(objs) x len(targs). +- targ_poses (List of Poses [(translation, rotation)] ): a list of target poses of tuple (translation, rotation). +- replace (Boolean): whether each object can match with one unique target. This is important if we have one target and multiple objects. If it's set to be false, then any object matching with the target will satisfy. +- rotations (Boolean): whether the placement action has a rotation degree of freedom. +- metric (`pose` or `zone`): `pose` or `zone` that the object needs to be transported to. Example: `pose`. +- params (List of (zone_target, zone_size)): a list of (zone_target, zone_size) for each zone if the metric is `zone`. +- step_max_reward (float): the total reward of matching all the objects with all the target poses. It is not dependent on the number of objects but dependent on the number of goals. +- symmetries: the radians that the object is symmetric around z axis. +- language_goal: the low-level language instructions that denote the goal of this step. + + + +>>> Answer: +Yes, I understand the APIs and the task structure you've provided. + +The `Task` class is the base class for defining tasks in the environment. It includes methods for resetting the task, defining the oracle agent, calculating rewards, checking task completion, and several helper functions for the environment. + +The `oracle` method defines an oracle agent that can perfectly execute the task. It uses perfect RGB-D orthographic images and segmentation masks to identify objects and their positions. The agent then calculates the actions required to match the objects to their targets. + +The `reward` method calculates the delta rewards for the current timestep. It evaluates the reward based on the matching of object poses or measuring object intersection with a zone. + +The `done` method checks if the task is completed or has failed. It returns True if all goals are achieved or if the cumulative rewards exceed 0.99. + +The `is_match` method checks if two poses match within a threshold. It calculates the translational error and rotational error around the z-axis. + +The `get_random_pose` method returns a random collision-free object pose within workspace bounds. + +The `get_lang_goal` method returns the current language goal, or a task completed message if there are no more language goals. + +The `get_reward` method returns the cumulative rewards. + +The `fill_template` method reads a file and replaces key strings. + +The `get_random_size` method returns a random box size. + +The `color_random_brown` method changes the color of an object to a random shade of brown. + +The `add_object` method in the Environment class adds an object to the environment. The object can be fixed, rigid, or deformable. + +The `self.goals` variable is a list of tuples that define the goals for the task. Each tuple includes the object IDs, a binary matrix that denotes which object is matched with which target, a list of target poses, a boolean indicating whether each object can match with one unique target, a boolean indicating whether the placement action has a rotation degree of freedom, the metric (pose or zone), a list of parameters for each zone if the metric is zone, the total reward of matching all the objects with all the target poses, and the radians that the object is symmetric around the z-axis. + +================= Error Book Preview! + +>>> Prompt: +Before writing the code for the task "build-circle". Here are some runtime errors that you do not want to make. Please confirm that you understand these runtime errors. + +""" +- environment.py, line 338, in info + pos, rot = p.getBasePositionAndOrientation(obj_id) +TypeError: an integer is required (got type NoneType) + +- task.py, line 118, in act + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] +IndexError: list index out of range + +- task.py, line 308, in is_match + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) +TypeError: 'float' object is not subscriptable + +- task.py", line 315, in is_match + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + +- utils.py", line 280, in quatXYZW_to_eulerXYZ + quaternion_wxyz = np.array([q[3], q[0], q[1], q[2]]) +IndexError: tuple index out of range + +- pallet_pose = self.get_random_pose(env, pallet_size) +pallet_surface_height = pallet_pose[0][2] +TypeError: 'NoneType' object is not subscriptable + +- No such file or directory: './cliport/environments/assets/circle/circle-template.urdf' + +- No such file or directory: './cliport/environments/assets/block/block-template.urdf' + +- task.py", line 308, in is_match + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) +IndexError: invalid index to scalar variable. + +-TypeError: get_random_size() missing 4 required positional arguments: 'min_y', 'max_y', 'min_z', and 'max_z' + +- task.py", line 195, in reward + obj_pts, zones = params +TypeError: cannot unpack non-iterable NoneType object + +- environment.py", line 230, in step + reward, info = self.task.reward() if action is not None else (0, {}) + File "task.py", line 200, in reward + pts = obj_pts[obj_id] +IndexError: arrays used as indices must be of integer (or boolean) type + +- generated_task.py", line 41, in reset + utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['light blue'], +KeyError: 'light blue' + +- environment.py", line 195, in reset + self.task.reset(self) + File "", line 38, in reset +TypeError: can only concatenate str (not "list") to str + +- environment.py", line 195, in reset + object_shape = np.random.choice(object_shapes) + in numpy.random.mtrand.RandomState.choice +ValueError: a must be 1-dimensional + +- No such file or directory: 'assets/box-template/box-template.urdf' + +- line 38, in reset.py +{'HALF': box_size / 2} +TypeError: unsupported operand type(s) for /: 'tuple' and 'int'. box_size is a tuple not a float. + +- line 38, in reset.py +IndexError: tuple index out of range +box_pose = (pallet_pose[0], pallet_pose[1], pallet_pose[2] + np.sum(box_sizes[:i+1])) + +- task.py", line 338, in fill_template + for i in range(len(replace[field])): +TypeError: object of type 'float' has no len(). + +- task.py", line 325, in get_random_pose + pos = (pos[0], pos[1], obj_size[2] / 2) +IndexError: tuple index out of range + +- task.py", line 206, in reward + for zone_idx, (zone_pose, zone_size) in enumerate(zones): +TypeError: 'NoneType' object is not iterable + +- task.py", +ball_pose = self.get_random_pose(env, ball_size) +ball_pose[0][2] += 0.02 +TypeError: 'tuple' object does not support item assignment +""" + + +You do not want to make mistakes such as +- using assets (urdfs) that do not exist +- use ambiguous language descriptions as goals. For instance, "place the colored blocks into the matching colored bowls" with one goal and sparse reward as the task instead of adding subgoal "place blue block into blue bowl" and give continuous reward. +- `matches` in the goal has wrong dimensions. It should have the same dimensions as number of objects (N) multiplied by the number of goal poses (M). Usually it is N by M. +- have vector dimension problem such as `np.random.choice(box_size)` or `box_size / 2` where `box_size` is a tuple and not an int +- make too large an object for stacking or make the task objects invisible for picking. +- accessing index out of bound `pallet_pose[2]` for `pallet_pose`. `pallet_pose=get_random_pose` returns a tuple (translation, rotation). It does not have 3rd component. Similarly accessing `container_pose[2]` or `box_pose[2]` would cause errors as well. Since it's a tuple, try to modify it in-place will also trigger errors. +- forget to replace str using `fill_template()` for urdfs with template such as `cylinder-template.urdf`. `ball-template.urdf`, `line-template.urf`. +- use `self.ee = Spatula()` as a function when doing pushing tasks, which is incorrect. It should be `self.ee = Spatula`. +- forget to compute target poses `targ_poses` for matching. Do not use object IDs for poses. +- change colors of complex objects such as `zone`. You can only change color of teomplate primitive such as `cylinder-template`. +- mistakenly use `random_pose` for target pose. Design target poses based on task objectives. +- add only one or fewer language goals which causes language-motion inconsistentcy. Note that the language goals usually are the same number as the pick and place goals. + + +>>> Answer: +Yes, I understand the potential runtime errors you've mentioned. + +These errors can occur due to various reasons such as: + +- Trying to perform operations on `NoneType` objects. This usually happens when a function that is expected to return a value returns `None`, often due to some error or exception in the function. +- Index errors, which occur when trying to access an index that is out of range for a list or array. This can happen when looping over the length of an array or when hardcoding indices without checking the length of the array. +- Type errors, which occur when an operation or function is applied to an object of inappropriate type. This can happen when trying to perform an operation that is not supported by the type of the object. +- Key errors, which occur when trying to access a dictionary with a key that does not exist in the dictionary. +- File not found errors, which occur when trying to open a file that does not exist or is not in the expected directory. +- Value errors, which occur when a function receives an argument of the correct type but inappropriate value, and the situation is not dealt with by raising a more specific exception. +- Trying to modify a tuple, which is an immutable object in Python. + +To avoid these errors, it's important to check the types and values of variables before performing operations on them, handle exceptions properly, and ensure that all necessary files and resources are available at the expected paths. Also, when designing tasks, it's crucial to ensure that the tasks are feasible and the goals are clearly defined. + +================= Code Reference! + +>>> Prompt: +Now I will provide you some reference code that might help you can write the code for the task "{'task-name': 'build-circle', 'task-description': 'Pick up six blocks of different colors (red, blue, green, yellow, orange, and purple) and place them on a tabletop in a circle arrangement. The arrangement should start with red at the top and continue clockwise in this order: blue, green, yellow, orange, and finally purple.', 'assets-used': ['block/block.urdf']}". + + +['align_box_corner.py', 'align_rope.py', 'assembling_kits.py', 'block_insertion.py', 'manipulating_rope.py', 'packing_boxes.py', 'packing_boxes_pairs.py', 'palletizing_boxes.py', 'place_red_in_green.py', 'put_block_in_bowl.py', 'separating_piles.py', 'stack_block_pyramid.py', 'sweeping_piles.py', 'towers_of_hanoi.py', 'build_wheel.py', 'rainbow_stack.py', 'connect_boxes_with_rope.py', 'build_car.py', 'manipulating_two_ropes.py', 'insert_sphere_into_container.py', 'build_bridge.py', 'stack_blocks_in_container.py', 'mix_piles.py', 'color_coordinated_block_tower.py', 'color_structured_block_tower.py', 'stack_color_coordinated_blocks.py', 'assemble_single_car.py', 'sort_and_stack_clr_blocks.py', 'create_pyramid_blocks_and_container.py', 'Four_corner_pyramid_challenge.py', 'colorful_block_tower_on_cylinder_base.py', 'corner_block_challenge.py', 'construct_corner_blocks.py', 'corner_sort_cylinders.py', 'sorting_blocks_into_pallets.py', 'sort_and_assemble_block_castle.py', 'vertical_insertion_blocks.py', 'color_coordinated_sphere_insertion.py', 'block_pyramid_with_limited_space.py', 'build_cylinder_structure.py', 'insert_blocks_lineup.py', 'color_specific_container_fill.py', 'multicolor_block_bridge.py', 'pyramid_blocks_assemble.py', 'place_ball_in_elevated_bowl.py', 'align_balls_in_colored_zones.py', 'color_coordinated_cylinder_tower.py', 'symmetric_block_bridge_construction.py', 'sphere_align_stand.py', 'construct_colorful_arch.py', 'color_sorted_container_stack.py', 'align_spheres_in_colored_zones.py', 'sort_insert_color_coordinated_blocks.py', 'color_ordered_insertion.py', 'color_coordinated_insertion.py', 'cylinder_stand_alignment.py', 'color_sorted_block_race.py', 'multi_level_block_construction.py', 'color_blocks_in_cylinder_maze.py', 'create_pyramid_with_color_coded_ells.py', 'move_piles_along_line.py', 'color_ordered_blocks_on_pallet.py', 'color_ordered_container_arrangement.py', 'multi_level_pyramid_construction.py', 'align_balls_in_colored_boxes.py', 'colored_balls_sorting_in_corner.py', 'color_coordinated_ball_insertion.py', 'color_sequenced_pyramid_packing.py', 'ball_sorting_with_blocks_barrier.py', 'color_coordinated_block_bridge.py', 'color_coordinated_cylinder_pyramid.py', 'sweep_and_sort_blocks.py', 'align_cylinders_in_zones.py', 'sphere_container_color_match.py', 'insert_ell_along_square_path.py', 'color_coordinated_box_ball_matching.py', 'cylinder_balancing_and_placement.py', 'color_coordinated_sphere_and_cylinder_assembly.py', 'sequential_block_insertion.py', 'sequential_insertion_and_stacking.py', 'color_coordinated_block_shifting.py', 'guided_block_path.py', 'mixed_color_block_barrier_insertion.py', 'ball_in_bowl_obstacle_course.py', 'ball_in_bowl_obstacle_course_new.py', 'color_coordinated_arch_construction.py', 'color_coordinated_zone_arrangement.py', 'color_coordinated_cylinder_stand_assembly.py', 'color_coordinated_ball_stacking.py', 'color_coded_blocks_on_corner.py', 'insertion_in_color_sequenced_zones.py', 'color_coordinated_zone_stacking.py', 'color_coordinated_cylinder_ball_match.py', 'multi_level_insertion_and_zone_matching.py', 'color_cued_ball_corner_sorting.py', 'cylinder_ring_stack.py', 'color_coordinated_sphere_on_pallet_pyramid.py', 'color_sequenced_sphere_placement.py', 'color_ordered_insertion_new.py', 'put_blocks_between_zones.py', 'put_blues_around_red.py', 'place_blue_on_line_ends.py', 'stack_three_layer_red_wall.py', 'block_on_cylinder_on_pallet.py', 'ball_on_box_on_container.py', 'align_rope_along_line.py', 'align_rope_cross_zone.py', 'put_kit_in_bowl.py', 'kit_in_bowl_in_zone.py', 'move_kit_from_zone_to_cylinder.py', 'move_bowl_from_pallet_to_corner.py'] + + +Please pick 2 task python files that you would like to use as reference. Format them in a python list. + + +>>> Answer: +Based on the task description, the tasks that seem most relevant are 'rainbow_stack.py' and 'color_coordinated_block_tower.py'. These tasks involve picking up blocks of different colors and arranging them in a specific order, which is similar to the 'build-circle' task. Here is the list: + +```python +reference_tasks = ['rainbow_stack.py', 'color_coordinated_block_tower.py'] +``` + +>>> Prompt: +Now I will provide you some reference code and you can write the code for the task "build-circle". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Use functions `make_piles` and `make_ropes` for creating piles and cables. Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "build-circle" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE + + +>>> Answer: +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class BuildCircle(Task): + """Pick up six blocks of different colors (red, blue, green, yellow, orange, and purple) + and place them on a tabletop in a circle arrangement. The arrangement should start with + red at the top and continue clockwise in this order: blue, green, yellow, orange, and finally purple.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "place the {color} block at the {position} of the circle" + self.task_completed_desc = "done building circle." + self.colors = ['red', 'blue', 'green', 'yellow', 'orange', 'purple'] + self.positions = ['top', 'top right', 'bottom right', 'bottom', 'bottom left', 'top left'] + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'block/block.urdf' + blocks = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[self.colors[i]]) + blocks.append(block_id) + + # Define target poses for the blocks in a circle arrangement. + radius = 0.1 + center = (0.5, 0.5, 0) + angles = np.linspace(0, 2*np.pi, 7)[:-1] + targ_poses = [(center[0] + radius*np.cos(angle), center[1] + radius*np.sin(angle), block_size[2]/2) for angle in angles] + targ_poses = [(pose, (0, 0, 0, 1)) for pose in targ_poses] # add default quaternion for orientation + + # Add goals. + for i in range(6): + language_goal = self.lang_template.format(color=self.colors[i], position=self.positions[i]) + self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targ_poses[i]], replace=False, + rotations=False, metric='pose', params=None, step_max_reward=1/6, language_goal=language_goal) +``` +In this code, we first add six blocks of different colors to the environment. We then define the target poses for the blocks in a circle arrangement. The target poses are calculated based on the radius of the circle and the angles of the positions in the circle. We then add the goals for the task, which involve placing each block at its corresponding position in the circle. The language goal for each block is generated based on its color and position. + +================= TRIAL: 1 \ No newline at end of file diff --git a/temp/container-pyramid-construction_error.txt b/temp/container-pyramid-construction_error.txt new file mode 100644 index 0000000000000000000000000000000000000000..193795a203d076cd5a970e431b0125b85a6f1508 --- /dev/null +++ b/temp/container-pyramid-construction_error.txt @@ -0,0 +1,7 @@ +Traceback (most recent call last): + File "/home/baochen/Desktop/projects/GPT-CLIPort/gensim/sim_runner.py", line 266, in simulate_task + obs = env.reset() + File "/home/baochen/Desktop/projects/GPT-CLIPort/cliport/environments/environment.py", line 281, in reset + self.task.reset(self) + File "", line 49, in reset +TypeError: can only concatenate list (not "float") to list diff --git a/temp/container-pyramid-construction_full_output.txt b/temp/container-pyramid-construction_full_output.txt new file mode 100644 index 0000000000000000000000000000000000000000..fb8bcac07616b61dc5b3d1e0ffa0151328fb04d8 --- /dev/null +++ b/temp/container-pyramid-construction_full_output.txt @@ -0,0 +1,1014 @@ + + +================= Task and Asset Design! + +>>> Prompt: +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task. My goal is to design creative and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + +========= +Here are all the assets. Please try to come up with tasks using only these assets. +""" +- stacking: ['stacking/block.urdf', 'stacking/stand.urdf'] +- corner: ['corner/corner-template.urdf'] +- ball: ['ball/ball-template.urdf', 'ball/ball.urdf'] +- sphere: ['sphere/sphere.urdf', 'sphere/sphere-template.urdf'] +- zone: ['zone/zone.urdf', 'zone/zone.obj'] +- block: ['block/block.urdf', 'block/block_for_anchors.urdf', 'block/small.urdf'] +- pallet: ['pallet/pallet.urdf', 'pallet/pallet.obj'] +- cylinder: ['cylinder/cylinder-template.urdf'] +- container: ['container/container-template.urdf'] +- bowl: ['bowl/bowl.urdf'] +- square: ['square/square-template.urdf'] +- box: ['box/box-template.urdf'] +- line: ['line/line-template.urdf'] +- insertion: ['insertion/ell.urdf', 'insertion/fixture.urdf'] + + + +""" + +There are certain rules on the asset usage. +1. Sweeping piles task must have small blocks `block/small.urdf`. Only the piles can be swept in all assets +2. Insertion tasks must have `insertion/ell.urdf` and `insertion/fixture.urdf`. Only the fixture can be inserted in all assets. +3. Rope tasks usually come with 'square/square-template.urdf'. + +========= +Here are some examples of good tasks. Try to be creative and high standard, and avoid overlapping with these tasks. + +- palletizing-boxes: {'assets-used': ['pallet/pallet.urdf', 'box/box-template.urdf'], 'task-description': 'pick up homogeneous fixed-sized boxes and stack them in transposed layers on the pallet.', 'task-name': 'palletizing-boxes'} +- build-car: {'task-name': 'build-car', 'task-description': 'Construct a simple car structure using blocks and cylinders.', 'assets-used': ['block/block.urdf', 'ball/ball-template.urdf']} +- sorting-blocks-into-pallets: {'task-name': 'sorting-blocks-into-pallets', 'task-description': 'Pick up blocks of four different colors (red, blue, green, yellow) and place them into four separate pallets of matching color. The pallets are placed in a row and the blocks are scattered randomly on the table.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} +- color-coordinated-insertion: {'task-name': 'color-coordinated-insertion', 'task-description': 'There are three insertion fixtures and three ell shaped blocks of different colors (red, blue, green) on the table top. The task is to pick up the ell shaped blocks and insert each one of them into the fixture of the same color. However, the ell blocks should be inserted in a specific sequence - red first, then blue, and finally green. This task is challenging due to the precision required for insertion and the need for color coordination and sequencing.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf']} +- create-pyramid-with-color-coded-ells: {'task-name': 'create-pyramid-with-color-coded-ells', 'task-description': "There are four insertion ell-shaped objects ('insertion/ell.urdf') of different colors (red, blue, yellow, and green) placed randomly on the tabletop. The task is to pick up each of these objects and stack them onto a fixed-size pallet in the shape of a pyramid. The order of the pyramid from bottom to top should be red, blue, yellow, and green.", 'assets-used': ['insertion/ell.urdf', 'pallet/pallet.urdf']} +- color-coordinated-block-bridge: {'task-name': 'color-coordinated-block-bridge', 'task-description': 'Construct a bridge by interleaving three differently colored blocks (red, blue, and green) on a pallet in a specific sequence - red block at the edges, blue block in the middle, and a green block on top of the red and blue blocks. Repeat this sequence until a bridge is formed across the length of the pallet.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} +- color-coordinated-cylinder-pyramid: {'task-name': 'color-coordinated-cylinder-pyramid', 'task-description': 'Construct a pyramid on a pallet using four cylinders of different colors (red, blue, green, and yellow). The first level should consist of a red cylinder and a blue cylinder side by side. The second level should consist of a green cylinder placed on top of the red and blue cylinders. The third and final level should consist of a yellow cylinder placed on top of the green cylinder. The challenge lies in the precise placement of cylinders, maintaining the balance of the structure, and correct color arrangement.', 'assets-used': ['cylinder/cylinder-template.urdf', 'pallet/pallet.urdf']} +- sequential-block-insertion: {'task-name': 'sequential-block-insertion', 'task-description': 'There are four blocks of different colors (red, blue, green, yellow) and four fixtures of matching colors. The task involves picking up each block and inserting it into the fixture of the same color, in the specific sequence of red, blue, green, and yellow. However, the challenge lies in the fact that the blocks and fixtures are initially arranged in a mixed order, demanding careful navigation, precise insertion, color matching, and sequence following.', 'assets-used': ['insertion/fixture.urdf', 'block/block.urdf']} +- mixed-color-block-barrier-insertion: {'task-name': 'mixed-color-block-barrier-insertion', 'task-description': 'There are four different colored blocks (red, blue, green, and yellow), and four fixtures in corresponding colors. Two barriers, each made of three blocks (orange, purple, and brown), are placed in between the blocks and fixtures, forming a path that the robot must navigate. The task involves picking up each colored block, navigating the barriers, and inserting each block into the fixture of the same color. The fixtures are arranged in a sequence from left to right: red, blue, green, and yellow, providing a challenge in precise navigation, color coordination, and insertion.', 'assets-used': ['block/block.urdf', 'insertion/fixture.urdf']} +- color-coordinated-ball-stacking: {'task-name': 'color-coordinated-ball-stacking', 'task-description': 'There are four balls of different colors (red, blue, green, yellow), and four containers of matching colors on the table. The task is to pick up each ball and stack it on top of the corresponding colored container. However, the stacking should be done in a specific color sequence - blue at the bottom, followed by yellow, then green, and finally red at the top. This task enforces challenging skills due to the precision required for stacking the balls, color coordination, and sequencing.', 'assets-used': ['ball/ball-template.urdf', 'container/container-template.urdf']} + + + + +========= +Here are some tasks that you have come up with before. Try to have a high-standard and avoid overlapping with these tasks. For instance, `bowl_ball_placement` and `sort_balls_in_bowls` are the same task. `pile_boxes_in_corner` and `stack_blocks_into_pallet` are similar tasks, `align-cylinder-in-corner` and `align-cylinder-corner` are similar. +Past Tasks: + +- sort-and-stack-clr-blocks: {'task-name': 'sort-and-stack-clr-blocks', 'task-description': 'Pick up four blocks of different colors (red, blue, green, yellow) and place them into separate corners of a pallet. After sorting, stack them in a specific sequence on top of the pallet. The bottom of the stack should start with a green block followed by a blue, then red, and finally a yellow block at the top.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} +- align-rope-cross-zone: {'task-name': 'align-rope-cross-zone', 'assets-used': ['zone/zone.urdf'], 'task-description': 'Align a deformable rope across the diagonal of a zone marked on the tabletop.'} +- color-ordered-insertion-new: {'task-name': 'color-ordered-insertion-new', 'task-description': 'There are four differently-colored ell objects (red, blue, green, yellow) and a corresponding set of color-coded fixtures. The task involves picking up each ell object and inserting it into the matching color fixture in a specific order: from left to right, insert red, blue, green, and finally yellow. The challenge lies in the precise manipulation of the ell objects and the color-coordination required.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf']} +- color-ordered-blocks-on-pallet: {'task-name': 'color-ordered-blocks-on-pallet', 'task-description': 'On a table there are six different colored blocks (red, blue, green, yellow, orange, and purple), a pallet, and a small corner structure. These colored blocks are arranged randomly within the small corner structure. The task involves picking up each colored block and placing it onto the pallet in specific color sequence: red, blue, green, yellow, orange, and finally purple.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf', 'corner/corner-template.urdf']} +- guided-block-path: {'task-name': 'guided-block-path', 'task-description': 'On the tabletop, there are four colored blocks (red, blue, green, and yellow) and four lines of the corresponding colors. The task is to pick up each block and move it along the line of the same color from start to end. The challenge lies in precise navigation along the line, color coordination, and block manipulation.', 'assets-used': ['block/block.urdf', 'line/line-template.urdf']} +- packing-boxes: {'assets-used': ['container/container-template.urdf', 'box/box-template.urdf'], 'task-description': 'pick up randomly sized boxes and place them tightly into a container.', 'task-name': 'packing-boxes'} +- color-coordinated-sphere-and-cylinder-assembly: {'task-name': 'color-coordinated-sphere-and-cylinder-assembly', 'task-description': 'The robot starts with four spheres of different colors (red, blue, green, yellow) and four cylinders of matching colors. The task is to pick up each sphere and place it on top of the cylinder of the same color, forming four sphere-and-cylinder pairs. However, the challenge here is to do this in a specific color sequence - red, blue, green, and finally yellow.', 'assets-used': ['sphere/sphere-template.urdf', 'cylinder/cylinder-template.urdf']} +- pyramid-blocks-assemble: {'task-name': 'pyramid-blocks-assemble', 'task-description': 'Construct a pyramid using nine blocks in a specific color order on a pallet. The bottom layer should contain five blocks: red, blue, green, yellow, and orange (in that order from left to right). The middle layer should consist of three blocks: yellow, red, and blue (from left to right). The top layer should contain a single green block. The pyramid requires careful placement and color matching.', 'assets-used': ['block/block.urdf', 'pallet/pallet.urdf']} +- insert-cylinder-in-container: {'task-name': 'insert-cylinder-in-container', 'task-description': 'Pick up a blue cylindrical block and place it into an empty container.', 'assets-used': ['cylinder/cylinder-template.urdf', 'container/container-template.urdf']} +- sphere-container-color-match: {'task-name': 'sphere-container-color-match', 'task-description': 'On a tabletop, there are four spheres of different colors (red, blue, green, and yellow) inside four containers of a different color (red, blue, green, and yellow). The task is to pick up each sphere and place it into a container of the same color. The task is challenging due to the manipulation of spherical objects and the color coordination required.', 'assets-used': ['sphere/sphere.urdf', 'container/container-template.urdf']} + + + + + + +========= +Here are some bad example task instances with reasons. +{ + "task_name": "sort-color-blocks", + "task_descriptions": "Pick up differently colored blocks and place them into separate bowls of matching color." + "assets-used": ["bowl.urdf", "box/box-template.urdf], +} +reasons: not interesting because it overlaps with the current task `put-block-in-bowl`. + +{ + "task-name": "guided-ball-maze", + "task-description": "Navigate a small ball through a maze by tilting the maze board to reach the target zone.", + "assets-used": ["zone-template.urdf", "square-template.urdf", "ball.urdf", "maze.urdf"], +} +reasons: the language descriptions are too ambiguous. Navigation is also hard to complete. Also maze.urf does not exist. + +{ + "task-name": "insert_cylinder_in_sphere", + "task-description": "Pick up the cylinder and insert it into the sphere with an opening on top.", + "assets-used": ["cylinder/cylinder-template.urdf", "sphere/sphere-template.urdf"], +} +reasons: this task does not make sense. The sphere does not have an opening on top, and you cannot insert a cylinder into a sphere. Similarly you cannot create task like `insert-ball-into-cylinder`. + +{ + "task-name": "ball-box-obstacle-course", + "task-description": "Navigate a ball through an obstacle course created by randomly placed boxes and finally place it inside a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: Navigate the ball is not related to tabletop manipulation tasks. + +{ + "task-name": "ball-in-box", + "task-description": "Use a cable to guide a ball into an open box.", + "assets-used": ["cable/cable.urdf", "ball/ball-template.urdf", "box/box-template.urdf"] +} +reasons: This task is too hard since it involves interaction of the cable and the ball and cannot be easily completed. + +{ + "task-name": "ball-in-container", + "task-description": "Use the spatula to lift a ball over a wall of boxes and drop it into a container.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "container/container-template.urdf"] +} +reasons: The only action primitives as pick and place. One cannot use a spatula to lift an object. + +{ + "task-name": "line-ball-sorting", + "task-description": "Move balls of different colors along a single green line, placing each ball in a designated colored box at the end of the line. The challenge includes precision in maintaining the ball on the line and the correct identification of the box color corresponding to each ball.", + "assets-used": ["ball/ball-template.urdf", "box/box-template.urdf", "line/single-green-line-template.urdf"] +} +reasons: Piling or stacking balls are physically infeasible in the simulation. + +{ + "task-name": "sweep-and-stack-blocks", + "task-description": "Sweep a pile of small red and blue blocks into two separate zones marked on the tabletop. Then pick up these blocks in each zone and stack them in two towers according to their colors, with the red tower higher than the blue.", + "assets-used": ["zone/zone.urdf", "block/small.urdf"] +} +reasons: Cannot do sweeping and stacking in the same task. + + +========= +Now please describe the new task in natural languages and explain its novelty and challenges. Format the answer in a python dictionary with keys "task-name" and value type string, "task-description" (one specific sentence) and value type string with lower-case and separated by hyphens, and "assets-used" and value type list of strings. Try to be as creative as possible. + +Note: +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. + + +>>> Answer: +{ + "task-name": "container-pyramid-construction", + "task-description": "On a tabletop, there are containers of different shapes (square, circle, and triangle) and three different colors (red, blue, and green). The task is to pick up each container and stack them on a pallet in the shape of a pyramid with three levels: the bottom level should contain three containers (one of each shape) of the same color, the middle level should contain two containers of the same shape but different color, and the top level should contain one container of a different color and shape. The challenge lies in the precise alignment of the containers, maintaining the balance of the structure, and correct color and shape arrangement.", + "assets-used": ["container/container-template.urdf", "pallet/pallet.urdf"] +} + +Novelty and Challenges: +1. This task introduces a novel combination of stacking and color-shape coordination skills. +2. The task requires the robot to recognize different colors and shapes, which adds a level of complexity. +3. The robot also needs to plan the pick and place movements so as to maintain the balance of the stacked pyramid structure, posing a challenge in maintaining stability and preventing the pyramid from toppling over during the stacking process. + +================= API Preview! + +>>> Prompt: +Before writing the code for the task "container-pyramid-construction". Here are some APIs that are defined. Please confirm that you understand these APIs. + +""" +class Task(): + """Base Task class.""" + + def __init__(self): + self.ee = Suction + self.mode = 'train' + self.sixdof = False + self.primitive = primitives.PickPlace() + self.oracle_cams = cameras.Oracle.CONFIG + + # Evaluation epsilons (for pose evaluation metric). + self.pos_eps = 0.01 + self.rot_eps = np.deg2rad(15) + + # Workspace bounds. + self.pix_size = 0.003125 + self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]]) + self.zone_bounds = np.copy(self.bounds) + + self.goals = [] + self.lang_goals = [] + self.task_completed_desc = "task completed." + self.progress = 0 + self._rewards = 0 + self.assets_root = None + + def reset(self, env): + if not self.assets_root: + raise ValueError('assets_root must be set for task, ' + 'call set_assets_root().') + self.goals = [] + self.lang_goals = [] + self.progress = 0 # Task progression metric in range [0, 1]. + self._rewards = 0 # Cumulative returned rewards. + + # ------------------------------------------------------------------------- + # Oracle Agent + # ------------------------------------------------------------------------- + + def oracle(self, env): + """Oracle agent.""" + OracleAgent = collections.namedtuple('OracleAgent', ['act']) + + def act(obs, info): + """Calculate action.""" + + # Oracle uses perfect RGB-D orthographic images and segmentation masks. + _, hmap, obj_mask = self.get_true_image(env) + + # Unpack next goal step. + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] + + # Match objects to targets without replacement. + if not replace: + + # Modify a copy of the match matrix. + matches = matches.copy() + + # Ignore already matched objects. + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + if self.is_match(pose, targs[j], symmetry): + matches[i, :] = 0 + matches[:, j] = 0 + + # Get objects to be picked (prioritize farthest from nearest neighbor). + nn_dists = [] + nn_targets = [] + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + xyz, _ = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + if len(targets_i) > 0: + targets_xyz = np.float32([targs[j][0] for j in targets_i]) + dists = np.linalg.norm( + targets_xyz - np.float32(xyz).reshape(1, 3), axis=1) + nn = np.argmin(dists) + nn_dists.append(dists[nn]) + nn_targets.append(targets_i[nn]) + + # Handle ignored objects. + else: + nn_dists.append(0) + nn_targets.append(-1) + order = np.argsort(nn_dists)[::-1] + + # Filter out matched objects. + order = [i for i in order if nn_dists[i] > 0] + + pick_mask = None + for pick_i in order: + pick_mask = np.uint8(obj_mask == objs[pick_i][0]) + + # Erode to avoid picking on edges. + # pick_mask = cv2.erode(pick_mask, np.ones((3, 3), np.uint8)) + + if np.sum(pick_mask) > 0: + break + + # Trigger task reset if no object is visible. + if pick_mask is None or np.sum(pick_mask) == 0: + self.goals = [] + self.lang_goals = [] + print('Object for pick is not visible. Skipping demonstration.') + return + + # Get picking pose. + pick_prob = np.float32(pick_mask) + pick_pix = utils.sample_distribution(pick_prob) + # For "deterministic" demonstrations on insertion-easy, use this: + # pick_pix = (160,80) + pick_pos = utils.pix_to_xyz(pick_pix, hmap, + self.bounds, self.pix_size) + pick_pose = (np.asarray(pick_pos), np.asarray((0, 0, 0, 1))) + + # Get placing pose. + targ_pose = targs[nn_targets[pick_i]] + obj_pose = p.getBasePositionAndOrientation(objs[pick_i][0]) + if not self.sixdof: + obj_euler = utils.quatXYZW_to_eulerXYZ(obj_pose[1]) + obj_quat = utils.eulerXYZ_to_quatXYZW((0, 0, obj_euler[2])) + obj_pose = (obj_pose[0], obj_quat) + world_to_pick = utils.invert(pick_pose) + obj_to_pick = utils.multiply(world_to_pick, obj_pose) + pick_to_obj = utils.invert(obj_to_pick) + place_pose = utils.multiply(targ_pose, pick_to_obj) + + # Rotate end effector? + if not rotations: + place_pose = (place_pose[0], (0, 0, 0, 1)) + + place_pose = (np.asarray(place_pose[0]), np.asarray(place_pose[1])) + + return {'pose0': pick_pose, 'pose1': place_pose} + + return OracleAgent(act) + + # ------------------------------------------------------------------------- + # Reward Function and Task Completion Metrics + # ------------------------------------------------------------------------- + + def reward(self): + """Get delta rewards for current timestep. + + Returns: + A tuple consisting of the scalar (delta) reward, plus `extras` + dict which has extra task-dependent info from the process of + computing rewards that gives us finer-grained details. Use + `extras` for further data analysis. + """ + reward, info = 0, {} + + # Unpack next goal step. + objs, matches, targs, _, _, metric, params, max_reward = self.goals[0] + + # Evaluate by matching object poses. + if metric == 'pose': + step_reward = 0 + for i in range(len(objs)): + object_id, (symmetry, _) = objs[i] + pose = p.getBasePositionAndOrientation(object_id) + targets_i = np.argwhere(matches[i, :]).reshape(-1) + for j in targets_i: + target_pose = targs[j] + if self.is_match(pose, target_pose, symmetry): + step_reward += max_reward / len(objs) + print(f"object {i} match with target {j} rew: {step_reward}") + break + + # Evaluate by measuring object intersection with zone. + elif metric == 'zone': + zone_pts, total_pts = 0, 0 + obj_pts, zones = params + for zone_idx, (zone_pose, zone_size) in enumerate(zones): + + # Count valid points in zone. + for obj_idx, obj_id in enumerate(obj_pts): + pts = obj_pts[obj_id] + obj_pose = p.getBasePositionAndOrientation(obj_id) + world_to_zone = utils.invert(zone_pose) + obj_to_zone = utils.multiply(world_to_zone, obj_pose) + pts = np.float32(utils.apply(obj_to_zone, pts)) + if len(zone_size) > 1: + valid_pts = np.logical_and.reduce([ + pts[0, :] > -zone_size[0] / 2, pts[0, :] < zone_size[0] / 2, + pts[1, :] > -zone_size[1] / 2, pts[1, :] < zone_size[1] / 2, + pts[2, :] < self.zone_bounds[2, 1]]) + + # if zone_idx == matches[obj_idx].argmax(): + zone_pts += np.sum(np.float32(valid_pts)) + total_pts += pts.shape[1] + step_reward = max_reward * (zone_pts / total_pts) + + # Get cumulative rewards and return delta. + reward = self.progress + step_reward - self._rewards + self._rewards = self.progress + step_reward + + # Move to next goal step if current goal step is complete. + if np.abs(max_reward - step_reward) < 0.01: + self.progress += max_reward # Update task progress. + self.goals.pop(0) + if len(self.lang_goals) > 0: + self.lang_goals.pop(0) + + return reward, info + + def done(self): + """Check if the task is done or has failed. + + Returns: + True if the episode should be considered a success, which we + use for measuring successes, which is particularly helpful for tasks + where one may get successes on the very last time step, e.g., getting + the cloth coverage threshold on the last alllowed action. + However, for bag-items-easy and bag-items-hard (which use the + 'bag-items' metric), it may be necessary to filter out demos that did + not attain sufficiently high reward in external code. Currently, this + is done in `main.py` and its ignore_this_demo() method. + """ + return (len(self.goals) == 0) or (self._rewards > 0.99) + # return zone_done or defs_done or goal_done + + # ------------------------------------------------------------------------- + # Environment Helper Functions + # ------------------------------------------------------------------------- + + def is_match(self, pose0, pose1, symmetry): + """Check if pose0 and pose1 match within a threshold.""" + + # Get translational error. + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) + dist_pos = np.linalg.norm(diff_pos) + + # Get rotational error around z-axis (account for symmetries). + diff_rot = 0 + if symmetry > 0: + rot0 = np.array(utils.quatXYZW_to_eulerXYZ(pose0[1]))[2] + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + diff_rot = np.abs(rot0 - rot1) % symmetry + if diff_rot > (symmetry / 2): + diff_rot = symmetry - diff_rot + + return (dist_pos < self.pos_eps) and (diff_rot < self.rot_eps) + + def get_random_pose(self, env, obj_size): + """Get random collision-free object pose within workspace bounds.""" + + # Get erosion size of object in pixels. + max_size = np.sqrt(obj_size[0] ** 2 + obj_size[1] ** 2) + erode_size = int(np.round(max_size / self.pix_size)) + + _, hmap, obj_mask = self.get_true_image(env) + + # Randomly sample an object pose within free-space pixels. + free = np.ones(obj_mask.shape, dtype=np.uint8) + for obj_ids in env.obj_ids.values(): + for obj_id in obj_ids: + free[obj_mask == obj_id] = 0 + free[0, :], free[:, 0], free[-1, :], free[:, -1] = 0, 0, 0, 0 + free = cv2.erode(free, np.ones((erode_size, erode_size), np.uint8)) + + # if np.sum(free) == 0: + # return None, None + + if np.sum(free) == 0: + # avoid returning None, None + # return None, None + pix = (obj_mask.shape[0] // 2, obj_mask.shape[1] // 2) + else: + pix = utils.sample_distribution(np.float32(free)) + pos = utils.pix_to_xyz(pix, hmap, self.bounds, self.pix_size) + pos = (pos[0], pos[1], obj_size[2] / 2) + theta = np.random.rand() * 2 * np.pi + rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) + return pos, rot + + def get_lang_goal(self): + if len(self.lang_goals) == 0: + return self.task_completed_desc + else: + return self.lang_goals[0] + + def get_reward(self): + return float(self._rewards) + + # ------------------------------------------------------------------------- + # Helper Functions + # ------------------------------------------------------------------------- + + def fill_template(self, template, replace): + """Read a file and replace key strings.""" + full_template_path = os.path.join(self.assets_root, template) + with open(full_template_path, 'r') as file: + fdata = file.read() + for field in replace: + for i in range(len(replace[field])): + fdata = fdata.replace(f'{field}{i}', str(replace[field][i])) + alphabet = string.ascii_lowercase + string.digits + rname = ''.join(random.choices(alphabet, k=16)) + tmpdir = tempfile.gettempdir() + template_filename = os.path.split(template)[-1] + fname = os.path.join(tmpdir, f'{template_filename}.{rname}') + with open(fname, 'w') as file: + file.write(fdata) + return fname + + def get_random_size(self, min_x, max_x, min_y, max_y, min_z, max_z): + """Get random box size.""" + size = np.random.rand(3) + size[0] = size[0] * (max_x - min_x) + min_x + size[1] = size[1] * (max_y - min_y) + min_y + size[2] = size[2] * (max_z - min_z) + min_z + return tuple(size) + + def color_random_brown(self, obj): + shade = np.random.rand() + 0.5 + color = np.float32([shade * 156, shade * 117, shade * 95, 255]) / 255 + p.changeVisualShape(obj, -1, rgbaColor=color) + + """"" + + # Environment Class + def add_object(self, urdf, pose, category='rigid'): + """List of (fixed, rigid, or deformable) objects in env.""" + fixed_base = 1 if category == 'fixed' else 0 + obj_id = pybullet_utils.load_urdf( + p, + os.path.join(self.assets_root, urdf), + pose[0], + pose[1], + useFixedBase=fixed_base) + self.obj_ids[category].append(obj_id) + return obj_id +""" + +========= +Note that the objects need to obey physics and not collide with each other, and the object goal poses need to be above the table with lower bound x=0.25, y=-0.5 and upper bound x=0.75, y=0.5. When there are multiple objects for a multi-step pick-and-place task, there are often multiple subgoals. Once the task and environment are generated, an agent with a pick and place primitive will follow the defined goal to accomplish the tasks. + +Additionally, make sure you understand and summarize the ``self.goals`` variables, which has a list of 8-tuple with (objs, matches, targ_poses, replace, rotations, metric, params, step_max_reward, symmetries). +- objs (List of obj_id): object ID. +- matches (Binary Matrix): a binary matrix that denotes which object is matched with which target. This matrix has dimension len(objs) x len(targs). +- targ_poses (List of Poses [(translation, rotation)] ): a list of target poses of tuple (translation, rotation). +- replace (Boolean): whether each object can match with one unique target. This is important if we have one target and multiple objects. If it's set to be false, then any object matching with the target will satisfy. +- rotations (Boolean): whether the placement action has a rotation degree of freedom. +- metric (`pose` or `zone`): `pose` or `zone` that the object needs to be transported to. Example: `pose`. +- params (List of (zone_target, zone_size)): a list of (zone_target, zone_size) for each zone if the metric is `zone`. +- step_max_reward (float): the total reward of matching all the objects with all the target poses. It is not dependent on the number of objects but dependent on the number of goals. +- symmetries: the radians that the object is symmetric around z axis. +- language_goal: the low-level language instructions that denote the goal of this step. + + + +>>> Answer: +Yes, I understand the APIs and the structure of the task. + +The `Task` class is the base class for defining a task in the environment. It includes methods for resetting the task, defining the oracle agent, calculating rewards, checking task completion, and several helper functions for the environment. + +The `oracle` method defines an oracle agent that can perfectly execute the task. It uses perfect RGB-D orthographic images and segmentation masks to identify the objects and their positions. The agent then calculates the pick and place poses to execute the task. + +The `reward` method calculates the reward for the current timestep based on the task's progress. It checks if the current goal step is complete and if so, it updates the task progress and moves to the next goal step. + +The `done` method checks if the task is done or has failed. It returns True if all the goals have been achieved or if the cumulative rewards exceed 0.99. + +The `is_match` method checks if two poses match within a threshold. It calculates the translational error and rotational error around the z-axis and returns True if both errors are within the defined thresholds. + +The `get_random_pose` method returns a random collision-free object pose within the workspace bounds. It uses the true image of the environment to identify free spaces and samples a random pose within these spaces. + +The `get_lang_goal` method returns the language goal for the current step. If there are no more language goals, it returns a task completed description. + +The `get_reward` method returns the cumulative rewards for the task. + +The `fill_template` and `get_random_size` methods are helper functions for generating the task environment. + +The `add_object` method in the Environment class is used to add an object to the environment. It takes the object's URDF file, its pose, and its category (fixed, rigid, or deformable) as inputs. + +The `self.goals` variable is a list of tuples that define the goals for the task. Each tuple includes the objects involved, their target poses, whether each object can match with one unique target, whether the placement action has a rotation degree of freedom, the metric for the task (pose or zone), parameters for the task, the maximum reward for the step, and the symmetry of the objects. + +================= Error Book Preview! + +>>> Prompt: +Before writing the code for the task "container-pyramid-construction". Here are some runtime errors that you do not want to make. Please confirm that you understand these runtime errors. + +""" +- environment.py, line 338, in info + pos, rot = p.getBasePositionAndOrientation(obj_id) +TypeError: an integer is required (got type NoneType) + +- task.py, line 118, in act + objs, matches, targs, replace, rotations, _, _, _ = self.goals[0] +IndexError: list index out of range + +- task.py, line 308, in is_match + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) +TypeError: 'float' object is not subscriptable + +- task.py", line 315, in is_match + rot1 = np.array(utils.quatXYZW_to_eulerXYZ(pose1[1]))[2] + +- utils.py", line 280, in quatXYZW_to_eulerXYZ + quaternion_wxyz = np.array([q[3], q[0], q[1], q[2]]) +IndexError: tuple index out of range + +- pallet_pose = self.get_random_pose(env, pallet_size) +pallet_surface_height = pallet_pose[0][2] +TypeError: 'NoneType' object is not subscriptable + +- No such file or directory: './cliport/environments/assets/circle/circle-template.urdf' + +- No such file or directory: './cliport/environments/assets/block/block-template.urdf' + +- task.py", line 308, in is_match + diff_pos = np.float32(pose0[0][:2]) - np.float32(pose1[0][:2]) +IndexError: invalid index to scalar variable. + +-TypeError: get_random_size() missing 4 required positional arguments: 'min_y', 'max_y', 'min_z', and 'max_z' + +- task.py", line 195, in reward + obj_pts, zones = params +TypeError: cannot unpack non-iterable NoneType object + +- environment.py", line 230, in step + reward, info = self.task.reward() if action is not None else (0, {}) + File "task.py", line 200, in reward + pts = obj_pts[obj_id] +IndexError: arrays used as indices must be of integer (or boolean) type + +- generated_task.py", line 41, in reset + utils.COLORS['green'], utils.COLORS['blue'], utils.COLORS['light blue'], +KeyError: 'light blue' + +- environment.py", line 195, in reset + self.task.reset(self) + File "", line 38, in reset +TypeError: can only concatenate str (not "list") to str + +- environment.py", line 195, in reset + object_shape = np.random.choice(object_shapes) + in numpy.random.mtrand.RandomState.choice +ValueError: a must be 1-dimensional + +- No such file or directory: 'assets/box-template/box-template.urdf' + +- line 38, in reset.py +{'HALF': box_size / 2} +TypeError: unsupported operand type(s) for /: 'tuple' and 'int'. box_size is a tuple not a float. + +- line 38, in reset.py +IndexError: tuple index out of range +box_pose = (pallet_pose[0], pallet_pose[1], pallet_pose[2] + np.sum(box_sizes[:i+1])) + +- task.py", line 338, in fill_template + for i in range(len(replace[field])): +TypeError: object of type 'float' has no len(). + +- task.py", line 325, in get_random_pose + pos = (pos[0], pos[1], obj_size[2] / 2) +IndexError: tuple index out of range + +- task.py", line 206, in reward + for zone_idx, (zone_pose, zone_size) in enumerate(zones): +TypeError: 'NoneType' object is not iterable + +- task.py", +ball_pose = self.get_random_pose(env, ball_size) +ball_pose[0][2] += 0.02 +TypeError: 'tuple' object does not support item assignment +""" + + +You do not want to make mistakes such as +- using assets (urdfs) that do not exist +- use ambiguous language descriptions as goals. For instance, "place the colored blocks into the matching colored bowls" with one goal and sparse reward as the task instead of adding subgoal "place blue block into blue bowl" and give continuous reward. +- `matches` in the goal has wrong dimensions. It should have the same dimensions as number of objects (N) multiplied by the number of goal poses (M). Usually it is N by M. +- have vector dimension problem such as `np.random.choice(box_size)` or `box_size / 2` where `box_size` is a tuple and not an int +- make too large an object for stacking or make the task objects invisible for picking. +- accessing index out of bound `pallet_pose[2]` for `pallet_pose`. `pallet_pose=get_random_pose` returns a tuple (translation, rotation). It does not have 3rd component. Similarly accessing `container_pose[2]` or `box_pose[2]` would cause errors as well. Since it's a tuple, try to modify it in-place will also trigger errors. +- forget to replace str using `fill_template()` for urdfs with template such as `cylinder-template.urdf`. `ball-template.urdf`, `line-template.urf`. +- use `self.ee = Spatula()` as a function when doing pushing tasks, which is incorrect. It should be `self.ee = Spatula`. +- forget to compute target poses `targ_poses` for matching. Do not use object IDs for poses. +- change colors of complex objects such as `zone`. You can only change color of teomplate primitive such as `cylinder-template`. +- mistakenly use `random_pose` for target pose. Design target poses based on task objectives. +- add only one or fewer language goals which causes language-motion inconsistentcy. Note that the language goals usually are the same number as the pick and place goals. + + +>>> Answer: +Yes, I understand these runtime errors and will avoid them when writing the code for the task "container-pyramid-construction". + +Here are some key points to remember: + +- Always ensure that the object IDs are valid integers before using them in PyBullet functions like `p.getBasePositionAndOrientation(obj_id)`. +- Always check if the `self.goals` list is not empty before accessing its elements. +- Ensure that the pose variables are correctly formatted as tuples of translation and rotation before using them in mathematical operations. +- Always check if the URDF files exist in the specified path before loading them into the environment. +- Ensure that the `replace` dictionary in the `fill_template` function contains lists as values, not single float values. +- Always check if the variables are not None before using them in mathematical operations or as indices. +- Ensure that the color keys used in `utils.COLORS` dictionary are valid. +- Always ensure that the variables used in mathematical operations have correct dimensions and data types. +- Always check if the variables are iterable before using them in a loop. +- Always ensure that the variables are mutable before trying to modify them in-place. + +In addition, when designing the task, it's important to: + +- Use assets (URDFs) that exist. +- Use clear and specific language descriptions as goals. +- Ensure that the `matches` matrix in the goal has correct dimensions. +- Avoid creating too large objects for stacking or making the task objects invisible for picking. +- Compute target poses `targ_poses` based on task objectives, not object IDs. +- Add enough language goals to maintain consistency between language and motion. +- Avoid changing colors of complex objects. +- Avoid using `random_pose` for target pose. Design target poses based on task objectives. + +================= Code Reference! + +>>> Prompt: +Now I will provide you some reference code that might help you can write the code for the task "{'task-name': 'container-pyramid-construction', 'task-description': 'On a tabletop, there are containers of different shapes (square, circle, and triangle) and three different colors (red, blue, and green). The task is to pick up each container and stack them on a pallet in the shape of a pyramid with three levels: the bottom level should contain three containers (one of each shape) of the same color, the middle level should contain two containers of the same shape but different color, and the top level should contain one container of a different color and shape. The challenge lies in the precise alignment of the containers, maintaining the balance of the structure, and correct color and shape arrangement.', 'assets-used': ['container/container-template.urdf', 'pallet/pallet.urdf']}". + + +['align_box_corner.py', 'align_rope.py', 'assembling_kits.py', 'block_insertion.py', 'manipulating_rope.py', 'packing_boxes.py', 'packing_boxes_pairs.py', 'palletizing_boxes.py', 'place_red_in_green.py', 'put_block_in_bowl.py', 'separating_piles.py', 'stack_block_pyramid.py', 'sweeping_piles.py', 'towers_of_hanoi.py', 'build_wheel.py', 'rainbow_stack.py', 'connect_boxes_with_rope.py', 'build_car.py', 'manipulating_two_ropes.py', 'insert_sphere_into_container.py', 'build_bridge.py', 'stack_blocks_in_container.py', 'mix_piles.py', 'color_coordinated_block_tower.py', 'color_structured_block_tower.py', 'stack_color_coordinated_blocks.py', 'assemble_single_car.py', 'sort_and_stack_clr_blocks.py', 'create_pyramid_blocks_and_container.py', 'Four_corner_pyramid_challenge.py', 'colorful_block_tower_on_cylinder_base.py', 'corner_block_challenge.py', 'construct_corner_blocks.py', 'corner_sort_cylinders.py', 'sorting_blocks_into_pallets.py', 'sort_and_assemble_block_castle.py', 'vertical_insertion_blocks.py', 'color_coordinated_sphere_insertion.py', 'block_pyramid_with_limited_space.py', 'build_cylinder_structure.py', 'insert_blocks_lineup.py', 'color_specific_container_fill.py', 'multicolor_block_bridge.py', 'pyramid_blocks_assemble.py', 'place_ball_in_elevated_bowl.py', 'align_balls_in_colored_zones.py', 'color_coordinated_cylinder_tower.py', 'symmetric_block_bridge_construction.py', 'sphere_align_stand.py', 'construct_colorful_arch.py', 'color_sorted_container_stack.py', 'align_spheres_in_colored_zones.py', 'sort_insert_color_coordinated_blocks.py', 'color_ordered_insertion.py', 'color_coordinated_insertion.py', 'cylinder_stand_alignment.py', 'color_sorted_block_race.py', 'multi_level_block_construction.py', 'color_blocks_in_cylinder_maze.py', 'create_pyramid_with_color_coded_ells.py', 'move_piles_along_line.py', 'color_ordered_blocks_on_pallet.py', 'color_ordered_container_arrangement.py', 'multi_level_pyramid_construction.py', 'align_balls_in_colored_boxes.py', 'colored_balls_sorting_in_corner.py', 'color_coordinated_ball_insertion.py', 'color_sequenced_pyramid_packing.py', 'ball_sorting_with_blocks_barrier.py', 'color_coordinated_block_bridge.py', 'color_coordinated_cylinder_pyramid.py', 'sweep_and_sort_blocks.py', 'align_cylinders_in_zones.py', 'sphere_container_color_match.py', 'insert_ell_along_square_path.py', 'color_coordinated_box_ball_matching.py', 'cylinder_balancing_and_placement.py', 'color_coordinated_sphere_and_cylinder_assembly.py', 'sequential_block_insertion.py', 'sequential_insertion_and_stacking.py', 'color_coordinated_block_shifting.py', 'guided_block_path.py', 'mixed_color_block_barrier_insertion.py', 'ball_in_bowl_obstacle_course.py', 'ball_in_bowl_obstacle_course_new.py', 'color_coordinated_arch_construction.py', 'color_coordinated_zone_arrangement.py', 'color_coordinated_cylinder_stand_assembly.py', 'color_coordinated_ball_stacking.py', 'color_coded_blocks_on_corner.py', 'insertion_in_color_sequenced_zones.py', 'color_coordinated_zone_stacking.py', 'color_coordinated_cylinder_ball_match.py', 'multi_level_insertion_and_zone_matching.py', 'color_cued_ball_corner_sorting.py', 'cylinder_ring_stack.py', 'color_coordinated_sphere_on_pallet_pyramid.py', 'color_sequenced_sphere_placement.py', 'color_ordered_insertion_new.py', 'put_blocks_between_zones.py', 'put_blues_around_red.py', 'place_blue_on_line_ends.py', 'stack_three_layer_red_wall.py', 'block_on_cylinder_on_pallet.py', 'ball_on_box_on_container.py', 'align_rope_along_line.py', 'align_rope_cross_zone.py', 'put_kit_in_bowl.py', 'kit_in_bowl_in_zone.py', 'move_kit_from_zone_to_cylinder.py', 'move_bowl_from_pallet_to_corner.py'] + + +Please pick 4 task python files that you would like to use as reference. Format them in a python list. + + +>>> Answer: +Sure, based on the task description, I would suggest the following four tasks as references: + +1. 'stack_block_pyramid.py' +2. 'rainbow_stack.py' +3. 'color_coordinated_block_tower.py' +4. 'multi_level_pyramid_construction.py' + +These tasks involve stacking objects in a specific order or pattern, which is similar to the "container-pyramid-construction" task. They can provide valuable insights into how to design the task, calculate rewards, and handle task completion. + +Here is the list in Python format: + +```python +reference_tasks = ['stack_block_pyramid.py', 'rainbow_stack.py', 'color_coordinated_block_tower.py', 'multi_level_pyramid_construction.py'] +``` + +>>> Prompt: +Now I will provide you some reference code and you can write the code for the task "container-pyramid-construction". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Use functions `make_piles` and `make_ropes` for creating piles and cables. Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "container-pyramid-construction" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE + + +>>> Answer: +Sure, here is the code for the task "container-pyramid-construction". + +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class ContainerPyramidConstruction(Task): + """Construct a pyramid of containers with specific color and shape arrangement.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "stack the {color} {shape} container on the {level} level" + self.task_completed_desc = "done constructing container pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add pallet. + pallet_size = (0.35, 0.35, 0.01) + pallet_pose = self.get_random_pose(env, pallet_size) + pallet_urdf = 'pallet/pallet.urdf' + env.add_object(pallet_urdf, pallet_pose, 'fixed') + + # Define container shapes and colors. + shapes = ['square', 'circle', 'triangle'] + colors = ['red', 'blue', 'green'] + levels = ['bottom', 'middle', 'top'] + + # Add containers. + container_urdf = 'container/container-template.urdf' + containers = [] + for i in range(3): + for j in range(3 - i): + # Define container size and color. + container_size = (0.1, 0.1, 0.1) + container_color = colors[i] + + # Define container pose. + x_offset = 0.05 * (j - (3 - i - 1) / 2) + z_offset = 0.1 * i + container_pose = (pallet_pose[0] + x_offset, pallet_pose[1], pallet_pose[2] + z_offset) + + # Add container to the environment. + replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} + container_urdf_filled = self.fill_template(container_urdf, replace) + container_id = env.add_object(container_urdf_filled, container_pose, color=container_color) + containers.append(container_id) + + # Add goals. + for i in range(3): + for j in range(3 - i): + idx = int(i * (i + 1) / 2 + j) + language_goal = self.lang_template.format(color=colors[i], shape=shapes[j], level=levels[i]) + self.add_goal(objs=[containers[idx]], matches=np.ones((1, 1)), targ_poses=[pallet_pose], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, language_goal=language_goal) +``` + +This code first adds a pallet to the environment. Then it adds containers of different shapes and colors on the pallet in a pyramid arrangement. The containers are added level by level from bottom to top, and from left to right on each level. The color of the containers changes from level to level. The task goals are added accordingly, with each goal corresponding to placing a container on a specific level of the pyramid. The language goal describes the color, shape, and level of the container to be placed. + +================= TRIAL: 1 \ No newline at end of file diff --git a/temp/dummy_error.txt b/temp/dummy_error.txt new file mode 100644 index 0000000000000000000000000000000000000000..6e1cb9378258bdb64e0361bd07352895d6c45983 --- /dev/null +++ b/temp/dummy_error.txt @@ -0,0 +1,7 @@ +Traceback (most recent call last): + File "/home/baochen/Desktop/projects/GPT-CLIPort/gensim/sim_runner.py", line 266, in simulate_task + obs = env.reset() + File "/home/baochen/Desktop/projects/GPT-CLIPort/cliport/environments/environment.py", line 281, in reset + self.task.reset(self) + File "", line 28, in reset +TypeError: __init__() takes 1 positional argument but 2 were given diff --git a/temp/dummy_full_output.txt b/temp/dummy_full_output.txt new file mode 100644 index 0000000000000000000000000000000000000000..3b2face99509fd9bd64a7403a0852969efa77194 --- /dev/null +++ b/temp/dummy_full_output.txt @@ -0,0 +1,572 @@ + + +================= Task and Asset Design! + +>>> Prompt: +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task `TARGET_TASK_NAME`. My goal is to design creative and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + + +========= +Here are all the assets. Use only these assets in the task and code design. +""" +insertion/: +ell.urdf fixture.urdf + +bowl/: +bowl.urdf + +box/: +box-template.urdf + +stacking/: +block.urdf stand.urdf + +zone/: +zone.obj zone.urdf + +pallet/: +pallet.obj pallet.urdf + +ball/: +ball-template.urdf + +cylinder/: +cylinder-template.urdf + +bowl/: +bowl.urdf + +# assets not for picking +corner/: +corner-template.urdf + +line/: +single-green-line-template.urdf + +container/: +container-template.urdf +""" + + +There are certain rules on the asset usage. +1. Sweeping piles task must have small blocks `block/small.urdf` and zones `zone.urdf`. Only the piles can be swept in all assets +2. Insertion tasks must have `insertion/ell.urdf` and `insertion/fixture.urdf`. Only the fixture can be inserted in all assets. + + +========= +Here are some examples of good tasks. Try to be creative and high standard, and avoid overlapping with these tasks. + +TASK_DESCRIPTION_PROMPT + + + +========= +Now let's design the task `TARGET_TASK_NAME`. Please describe the new task `TARGET_TASK_NAME` in natural languages in a clear and detailed way. Format the answer in a python dictionary with keys "task-name" and value type string with lower-case and separated by hyphens, "task-description" (one sentence and do not mention urdf paths) and value type string, and "assets-used" and value type list of strings. + +Note: +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. + + +========= +Now I will provide you some reference code and you can write the code for the task. + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick) + + self.lang_goals.append(self.lang_template.format(obj=shapes[obj_shapes[i]])) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1) + self.lang_goals.append(self.lang_template) + + # Colors of distractor objects. + # IMPORTANT: RETRIEVE THE ACTUAL COLOR VALUES + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" + + +========= +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE + + + + +>>> Answer: +The task you're asking for is "TARGET_TASK_NAME". This is a placeholder name, so I'll replace it with a concrete task name. Let's design a task called "insert-ell-into-green-fixture". In this task, the robot needs to pick up `insertion/ell.urdf` and `insertion/fixture.urdf` and insert ell into the green fixture. + +```python +import pybullet as p +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertEllIntoGreenFixture(Task): + """Insert ell into the green fixture.""" + + def __init__(self): + super().__init__() + self.max_steps = 5 + self.lang_template = "insert the ell into the green fixture" + self.task_completed_desc = "done inserting ell into fixture." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add ell. + ell_size = (0.02, 0.02, 0.02) # x, y, z dimensions for the asset size + ell_pose = self.get_random_pose(env, ell_size) + ell_urdf = 'insertion/ell.urdf' + ell_id = env.add_object(ell_urdf, ell_pose) + ell_color = p.getVisualShapeData(ell_id)[0][7] # retrieve the actual color values of ell + + # Add green fixture. + fixture_size = (0.02, 0.02, 0.02) # x, y, z dimensions for the asset size + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_urdf = 'insertion/fixture.urdf' + env.add_object(fixture_urdf, fixture_pose, fixed_base=1, color=utils.COLORS['green']) + + # Goal: insert ell into the fixture. + self.add_goal(objs=[ell_id], matches=[[1]], targ_poses=[fixture_pose], + replace=False, rotations=True, metric='pose', params=None, step_max_reward=1) + self.lang_goals.append(self.lang_template.format(obj='ell', color=ell_color)) + +``` +The task is designed to test the robot's ability to perform delicate manipulations. It must pick up the ell, move it to the fixture, and then insert it. The ell and fixture are positioned in random locations at the start of each task instance, ensuring a diverse range of scenarios. + +================= Code Generation! + +>>> Prompt: +Now I will provide you some reference code and you can write the code for the task "dummy". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. If you have only one goal, `step_max_reward` in `add_goal` should be 1. Use functions `make_piles` and `make_ropes` for creating piles and cables. To use spatula together with the push primitives, import the libraries +""" +from cliport.tasks import primitives; +from cliport.tasks.grippers import Spatula +""" +and then use `self.primitive = primitives.push` and `self.ee = Spatula`. +Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "dummy" in python code block starting with ```python. Reminder: {'task-name': 'dummy', 'assets-used': [], 'task_descriptions': ''} + + +>>> Answer: +The task "dummy" is a placeholder task. I'll replace it with a concrete task called "sweep-red-blocks-into-zone". In this task, the robot needs to sweep a pile of red blocks into a designated zone. + +```python +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + +class SweepRedBlocksIntoZone(Task): + """Sweep a pile of red blocks into a designated zone.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "sweep the pile of red blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().__init__(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of red blocks with `make_piles` function + block_urdf = 'stacking/block.urdf' + block_size = (0.04, 0.04, 0.04) + block_color = utils.COLORS['red'] + pile_pose = self.get_random_pose(env, block_size) + pile_ids = self.make_piles(env, block_urdf, block_size, block_color, pile_pose, num_piles=1, num_objs=5) + + # Add goal + self.add_goal(objs=pile_ids, matches=np.ones((5, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +``` + +In this task, the robot is required to sweep a pile of red blocks into a designated zone. The pile of blocks and the zone are positioned randomly at the start of each task instance, ensuring a diverse range of scenarios. The robot is equipped with a spatula and uses the push primitive to perform the task. The task is completed when all blocks are within the zone. + +================= TRIAL: 1 \ No newline at end of file diff --git a/temp/insert-and-stack_error.txt b/temp/insert-and-stack_error.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa36a16f91e185db0c786cc66c7123e9ee5ae050 --- /dev/null +++ b/temp/insert-and-stack_error.txt @@ -0,0 +1,7 @@ +Traceback (most recent call last): + File "/home/baochen/Desktop/projects/GPT-CLIPort/gensim/sim_runner.py", line 266, in simulate_task + obs = env.reset() + File "/home/baochen/Desktop/projects/GPT-CLIPort/cliport/environments/environment.py", line 281, in reset + self.task.reset(self) + File "", line 31, in reset +AttributeError: 'InsertAndStack' object has no attribute 'color_random_bright' diff --git a/temp/insert-and-stack_full_output.txt b/temp/insert-and-stack_full_output.txt new file mode 100644 index 0000000000000000000000000000000000000000..adda21175e7a997e90055e387b9a97fca7ae5595 --- /dev/null +++ b/temp/insert-and-stack_full_output.txt @@ -0,0 +1,636 @@ + + +================= Task and Asset Design! + +>>> Prompt: +You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task `TARGET_TASK_NAME`. My goal is to design creative and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. + + +========= +Here are all the assets. Use only these assets in the task and code design. +""" +insertion/: +ell.urdf fixture.urdf + +bowl/: +bowl.urdf + +box/: +box-template.urdf + +stacking/: +block.urdf stand.urdf + +zone/: +zone.obj zone.urdf + +pallet/: +pallet.obj pallet.urdf + +ball/: +ball-template.urdf + +cylinder/: +cylinder-template.urdf + +bowl/: +bowl.urdf + +# assets not for picking +corner/: +corner-template.urdf + +line/: +single-green-line-template.urdf + +container/: +container-template.urdf +""" + + +There are certain rules on the asset usage. +1. Sweeping piles task must have small blocks `block/small.urdf` and zones `zone.urdf`. Only the piles can be swept in all assets +2. Insertion tasks must have `insertion/ell.urdf` and `insertion/fixture.urdf`. Only the fixture can be inserted in all assets. + + +========= +Here are some examples of good tasks. Try to be creative and high standard, and avoid overlapping with these tasks. + +TASK_DESCRIPTION_PROMPT + + + +========= +Now let's design the task `TARGET_TASK_NAME`. Please describe the new task `TARGET_TASK_NAME` in natural languages in a clear and detailed way. Format the answer in a python dictionary with keys "task-name" and value type string with lower-case and separated by hyphens, "task-description" (one sentence and do not mention urdf paths) and value type string, and "assets-used" and value type list of strings. + +Note: +- Do not use assets that are not in the list above. +- Tasks that have more colors and shapes are interesting. +- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. +- The task need to obey physics and remain feasible. + + +========= +Now I will provide you some reference code and you can write the code for the task. + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick) + + self.lang_goals.append(self.lang_template.format(obj=shapes[obj_shapes[i]])) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1) + self.lang_goals.append(self.lang_template) + + # Colors of distractor objects. + # IMPORTANT: RETRIEVE THE ACTUAL COLOR VALUES + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" + + +========= +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. Reminder: TASK_STRING_TEMPLATE + + + + +>>> Answer: +The task we will design is called "insert-and-stack". In this task, the robot is required to first insert the ell into the fixture and then stack a number of blocks on top of the inserted fixture. The task should be completed within a specified zone. The blocks can be of multiple colors to make the task more interesting and appealing. + +Here is how we define it: + +```python +{ + "task-name": "insert-and-stack", + "task-description": "Insert the ell into the fixture and then stack three blocks of different colors on top of the inserted fixture within the specified zone.", + "assets-used": ["insertion/ell.urdf", "insertion/fixture.urdf", "stacking/block.urdf", "zone/urdf"] +} +``` + +Now let's write the task code: + +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +import os + +class InsertAndStack(Task): + """Insert ell into fixture and then stack blocks on top of it.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.metric = 'pose' + self.lang_template = "insert the ell into the fixture and then stack blocks on top of it" + self.task_completed_desc = "done insert-and-stack." + + def reset(self, env): + super().reset(env) + + # Add ell + ell_size = (0.1, 0.1, 0.1) + ell_pose = self.get_random_pose(env, ell_size) + ell_urdf = 'insertion/ell.urdf' + ell_id = env.add_object(ell_urdf, ell_pose) + self.color_random_bright(ell_id) + + # Add fixture + fixture_size = (0.12, 0.12, 0.1) + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_urdf = 'insertion/fixture.urdf' + fixture_id = env.add_object(fixture_urdf, fixture_pose) + self.color_random_bright(fixture_id) + + # Add blocks + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + blocks = [] + for _ in range(3): # We want 3 blocks + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose) + self.color_random_bright(block_id) # Randomly color the blocks + blocks.append(block_id) + + # Define the zone + zone_size = (0.1, 0.1, 0.1) + zone_pose = self.get_random_pose(env, zone_size) + zone_urdf = 'zone/zone.urdf' + env.add_object(zone_urdf, zone_pose, 'fixed') # Zone is static + + # Set task goals + objs = [ell_id] + blocks + goal_poses = [fixture_pose] * (len(objs)) + self.goals.append((objs, goal_poses, False)) + self.lang_goals.append(self.lang_template) +``` + +In this code, we first add the ell and fixture into the environment. They are randomly colored for aesthetic purposes. + +Next, we add three blocks into the environment, which are also randomly colored. + +We then define a zone in which the task needs to be performed and add it to the environment. + +Finally, we set the goal of the task, which is for the ell and blocks to be placed in the fixture's position. This effectively means that the blocks must be stacked on top of the ell that has been inserted into the fixture. + +Note: The color_random_bright() method is assumed to be a method of the Task class which sets a random bright color to the object. If such method does not exist, the color assignment lines can be removed. + +================= Code Generation! + +>>> Prompt: +Now I will provide you some reference code and you can write the code for the task "insert-and-stack". + + +""" +import os + +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + + +class PackingShapes(Task): + """pick up randomly sized shapes and place them tightly into a container.""" + + def __init__(self): + super().__init__() + self.max_steps = 1 + self.homogeneous = False + + self.lang_template = "pack the {obj} in the brown box" + self.task_completed_desc = "done packing shapes." + self.additional_reset() + + + def reset(self, env): + super().reset(env) + + # Shape Names: + shapes = utils.assembling_kit_shapes + + n_objects = 5 + if self.mode == 'train': + obj_shapes = np.random.choice(self.train_set, n_objects, replace=False) + else: + if self.homogeneous: + obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects + else: + obj_shapes = np.random.choice(self.test_set, n_objects, replace=False) + + # Shuffle colors to avoid always picking an object of the same color + colors, color_names = utils.get_colors(mode=self.mode) + + # Add container box. + zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05) + zone_pose = self.get_random_pose(env, zone_size) + container_template = 'container/container-template.urdf' + replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)} + # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template` + container_urdf = self.fill_template(container_template, replace) + env.add_object(container_urdf, zone_pose, 'fixed') + + # Add objects. + objects = [] + template = 'kitting/object-template.urdf' + for i in range(n_objects): + shape = obj_shapes[i] + + # x, y, z dimensions for the asset size + size = (0.08, 0.08, 0.02) + pose= self.get_random_pose(env, size) + fname = f'{shape:02d}.obj' + fname = os.path.join(self.assets_root, 'kitting', fname) + scale = [0.003, 0.003, 0.001] # .0005 + replace = {'FNAME': (fname,), + 'SCALE': scale, + 'COLOR': colors[i]} + + # IMPORTANT: REPLACE THE TEMPLATE URDF + urdf = self.fill_template(template, replace) + block_id = env.add_object(urdf, pose) + objects.append(block_id) + + # Pick the first shape. + num_objects_to_pick = 1 + for i in range(num_objects_to_pick): + # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict + language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]]) + self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False, + rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick, + language_goal=language_goal) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + + +class PlaceRedInGreen(Task): + """pick up the red blocks and place them into the green bowls amidst other objects.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.lang_template = "put the red blocks in a green bowl" + self.task_completed_desc = "done placing blocks in bowls." + self.additional_reset() + + def reset(self, env): + super().reset(env) + n_bowls = np.random.randint(1, 4) + n_blocks = np.random.randint(1, n_bowls + 1) + + # Add bowls. + # x, y, z dimensions for the asset size + bowl_size = (0.12, 0.12, 0) + bowl_urdf = 'bowl/bowl.urdf' + bowl_poses = [] + for _ in range(n_bowls): + bowl_pose = self.get_random_pose(env, obj_size=bowl_size) + env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed') + bowl_poses.append(bowl_pose) + + # Add blocks. + # x, y, z dimensions for the asset size + blocks = [] + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + for _ in range(n_blocks): + block_pose = self.get_random_pose(env, obj_size=block_size) + block_id = env.add_object(block_urdf, block_pose) + blocks.append(block_id) + + # Goal: each red block is in a different green bowl. + self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) + + # Colors of distractor objects. + bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green'] + block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red'] + + # Add distractors. + n_distractors = 0 + while n_distractors < 6: + is_block = np.random.rand() > 0.5 + urdf = block_urdf if is_block else bowl_urdf + size = block_size if is_block else bowl_size + colors = block_colors if is_block else bowl_colors + pose = self.get_random_pose(env, obj_size=size) + color = colors[n_distractors % len(colors)] + + obj_id = env.add_object(urdf, pose, color=color) + n_distractors += 1 +""" +""" +import numpy as np +from cliport.tasks import primitives +from cliport.tasks.grippers import Spatula +from cliport.tasks.task import Task +from cliport.utils import utils + + +class SweepingPiles(Task): + """Push piles of small objects into a target goal zone marked on the tabletop.""" + + def __init__(self): + super().__init__() + self.max_steps = 20 + self.lang_template = "push the pile of blocks into the green square" + self.task_completed_desc = "done sweeping." + self.primitive = primitives.push + self.ee = Spatula + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add goal zone. + zone_size = (0.12, 0.12, 0) + zone_pose = self.get_random_pose(env, zone_size) + env.add_object('zone/zone.urdf', zone_pose, 'fixed') + + # Add pile of small blocks with `make_piles` function + obj_ids = self.make_piles(env) + + # Add goal + self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True, + rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template) +""" +""" +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils +import pybullet as p + +class StackBlockPyramid(Task): + """Build a pyramid of colored blocks in a color sequence""" + + def __init__(self): + super().__init__() + self.max_steps = 12 + self.lang_template = "make the {row} row with {blocks}" + self.task_completed_desc = "done stacking block pyramid." + self.additional_reset() + + def reset(self, env): + super().reset(env) + + # Add base. + base_size = (0.05, 0.15, 0.005) + base_urdf = 'stacking/stand.urdf' + base_pose = self.get_random_pose(env, base_size) + env.add_object(base_urdf, base_pose, category='fixed') + + # Block colors. + colors = [ + utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'], + utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red'] + ] + + # Add blocks. + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + + objs = [] + for i in range(6): + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose, color=colors[i]) + objs.append(block_id) + + # IMPORTANT Associate placement locations for goals. + place_pos = [(0, -0.05, 0.03), (0, 0, 0.03), + (0, 0.05, 0.03), (0, -0.025, 0.08), + (0, 0.025, 0.08), (0, 0, 0.13)] + targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos] + + # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple). + language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom") + self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (middle row: yellow, orange). + language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle") + self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal) + + # Goal: blocks are stacked in a pyramid (top row: red). + language_goal = self.lang_template.format(blocks="the red block", row="top") + self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal) +""" + + + +Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. If you have only one goal, `step_max_reward` in `add_goal` should be 1. Use functions `make_piles` and `make_ropes` for creating piles and cables. To use spatula together with the push primitives, import the libraries +""" +from cliport.tasks import primitives; +from cliport.tasks.grippers import Spatula +""" +and then use `self.primitive = primitives.push` and `self.ee = Spatula`. +Note that the number of language goals usually match the number of motion goals, since they should correspond to each other. + +Now write the code for the task "insert-and-stack" in python code block starting with ```python. Reminder: {'task-name': 'insert-and-stack', 'task-description': 'Insert the ell into the fixture and then stack three blocks of different colors on top of the inserted fixture within the specified zone.', 'assets-used': ['insertion/ell.urdf', 'insertion/fixture.urdf', 'stacking/block.urdf', 'zone/urdf']} + + +>>> Answer: +Here is the code for the task "insert-and-stack": + +```python +import numpy as np +from cliport.tasks.task import Task +from cliport.utils import utils + +class InsertAndStack(Task): + """Insert the ell into the fixture and then stack blocks on top of it.""" + + def __init__(self): + super().__init__() + self.max_steps = 10 + self.metric = 'pose' + self.lang_template = "insert the ell into the fixture and then stack blocks on top of it" + self.task_completed_desc = "done insert-and-stack." + + def reset(self, env): + super().reset(env) + + # Add ell + ell_size = (0.1, 0.1, 0.1) + ell_pose = self.get_random_pose(env, ell_size) + ell_urdf = 'insertion/ell.urdf' + ell_id = env.add_object(ell_urdf, ell_pose) + self.color_random_bright(ell_id) + + # Add fixture + fixture_size = (0.12, 0.12, 0.1) + fixture_pose = self.get_random_pose(env, fixture_size) + fixture_urdf = 'insertion/fixture.urdf' + fixture_id = env.add_object(fixture_urdf, fixture_pose) + self.color_random_bright(fixture_id) + + # Add blocks + block_size = (0.04, 0.04, 0.04) + block_urdf = 'stacking/block.urdf' + blocks = [] + for _ in range(3): # We want 3 blocks + block_pose = self.get_random_pose(env, block_size) + block_id = env.add_object(block_urdf, block_pose) + self.color_random_bright(block_id) # Randomly color the blocks + blocks.append(block_id) + + # Define the zone + zone_size = (0.1, 0.1, 0.1) + zone_pose = self.get_random_pose(env, zone_size) + zone_urdf = 'zone/zone.urdf' + env.add_object(zone_urdf, zone_pose, 'fixed') # Zone is static + + # Set task goals + objs = [ell_id] + blocks + goal_poses = [fixture_pose] * (len(objs)) + self.add_goal(objs=objs, matches=np.ones((len(objs), 1)), targ_poses=goal_poses, replace=False, + rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) +``` + +In this code, we first add the ell and fixture into the environment. They are randomly colored for aesthetic purposes. + +Next, we add three blocks into the environment, which are also randomly colored. + +We then define a zone in which the task needs to be performed and add it to the environment. + +Finally, we set the goal of the task, which is for the ell and blocks to be placed in the fixture's position. This effectively means that the blocks must be stacked on top of the ell that has been inserted into the fixture. + +Note: The color_random_bright() method is assumed to be a method of the Task class which sets a random bright color to the object. If such method does not exist, the color assignment lines can be removed. + +================= TRIAL: 1 \ No newline at end of file diff --git a/transfer.sh b/transfer.sh new file mode 100644 index 0000000000000000000000000000000000000000..10f58b7a96dd7daaee77895ef4d2208c31214d7c --- /dev/null +++ b/transfer.sh @@ -0,0 +1,2 @@ +cp -r cliport gensim BLOG.md setup.py prompts media .gitignore ../Hf_GenSim/ +cp -r cliport gensim BLOG.md setup.py prompts media README.md requirements.txt .gitignore ../GenSim/ \ No newline at end of file