init

.dockerignore

@@ -0,0 +1,6 @@
+# Ignore everything
+**
+
+# Except scripts
+!/scripts
+!/requirements.txt

.gitattributes

@@ -0,0 +1,2 @@
+*.stl filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text

.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

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)
+
+<details><summary>
+Prompt Metric Ablation
+</summary>
+
+![](media/prompt_metric.png)
+
+</details>
+
+## 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.
+
+<details><summary>
+Stack-Tasks Examples (Complexity)
+</summary>
+
+![](media/stack_task.gif)
+
+</details>
+
+<details><summary>
+Build-Task Examples (Creativity)
+</summary>
+
+![](media/build_task.gif)
+
+</details>
+
+<details><summary>
+Pick-and-Place-Task Examples (Compositionality)
+</summary>
+
+![](media/pick_place_task.gif)
+
+</details>
+<!--  ![](media/generated_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.
+
+<!-- <details><summary>
+Policy Training Results
+</summary>
+
+![](media/failure_case.gif)
+
+</details> -->
+
+
+## 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`.
+
+<details><summary>
+Failure Cases
+</summary>
+
+![](media/failure_case.gif)
+
+</details>
+
+
+## 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.

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

LICENSE

@@ -0,0 +1,201 @@
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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:
+
+<!-- [Code as Policies: Language Model Programs for Embodied Control](https://code-as-policies.github.io/)
+ -->
+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.

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)

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

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
+         
+         }

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,
+        )

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,
+        }

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,
+        )

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

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

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

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'

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',
+    ]

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()

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

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
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+v -3.870231 1.555544 -0.652395
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cliport/environments/assets/bags/bl_sphere_bag_basic_004.mtl

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cliport/environments/assets/bags/bl_sphere_bag_basic_004.obj

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+# Blender v2.82 (sub 7) OBJ File: ''
+# www.blender.org
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cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.3_numV_289.mtl

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cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.6_numV_353.mtl

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cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.8_numV_385.mtl

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cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.8_numV_385.obj

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cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.8_numV_385_top_ring.txt

@@ -0,0 +1,32 @@
+0 0.00000 0.70711 0.70711
+7 0.13795 0.69352 0.70711
+19 0.27060 0.65328 0.70711
+31 0.39285 0.58794 0.70711
+43 0.50000 0.50000 0.70711
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+67 0.65328 0.27060 0.70711
+79 0.69352 0.13795 0.70711
+91 0.70711 -0.00000 0.70711
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+356 -0.27060 0.65328 0.70711
+368 -0.13795 0.69352 0.70711

cliport/environments/assets/ball/ball-template.urdf

@@ -0,0 +1,31 @@
+<?xml version="0.0" ?>
+<robot name="box.urdf">
+  <link name="baseLink">
+    <contact>
+      <lateral_friction value="1.0"/>
+      <rolling_friction value="0.0001"/>
+      <inertia_scaling value="3.0"/>
+    </contact>
+    <inertial>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+       <mass value="0.1"/>
+       <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
+    </inertial>
+    <visual>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <sphere radius="DIM0"/>
+      </geometry>
+      <material name="red">
+        <color rgba="1 0.3412 0.3490 1"/>
+      </material>
+    </visual>
+    <collision>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+	 	    <sphere radius="DIM0"/>
+      </geometry>
+    </collision>
+  </link>
+</robot>
+

cliport/environments/assets/ball/ball-template_COLOR.urdf

@@ -0,0 +1,31 @@
+<?xml version="0.0" ?>
+<robot name="box.urdf">
+  <link name="baseLink">
+    <contact>
+      <lateral_friction value="1.0"/>
+      <rolling_friction value="0.0001"/>
+      <inertia_scaling value="3.0"/>
+    </contact>
+    <inertial>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+       <mass value="0.1"/>
+       <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
+    </inertial>
+    <visual>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <sphere radius="0.03"/>
+      </geometry>
+      <material name="red">
+        <color rgba="1 0.3412 0.3490 1"/>
+      </material>
+    </visual>
+    <collision>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+	 	    <sphere radius="0.03"/>
+      </geometry>
+    </collision>
+  </link>
+</robot>
+

cliport/environments/assets/ball/ball-template_DIM.urdf

@@ -0,0 +1,31 @@
+<?xml version="0.0" ?>
+<robot name="box.urdf">
+  <link name="baseLink">
+    <contact>
+      <lateral_friction value="1.0"/>
+      <rolling_friction value="0.0001"/>
+      <inertia_scaling value="3.0"/>
+    </contact>
+    <inertial>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+       <mass value="0.1"/>
+       <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
+    </inertial>
+    <visual>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <sphere radius="DIM0"/>
+      </geometry>
+      <material name="red">
+        <color rgba="1 0.3412 0.3490 1"/>
+      </material>
+    </visual>
+    <collision>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+	 	    <sphere radius="DIM0"/>
+      </geometry>
+    </collision>
+  </link>
+</robot>
+

cliport/environments/assets/ball/ball.urdf

@@ -0,0 +1,32 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<robot name="box.urdf">
+  <link name="baseLink">
+    <contact>
+      <lateral_friction value="1.0"/>
+      <rolling_friction value="0.0001"/>
+      <inertia_scaling value="3.0"/>
+    </contact>
+    <inertial>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+       <mass value="0.1"/>
+       <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
+    </inertial>
+    <visual>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <sphere radius="0.03"/>
+      </geometry>
+      <material name="red">
+        <color rgba="1 0.3412 0.3490 1"/>
+      </material>
+    </visual>
+    <collision>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+	 	    <sphere radius="0.03"/>
+      </geometry>
+    </collision>
+  </link>
+</robot>
+

cliport/environments/assets/block/block.urdf

@@ -0,0 +1,31 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<robot name="box.urdf">
+  <link name="baseLink">
+    <contact>
+      <lateral_friction value="1.0"/>
+      <rolling_friction value="0.0001"/>
+      <inertia_scaling value="3.0"/>
+    </contact>
+    <inertial>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+       <mass value=".1"/>
+       <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
+    </inertial>
+    <visual>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+         <box size="0.04 0.04 0.04"/>
+      </geometry>
+      <material name="red">
+        <color rgba="1 0.3412 0.3490 1"/>
+      </material>
+    </visual>
+    <collision>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+	 	    <box size="0.04 0.04 0.04"/>
+      </geometry>
+    </collision>
+  </link>
+</robot>
+