Create README.md

README.md

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+---
+# Example metadata to be added to a dataset card.  
+# Full dataset card template at https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md
+language:
+- en
+license: mit  # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
+tags:
+- robotics
+- manipulation
+- rearrangement
+- computer-vision
+- reinforcement-learning
+- imitation-learning
+- rgbd
+- rgb
+- depth
+- low-level-control
+- whole-body-control
+- home-assistant
+- simulation
+- maniskill
+annotations_creators:
+- machine-generated  # Generated from RL policies with filtering
+language_creators:
+- machine-generated
+language_details: en-US
+pretty_name: ManiSkill-HAB TidyHouse Dataset
+size_categories:
+- 1M<n<10M  # Dataset has 18K episodes with 3.6M transitions
+# source_datasets:  # None, original
+task_categories:
+- robotics
+- reinforcement-learning
+task_ids:
+- grasping
+- task-planning
+
+configs:
+  - config_name: pick-002_master_chef_can
+    data_files:
+    - split: trajectories
+      path: pick/002_master_chef_can.h5
+    - split: metadata
+      path: pick/002_master_chef_can.json
+  
+  - config_name: pick-003_cracker_box
+    data_files:
+    - split: trajectories
+      path: pick/003_cracker_box.h5
+    - split: metadata
+      path: pick/003_cracker_box.json
+  
+  - config_name: pick-004_sugar_box
+    data_files:
+    - split: trajectories
+      path: pick/004_sugar_box.h5
+    - split: metadata
+      path: pick/004_sugar_box.json
+  
+  - config_name: pick-005_tomato_soup_can
+    data_files:
+    - split: trajectories
+      path: pick/005_tomato_soup_can.h5
+    - split: metadata
+      path: pick/005_tomato_soup_can.json
+  
+  - config_name: pick-007_tuna_fish_can
+    data_files:
+    - split: trajectories
+      path: pick/007_tuna_fish_can.h5
+    - split: metadata
+      path: pick/007_tuna_fish_can.json
+  
+  - config_name: pick-008_pudding_box
+    data_files:
+    - split: trajectories
+      path: pick/008_pudding_box.h5
+    - split: metadata
+      path: pick/008_pudding_box.json
+  
+  - config_name: pick-009_gelatin_box
+    data_files:
+    - split: trajectories
+      path: pick/009_gelatin_box.h5
+    - split: metadata
+      path: pick/009_gelatin_box.json
+  
+  - config_name: pick-010_potted_meat_can
+    data_files:
+    - split: trajectories
+      path: pick/010_potted_meat_can.h5
+    - split: metadata
+      path: pick/010_potted_meat_can.json
+  
+  - config_name: pick-024_bowl
+    data_files:
+    - split: trajectories
+      path: pick/024_bowl.h5
+    - split: metadata
+      path: pick/024_bowl.json
+  
+  - config_name: place-002_master_chef_can
+    data_files:
+    - split: trajectories
+      path: place/002_master_chef_can.h5
+    - split: metadata
+      path: place/002_master_chef_can.json
+  
+  - config_name: place-003_cracker_box
+    data_files:
+    - split: trajectories
+      path: place/003_cracker_box.h5
+    - split: metadata
+      path: place/003_cracker_box.json
+  
+  - config_name: place-004_sugar_box
+    data_files:
+    - split: trajectories
+      path: place/004_sugar_box.h5
+    - split: metadata
+      path: place/004_sugar_box.json
+  
+  - config_name: place-005_tomato_soup_can
+    data_files:
+    - split: trajectories
+      path: place/005_tomato_soup_can.h5
+    - split: metadata
+      path: place/005_tomato_soup_can.json
+  
+  - config_name: place-007_tuna_fish_can
+    data_files:
+    - split: trajectories
+      path: place/007_tuna_fish_can.h5
+    - split: metadata
+      path: place/007_tuna_fish_can.json
+  
+  - config_name: place-008_pudding_box
+    data_files:
+    - split: trajectories
+      path: place/008_pudding_box.h5
+    - split: metadata
+      path: place/008_pudding_box.json
+  
+  - config_name: place-009_gelatin_box
+    data_files:
+    - split: trajectories
+      path: place/009_gelatin_box.h5
+    - split: metadata
+      path: place/009_gelatin_box.json
+  
+  - config_name: place-010_potted_meat_can
+    data_files:
+    - split: trajectories
+      path: place/010_potted_meat_can.h5
+    - split: metadata
+      path: place/010_potted_meat_can.json
+  
+  - config_name: place-024_bowl
+    data_files:
+    - split: trajectories
+      path: place/024_bowl.h5
+    - split: metadata
+      path: place/024_bowl.json
+
+# # Optional. This part can be used to store the feature types and size of the dataset to be used in python. This can be automatically generated using the datasets-cli.
+# dataset_info:
+#   features:
+#     - name: {feature_name_0}    # Example: id
+#       dtype: {feature_dtype_0}  # Example: int32
+#     - name: {feature_name_1}    # Example: text
+#       dtype: {feature_dtype_1}  # Example: string
+#     - name: {feature_name_2}    # Example: image
+#       dtype: {feature_dtype_2}  # Example: image
+#     # Example for SQuAD:
+#     # - name: id
+#     #   dtype: string
+#     # - name: title
+#     #   dtype: string
+#     # - name: context
+#     #   dtype: string
+#     # - name: question
+#     #   dtype: string
+#     # - name: answers
+#     #   sequence:
+#     #     - name: text
+#     #       dtype: string
+#     #     - name: answer_start
+#     #       dtype: int32
+#   config_name: {config_name}  # Name of the dataset subset. Example for glue: sst2
+#   splits:
+#     - name: {split_name_0}                  # Example: train
+#       num_bytes: {split_num_bytes_0}        # Example for SQuAD: 79317110
+#       num_examples: {split_num_examples_0}  # Example for SQuAD: 87599
+#   download_size: {dataset_download_size}   # Example for SQuAD: 35142551
+#   dataset_size: {dataset_size}             # Example for SQuAD: 89789763
+
+# It can also be a list of multiple subsets (also called "configurations"):
+# ```yaml
+# dataset_info:
+#   - config_name: {config0}
+#     features:
+#       ...
+#   - config_name: {config1}
+#     features:
+#       ...
+# ```
+
+# # Optional. If you want your dataset to be protected behind a gate that users have to accept to access the dataset. More info at https://huggingface.co/docs/hub/datasets-gated
+# extra_gated_fields:
+# - {field_name_0}: {field_type_0}  # Example: Name: text
+# - {field_name_1}: {field_type_1}  # Example: Affiliation: text
+# - {field_name_2}: {field_type_2}  # Example: Email: text
+# - {field_name_3}: {field_type_3}  # Example for speech datasets: I agree to not attempt to determine the identity of speakers in this dataset: checkbox
+# extra_gated_prompt: {extra_gated_prompt}  # Example for speech datasets: By clicking on “Access repository” below, you also agree to not attempt to determine the identity of speakers in the dataset.
+
+# # Optional. Add this if you want to encode a train and evaluation info in a structured way for AutoTrain or Evaluation on the Hub
+# train-eval-index:
+#   - config: {config_name}           # The dataset subset name to use. Example for datasets without subsets: default. Example for glue: sst2
+#     task: {task_name}               # The task category name (same as task_category). Example: question-answering
+#     task_id: {task_type}            # The AutoTrain task id. Example: extractive_question_answering
+#     splits:
+#       train_split: train            # The split to use for training. Example: train
+#       eval_split: validation        # The split to use for evaluation. Example: test
+#     col_mapping:                    # The columns mapping needed to configure the task_id.
+#     # Example for extractive_question_answering:
+#       # question: question
+#       # context: context
+#       # answers:
+#       #   text: text
+#       #   answer_start: answer_start
+#     metrics:
+#       - type: {metric_type}         # The metric id. Example: wer. Use metric id from https://hf.co/metrics
+#         name: {metric_name}         # Tne metric name to be displayed. Example: Test WER
+---
+
+# ManiSkill-HAB TidyHouse Dataset
+
+**[Paper (arXiv TBA)]()** 
+| **[Website](https://arth-shukla.github.io/mshab)** 
+| **[Code](https://github.com/arth-shukla/mshab)** 
+| **[Models](https://huggingface.co/arth-shukla/mshab_checkpoints)** 
+| **[(Full) Dataset](https://arth-shukla.github.io/mshab/#dataset-section)** 
+| **[Supplementary](https://sites.google.com/view/maniskill-hab)**
+
+
+<!-- Provide a quick summary of the dataset. -->
+
+Whole-body, low-level control/manipulation demonstration dataset for ManiSkill-HAB TidyHouse.
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+Demonstration dataset for ManiSkill-HAB TidyHouse. Each subtask/object combination (e.g pick 002_master_chef_can) has 1000 successful episodes (200 samples/demonstration) gathered using [RL policies](https://huggingface.co/arth-shukla/mshab_checkpoints) fitered for safe robot behavior with a rule-based event labeling system.
+
+TidyHouse contains the Pick and Place subtasks. Relative to the other MS-HAB long-horizon tasks (PrepareGroceries, SetTable), TidyHouse Pick is approximately medium difficulty, while TidyHouse Place is medium-to-hard difficulty (on a scale of easy-medium-hard).
+
+### Related Datasets
+
+Full information about the MS-HAB datasets (size, difficulty, links, etc), including the other long horizon tasks, are available [on the ManiSkill-HAB website](https://arth-shukla.github.io/mshab/#dataset-section).
+
+- [ManiSkill-HAB PrepareGroceries Dataset](https://huggingface.co/datasets/arth-shukla/MS-HAB-PrepareGroceries)
+- [ManiSkill-HAB SetTable Dataset](https://huggingface.co/datasets/arth-shukla/MS-HAB-SetTable)
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+### Direct Use
+
+This dataset can be used to train vision-based learning from demonstrations and imitation learning methods, which can be evaluated with the [MS-HAB environments](https://github.com/arth-shukla/mshab). This dataset may be useful as synthetic data for computer vision tasks as well.
+
+### Out-of-Scope Use
+
+While blind state-based policies can be trained on this dataset, it is recommended to train vision-based policies to handle collisions and obstructions.
+
+## Dataset Structure
+
+Each subtask/object combination has files `[SUBTASK]/[OBJECT].json` and `[SUBTASK]/[OBJECT].h5`. The JSON file contains episode metadata, event labels, etc, while the HDF5 file contains the demonstration data.
+
+## Dataset Creation
+
+<!-- TODO (arth): link paper appendix, maybe html, for the event labeling system -->
+The data is gathered using [RL policies](https://huggingface.co/arth-shukla/mshab_checkpoints) fitered for safe robot behavior with a rule-based event labeling system.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+The dataset is purely synthetic.
+
+While MS-HAB supports high-quality ray-traced rendering, this dataset uses ManiSkill's default rendering for data generation due to efficiency. However, users can generate their own data with the [data generation code](https://github.com/arth-shukla/mshab/blob/main/mshab/utils/gen/gen_data.py).
+
+<!-- TODO (arth): citation -->
+<!-- ## Citation [TBA]
+
+[Citation TBA] -->