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--- |
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license: cc |
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--- |
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# RoboCasa-Cosmos-Policy |
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## Dataset Description |
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RoboCasa-Cosmos-Policy is a modified version of the [RoboCasa simulation benchmark dataset](https://github.com/robocasa/robocasa), created as part of the Cosmos Policy project. This is the dataset used to train the [Cosmos-Policy-RoboCasa-Predict2-2B](https://huggingface.co/nvidia/Cosmos-Policy-RoboCasa-Predict2-2B) checkpoint. |
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### Key Modifications |
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Our modifications include the following: |
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1. **Higher-resolution images**: Images are saved at 224×224 pixels (vs. 128×128 in the original). |
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2. **No-op actions filtering**: Transitions with "no-op" (zero) actions that don't change the robot's state are filtered out. |
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3. **Success trimming**: Episodes are terminated early when success is detected, removing unnecessary trailing actions. |
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4. **JPEG compression**: Images in the full rollouts set (`all_episodes/` described below) are JPEG-compressed to reduce storage requirements. However, the successes-only set (`success_only/` described below) contains raw images that are not compressed (though they can be compressed post-hoc if desired). |
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5. **Deterministic regeneration**: All demonstrations are replayed in the simulation environment with deterministic seeding for reproducibility. |
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### Dataset Structure |
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The dataset is organized into two main directories: |
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- **`success_only/`**: Contains only successful demonstration episodes (filtered version). These are demonstrations that succeeded when replayed in the simulation environments. This set is used to train Cosmos Policy to generate high-quality actions. |
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- **`all_episodes/`**: Contains all episodes, including both successful and failed demonstrations. This set is used to train Cosmos Policy's world model and value function. |
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Each directory contains data from 24 kitchen manipulation tasks organized into 7 categories: |
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- `kitchen_coffee/` - Coffee machine tasks |
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- `kitchen_doors/` - Cabinet/door manipulation tasks |
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- `kitchen_drawer/` - Drawer manipulation tasks |
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- `kitchen_microwave/` - Microwave tasks |
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- `kitchen_pnp/` - Pick-and-place tasks |
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- `kitchen_sink/` - Sink-related tasks |
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- `kitchen_stove/` - Stove manipulation tasks |
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### Data Format |
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Each HDF5 file in `success_only/` contains: |
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``` |
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data/ |
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├── demo_0/ |
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│ ├── obs/ |
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│ │ ├── robot0_agentview_left_rgb # Left third-person camera images |
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│ │ ├── robot0_agentview_right_rgb # Right third-person camera images |
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│ │ ├── robot0_eye_in_hand_rgb # Wrist camera images |
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│ │ ├── gripper_states # Gripper joint positions |
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│ │ ├── joint_states # Robot joint positions |
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│ │ ├── ee_states # End-effector states (position + orientation) |
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│ │ ├── ee_pos # End-effector position |
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│ │ └── ee_ori # End-effector orientation |
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│ ├── actions # Action sequence |
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│ ├── states # Environment states |
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│ ├── robot_states # Combined robot state (gripper + EEF pos + EEF quat) |
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│ ├── rewards # Sparse rewards (0 until final timestep) |
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│ └── dones # Episode termination flags |
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│ └── task_description (attribute) # Natural language task description |
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├── demo_1/ |
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... |
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``` |
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The `all_episodes/` directory contains rollout data in a different format. Each episode is stored as a separate HDF5 file with the naming pattern: |
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``` |
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episode_data--task={task_name}--{timestamp}--ep={episode_num}--success={True/False}--regen_demo.hdf5 |
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``` |
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Each of these HDF5 files contains: |
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``` |
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# Datasets (arrays) |
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primary_images_jpeg # Left third-person camera images (JPEG compressed), shape: (T, H, W, 3) |
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secondary_images_jpeg # Right third-person camera images (JPEG compressed), shape: (T, H, W, 3) |
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wrist_images_jpeg # Wrist camera images (JPEG compressed), shape: (T, H, W, 3) |
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proprio # Proprioceptive state (gripper + EEF pos + quat), shape: (T, 9) |
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actions # Action sequence, shape: (T, 7) |
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# Attributes (scalars/metadata) |
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success # Boolean: True if episode succeeded, False otherwise |
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task_description # String: Natural language task description |
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``` |
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### Statistics |
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- **Total tasks**: 24 kitchen manipulation tasks |
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- **Demonstrations per task**: ~50 human teleoperation demonstrations per task before filtering |
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- **Success rate**: ~80-90% (varies by task) |
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- **Image resolution**: 224×224×3 (RGB) |
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- **Action dimensions**: 7 (6-DoF end-effector control + 1 gripper) |
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- **Proprioception dimensions**: 9 (2 gripper joints + 3 EEF position + 4 EEF quaternion) |
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### Original RoboCasa Dataset |
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This dataset is derived from the original RoboCasa benchmark: |
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- **Paper**: [RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots](https://arxiv.org/abs/2406.02523) |
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- **Repository**: https://github.com/robocasa/robocasa |
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- **License**: CC BY 4.0 |
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### Citation |
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If you use this dataset, please cite both the original RoboCasa paper and the Cosmos Policy paper. |
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<!-- ```bibtex |
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@article{nasiriany2024robocasa, |
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title={Robocasa: Large-scale simulation of everyday tasks for generalist robots}, |
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author={Nasiriany, Soroush and Maddukuri, Abhiram and Zhang, Lance and Parikh, Adeet and Lo, Aaron and Joshi, Abhishek and Mandlekar, Ajay and Zhu, Yuke}, |
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journal={arXiv preprint arXiv:2406.02523}, |
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year={2024} |
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} |
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# TODO: Add Cosmos Policy BibTeX |
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``` --> |
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### License |
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Creative Commons Attribution 4.0 International (CC BY 4.0) |
