sharinka0715/X-WAM-RoboCasa

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X-WAM

Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising

   

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Dataset Summary

This is the RoboCasa fine-tuning dataset used to train the **X-WAM** unified 4D World Action Model. It packages single-arm kitchen manipulation demonstrations into a unified multi-view RGB-D video + low-dimensional state/action format, where each episode provides synchronized RGB videos, depth videos, end-effector proprioception, actions, and language instructions.

• —Source benchmark: RoboCasa
• —Embodiment: Single-arm manipulator
• —Tasks: 24 atomic kitchen manipulation tasks (pick-and-place, open/close doors & drawers, press buttons, etc.)
• —Modalities: 3 camera views × (RGB + Depth) + EE proprioception + EE actions + language

Dataset Structure

.
├── data/                          # Per-episode low-dim states, actions & metadata (JSON)
│   └── chunk-{0000..0001}/
│       └── episode_{id}.json
├── video/                         # RGB videos (.mp4, H.264)
│   ├── robot0_agentview_left/chunk-{id}/episode_{id}.mp4
│   ├── robot0_agentview_right/chunk-{id}/episode_{id}.mp4
│   └── robot0_eye_in_hand/chunk-{id}/episode_{id}.mp4
├── depth/                         # Depth videos (.mp4, H.264; same layout as video/)
│   ├── robot0_agentview_left/...
│   ├── robot0_agentview_right/...
│   └── robot0_eye_in_hand/...
└── metadata.json                  # { "chunk-xxxx/episode_xxxxxxx": num_frames }

metadata.json maps each episode key to its number of frames, e.g.:

{
  "chunk-0000/episode_0000000": 413,
  "chunk-0000/episode_0000001": 412
}

Camera Views

Episode Schema (data/.../episode_{id}.json)

Each entry in observations[<camera>] points to the corresponding RGB/Depth clip:

{
  "type": "static",
  "rgb_path":   "video/robot0_agentview_left/chunk-0000/episode_0000000.mp4",
  "depth_path": "depth/robot0_agentview_left/chunk-0000/episode_0000000.mp4",
  "start": 0,
  "end": 413,
  "fps": 20.0
}

State & Action Spaces (single-arm)

Usage

# Please refer to the code repository for full data loading, training and evaluation scripts:
# https://github.com/sharinka0715/X-WAM

Source & Attribution

This dataset is derived from the **RoboCasa** benchmark, re-rendered with multi-view RGB-D and re-packaged into the unified X-WAM format. Please also cite and comply with the license of the original RoboCasa benchmark when using this data.

Citation

If you use this dataset, please cite X-WAM:

@article{guo2026xwam,
  title={Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising},
  author={Guo, Jun and Li, Qiwei and Li, Peiyan and Chen, Zilong and Sun, Nan and Su, Yifei and Wang, Heyun and Zhang, Yuan and Li, Xinghang and Liu, Huaping},
  journal={arXiv preprint arXiv:2604.26694},
  year={2026}
}

License

This dataset is released under the Apache License 2.0, subject to the terms of the upstream RoboCasa benchmark.