---
license: apache-2.0
task_categories:
  - robotics
tags:
  - robotics
  - vla
  - world-model
  - manipulation
  - imitation-learning
  - rgbd
size_categories:
  - 1K<n<10K
---

<div align="center">

# X-WAM

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

[![Paper](https://img.shields.io/badge/📄-Paper-red)](https://arxiv.org/abs/2604.26694)
[![Project Page](https://img.shields.io/badge/🌐-Project_Page-blue)](https://sharinka0715.github.io/X-WAM/)
[![Code](https://img.shields.io/badge/💻-Code-orange)](https://github.com/sharinka0715/X-WAM)
[![License](https://img.shields.io/badge/License-Apache_2.0-green.svg)](LICENSE)

</div>

---

## Dataset Summary

This is the **RoboCasa** fine-tuning dataset used to train the [**X-WAM**](https://github.com/sharinka0715/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](https://robocasa.ai/)
- **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

| | |
| :--- | :--- |
| Episodes | **1,235** |
| Total frames | **341,017** |
| Avg. frames / episode | ~276 |
| Camera views | 3 (2 static + 1 wrist) |
| Video resolution | 256 × 256, H.264 |
| Frame rate | 20 fps |
| Instructions / episode | 1 |
| Total size | ~5.1 GB |

---

## 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.:

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

### Camera Views

| View | Type | Description |
| :--- | :--- | :--- |
| `robot0_agentview_left`  | static  | Left third-person view |
| `robot0_agentview_right` | static  | Right third-person view |
| `robot0_eye_in_hand`     | dynamic | Wrist-mounted (eye-in-hand) view |

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

| Field | Type | Description |
| :--- | :--- | :--- |
| `num_frames` | `int` | Number of frames `N` in the episode |
| `instructions` | `list[str]` | Natural-language task description (1 per episode) |
| `observations` | `dict` | Per-camera video references (see below) |
| `proprios` | `dict` | Per-frame proprioceptive state, length `N` |
| `actions` | `dict` | Per-frame actions, length `N-1` |

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

```json
{
  "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)

| Group | Key | Dim | Description |
| :--- | :--- | :--- | :--- |
| `proprios` | `left_ee_pos`      | 3 | EE position (xyz), absolute |
| `proprios` | `left_ee_rotm`     | 9 | EE rotation matrix (flattened 3×3), absolute |
| `proprios` | `left_gripper_pos` | 1 | Gripper opening |
| `actions`  | `left_ee_pos`      | 3 | Target EE position |
| `actions`  | `left_ee_rotm`     | 9 | Target EE rotation matrix |
| `actions`  | `left_gripper_pos` | 1 | Target gripper opening |
| `actions`  | `raw_actions`      | 7 | Raw environment action |

---

## Usage

```python
# 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**](https://robocasa.ai/) 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:

```bibtex
@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](LICENSE), subject to the
terms of the upstream RoboCasa benchmark.