---
title: MuseVLA-dataset
canonical_url: "https://www.modelscope.cn/datasets/microsoft/MuseVLA-dataset"
md_url: "https://www.modelscope.cn/datasets/microsoft/MuseVLA-dataset.md"
repository: microsoft/MuseVLA-dataset
last_updated: 2026-08-14
license: cc-by-nc-4.0
storage_size: "110 GB"
downloads: 102034
stars: 0
---

# MuseVLA-dataset

> MuseVLA-dataset - microsoft 在 ModelScope 开源的数据集。Multi-modal robot manipulation dataset with synchronized RGB, depth, acoustic, thermal, and radar streams. Released as two parts (dataset01/, dataset02/) sharing the same per-episode layout. Together they cover…

microsoft/MuseVLA-dataset 是 ModelScope 魔搭社区上的数据集，存储大小 110 GB，采用 cc-by-nc-4.0 许可。

- **Repository**: microsoft/MuseVLA-dataset
- **License**: cc-by-nc-4.0
- **Storage size**: 110 GB
- **Downloads**: 102034
- **Stars**: 0
- **Last updated**: 2026-08-14

Source: https://www.modelscope.cn/datasets/microsoft/MuseVLA-dataset

---

<div align="center">
  <span style="font-size:32px;">MuseVLA Dataset</span>
</div>

<p align="center">
  <a href="https://arxiv.org/abs/2606.17598"><img src="https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white" alt="arXiv"></a>
  <a href="https://github.com/microsoft/MuseVLA"><img src="https://img.shields.io/badge/Code-GitHub-181717?logo=github&logoColor=white" alt="Code Repository"></a>
  <a href="https://huggingface.co/microsoft/MuseVLA"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue" alt="Hugging Face Model"></a>
</p>

Multi-modal robot manipulation dataset with synchronized RGB, depth, acoustic,
thermal, and radar streams. Released as two parts (`dataset_01/`,
`dataset_02/`) sharing the same per-episode layout. Together they cover
~1400 episodes across 11 instructions (towel / clothes / box / item / drink
manipulation).

## Per-episode contents

```
{episode_name}/
├── video.mp4                           # RGB, 1280×720, 30 fps
├── mask/video.mp4                      # SAM3 segmentation mask
├── depth/images/                       # depth jpgs
├── acoustic/{images,images_masked}/    # spectrogram + 3 overlay variants
├── radar/{images,images_masked}/       # radar + 3 overlay variants
├── thermal/{left,right,images,images_masked}/   # left / right / merged / overlays
├── 6d_pose/right_arm.npy               # end-effector trajectory
├── arm_command/, arm_status/, ee_command/, hand_status/   # control signals
```

Annotations live under `*_labels/{episode}.json` with per-segment
`instruction`, `start`, `end`, plus `_validity.json` recording SAM3 validity.
Top-level `instructions_all.json`, `valid_samples.json`, and
`statistics_6dpose_delta_NRt0.json` are provided for indexing and
normalization.

## Statistics

| Episodes | Valid | Annotated segments | Unique instructions | RGB | Frame rate |
|---:|---:|---:|---:|---|---|
| 1397 (1279 + 118) | 1010 (913 + 97) | 1930 (1737 + 193) | 11 | 1280 × 720 | 30 fps |

## Citation

```bibtex
@misc{liu2026musevlaadaptivemultimodalsensing,
  title={MuseVLA: An Adaptive Multimodal Sensing Vision-Language-Action Model for Robotic Manipulation},
  author={Xingyuming Liu and Ruichun Ma and Heyu Guo and Qixiu Li and Qingwen Yang and Lin Luo and Shiqi Jiang and Chenren Xu and Jiaolong Yang and Baining Guo},
  year={2026},
  eprint={2606.17598},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  url={https://arxiv.org/abs/2606.17598},
}
```

## License

CC-BY-NC-4.0 (research use only).