Add MuseVLA model card

README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+- Robotics
+- Vision-Language-Action
+- Manipulation
+- Multimodal
+- Sensor-Fusion
+- Diffusion
+language:
+- en
+pipeline_tag: robotics
+arxiv:
+- 2606.17598
+---
+<div align="center">
+  <span style="font-size:32px;">MuseVLA: An Adaptive Multimodal Sensing Vision-Language-Action Model for Robotic Manipulation</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>
+  <a href="https://huggingface.co/datasets/microsoft/MuseVLA-dataset"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue" alt="Hugging Face Dataset"></a>
+</p>
+
+MuseVLA is an adaptive multimodal sensing Vision-Language-Action (VLA) model
+for robotic manipulation. Built on top of
+[VITRA](https://github.com/microsoft/VITRA), MuseVLA treats novel sensors as
+on-demand tools: it first selects the modality needed for a task, grounds the
+selected sensor observation in the RGB image, and then generates robot actions.
+The model supports thermal, acoustic, and mmWave radar sensing in addition to
+RGB observations. MuseVLA achieves an average success rate of **80.6%** across
+thermal-, audio-, and radar-guided manipulation tasks, as well as **66.7%**
+average success on unseen sensor-guided tasks.
+
+
+All our [code](https://github.com/microsoft/MuseVLA) and
+[pre-trained model weights](https://huggingface.co/microsoft/MuseVLA) are
+licensed under the MIT license.
+
+Please refer to our [paper](https://arxiv.org/abs/2606.17598),
+[code repository](https://github.com/microsoft/MuseVLA), and
+[dataset](https://huggingface.co/datasets/microsoft/MuseVLA-dataset) for more
+details.
+
+
+## Model Summary
+
+- **Model type:** Vision-Language-Action Model
+- **Sensor modalities:** RGB, thermal, acoustic, and mmWave radar
+- **Language(s) (NLP):** en
+- **License:** MIT
+- **Training Dataset:** [MuseVLA-dataset](https://huggingface.co/datasets/microsoft/MuseVLA-dataset)
+- **Repository:** [https://github.com/microsoft/MuseVLA](https://github.com/microsoft/MuseVLA)
+- **Paper:** [MuseVLA: An Adaptive Multimodal Sensing Vision-Language-Action Model for Robotic Manipulation](https://arxiv.org/abs/2606.17598)
+
+## 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},
+}
+```