---
license: apache-2.0
library_name: transformers
pipeline_tag: robotics
---

# 🌏 UniVLA
> This is the official checkpoint of our RSS 2025 work: **Learning to Act Anywhere with Task-centric Latent Actions**

#### Paper: https://arxiv.org/pdf/2505.06111
#### Code: https://github.com/OpenDriveLab/UniVLA

## 🔥 Highlights
- A recipe towards generalist policy by planning in a unified, embodiment-agnostic action space.
- A novel approach for extracting task-centric latent actions from cross-embodiment videos.
- A VLA that achieves state-of-the-art results on multiple benchmarks with compute-efficient training.

## How to use

This is the UniVLA pre-trained on BridgeV2 (we used the version in Open-X GCP Bucket). For finetuning on simulation benchmarks or your customized dataset, please visit our [official repo](https://github.com/OpenDriveLab/UniVLA).

## 📝 Citation
If you find our code or models useful in your work, please cite [our paper](https://arxiv.org/pdf/2505.06111):

```bibtex
@article{bu2025univla,
  title={UniVLA: Learning to Act Anywhere with Task-centric Latent Actions}, 
  author={Qingwen Bu and Yanting Yang and Jisong Cai and Shenyuan Gao and Guanghui Ren and Maoqing Yao and Ping Luo and Hongyang Li},
  journal={arXiv preprint arXiv:2505.06111},
  year={2025}
}
```