🌏 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 our full data collection (OpenX + Ego4D). For finetuning on simulation benchmarks or your customized dataset, please visit our official repo.

📝 Citation

If you find our code or models useful in your work, please cite our paper:

@article{bu2025univla,
  title={Univla: Learning to act anywhere with task-centric latent actions},
  author={Bu, Qingwen and Yang, Yanting and Cai, Jisong and Gao, Shenyuan and Ren, Guanghui and Yao, Maoqing and Luo, Ping and Li, Hongyang},
  journal={arXiv preprint arXiv:2505.06111},
  year={2025}
}