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--- |
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license: apache-2.0 |
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library_name: transformers |
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pipeline_tag: robotics |
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--- |
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# 🌏 UniVLA |
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> This is the official checkpoint of our RSS 2025 work: **Learning to Act Anywhere with Task-centric Latent Actions** |
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#### Paper: https://arxiv.org/pdf/2505.06111 |
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#### Code: https://github.com/OpenDriveLab/UniVLA |
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## 🔥 Highlights |
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- A recipe towards generalist policy by planning in a unified, embodiment-agnostic action space. |
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- A novel approach for extracting task-centric latent actions from cross-embodiment videos. |
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- A VLA that achieves state-of-the-art results on multiple benchmarks with compute-efficient training. |
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## How to use |
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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](https://github.com/OpenDriveLab/UniVLA). |
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## 📝 Citation |
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If you find our code or models useful in your work, please cite [our paper](https://arxiv.org/pdf/2505.06111): |
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```bibtex |
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@article{bu2025univla, |
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title={Univla: Learning to act anywhere with task-centric latent actions}, |
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author={Bu, Qingwen and Yang, Yanting and Cai, Jisong and Gao, Shenyuan and Ren, Guanghui and Yao, Maoqing and Luo, Ping and Li, Hongyang}, |
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journal={arXiv preprint arXiv:2505.06111}, |
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year={2025} |
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} |
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``` |
