metadata
license: apache-2.0
pipeline_tag: robotics
SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning
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SafeVLA is a family of vision-language-action models (VLAs) aligned for safety using an integrated safety approach (ISA). By modeling safety requirements and utilizing safe reinforcement learning within a constrained Markov decision process (CMDP) paradigm, SafeVLA explicitly integrates safety constraints into generalist robot policies. The model achieves an effective balance between task performance and safety violation mitigation, handling long-tail risks and extreme failure scenarios in mobile manipulation tasks.
Citation
If you find our code or models useful in your work, please cite our paper:
@inproceedings{zhang25safevla,
title={SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning},
author={Borong Zhang and Yuhao Zhang and Jiaming Ji and Yingshan Lei and Josef Dai and Yuanpei Chen and Yaodong Yang},
booktitle={Thirty-ninth Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=dt940loCBT}
}