README.md

---
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
pipeline_tag: robotics
tags: [robotics, vla, openpi, manipulation, maniguard, franka]
---

# pi0-sim2real-clutter-task0048-sim-lora

A ManiGuard fine-tuned VLA checkpoint (openpi).
**ManiGuard**: [paper (arXiv:2608.17386)](https://arxiv.org/abs/2608.17386) ·
[code](https://github.com/NU-IDEAS-Lab/ManiGuard) ·
[docs](https://nu-ideas-lab.github.io/ManiGuard/)

## Citation

```bibtex
@misc{peng2026maniguard,
  title         = {{MANIGUARD}: A Benchmark and Data Suite for Specification-Grounded
                   Safety Evaluation and Improvement of Robotic Manipulation},
  author        = {Peng, Yiyan and Wang, Philip and Zhan, Simon Sinong and Lyu, Yiqi
                   and Ni, Zhenyang and Yan, Jixin and Wong, Fiorelli and Jiao, Ruochen
                   and Yin, Hang and Cao, Xinyu and Shao, Huajie and Li, Manling
                   and Zhang, Ruohan and Zhu, Qi},
  year          = {2026},
  eprint        = {2608.17386},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url           = {https://arxiv.org/abs/2608.17386},
}
```

## License

The fine-tuned weights derive from a Physical Intelligence openpi base model whose
VLM backbone is PaliGemma; use of these weights is therefore subject to the
[Gemma Terms of Use](https://ai.google.dev/gemma/terms) (including the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy)),
which downstream users must pass on. The openpi training code and ManiGuard's own
contributions are Apache-2.0.