Safe Learning in the Real World via Adaptive Shielding with Hamilton-Jacobi Reachability
Abstract
We present a robust shielding framework using Hamilton-Jacobi Reachability that can be combined with any off-policy Reinforcement Learning to enable safer learning. Using an approximate model of a system dynamics, our method can capture the local model mismatch from a safety perspective. This leads to a more conservative safety filter that can adapt to model mismatch. Using a Turtlebot 2, we demonstrate that our method can allow for safe learning in the real-world with minimal human intervention.
Cite
Text
Lu et al. "Safe Learning in the Real World via Adaptive Shielding with Hamilton-Jacobi Reachability." Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, 2025.Markdown
[Lu et al. "Safe Learning in the Real World via Adaptive Shielding with Hamilton-Jacobi Reachability." Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, 2025.](https://mlanthology.org/l4dc/2025/lu2025l4dc-safe/)BibTeX
@inproceedings{lu2025l4dc-safe,
title = {{Safe Learning in the Real World via Adaptive Shielding with Hamilton-Jacobi Reachability}},
author = {Lu, Michael and Gosain, Jashanraj and Sang, Luna and Chen, Mo},
booktitle = {Proceedings of the 7th Annual Learning for Dynamics \& Control Conference},
year = {2025},
pages = {1257-1270},
volume = {283},
url = {https://mlanthology.org/l4dc/2025/lu2025l4dc-safe/}
}