Overview
Ensuring safety in dynamic multi-agent systems is challenging due to limited information about the other agents. Control Barrier Functions (CBFs) show promise for safety assurance but current methods make strong assumptions about other agents and often rely on manual tuning to balance safety, feasibility, and performance. In this work, we will delve into the problem of adaptive safe learning for multi-agent systems with CBF. We will show how emergent behavior can be profoundly influenced by the CBF configuration, highlighting the necessity for a responsive and dynamic approach to CBF design.
So far, we have developed ASRL, a novel adaptive safe RL framework, to fully automate the optimization of policy and CBF coefficients, to enhance safety and long-term performance through reinforcement learning. By directly interacting with the other agents, ASRL learns to cope with diverse agent behaviors and maintains the cost violations below a desired limit. We will evaluate ASRL in a multi-robot system and a competitive multi-agent racing scenario, against learning-based and control-theoretic approaches. We will build upon CBF-based control to formulate a theory for safe control synthesis for hybrid dynamical systems.
Video Overview
Publications
- Learning Adaptive Safety for Multi-Agent Systems
Berducci, Yang, Mangharam, Grosu
IEEE International Conference on Robotics and Automation (ICRA), 2024.
Link to Paper
Contributors
Luigi Berducci and Shuo Yang and Rahul Mangharam and Radu Grosu
Citation
@INPROCEEDINGS{10611037,
author={Berducci, Luigi and Yang, Shuo and Mangharam, Rahul and Grosu, Radu},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
title={Learning Adaptive Safety for Multi-Agent Systems},
year={2024},
volume={},
number={},
pages={2859-2865},
keywords={Adaptation models;Costs;Uncertainty;Scalability;Reinforcement learning;Safety;Observability},
doi={10.1109/ICRA57147.2024.10611037}}
