3 papers
cs.LG2025
Hierarchical Multi-Agent Reinforcement Learning with Control Barrier Functions for Safety-Critical Autonomous Systems
H. M. Sabbir Ahmad, Ehsan Sabouni, Alexander Wasilkoff +6
We address the problem of safe policy learning in multi-agent safety-critical autonomous systems. In such systems, it is necessary for each agent to meet the safety requirements at…
cs.RO2025
GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control
Songyuan Zhang, Oswin So, Kunal Garg +1
Distributed, scalable, and safe control of large-scale multi-agent systems is a challenging problem. In this paper, we design a distributed framework for safe multi-agent control i…
cs.RO2024
Failure Prediction from Limited Hardware Demonstrations
Anjali Parashar, Kunal Garg, Joseph Zhang +1
Prediction of failures in real-world robotic systems either requires accurate model information or extensive testing. Partial knowledge of the system model makes simulation-based f…