HALO: Learning Human-Robot Collaboration via Heterogeneous-Agent Lyapunov Policy Optimization 事件

HALO: Learning Human-Robot Collaboration via Heterogeneous-Agent Lyapunov Policy Optimization arXiv:2603.03741v2 Announce Type: replace-cross Abstract: To improve generalization and resilience in human-robot collaboration (HRC), robots must contend with diverse combinations of human behaviors and contexts, motivating multi-agent reinforcement learning (MARL). However, inherent heterogeneity between robots and humans creates a rationality gap (RG), where decentralized policy updates deviate from