AI RESEARCH
HALO: Learning Human-Robot Collaboration via Heterogeneous-Agent Lyapunov Policy Optimization
arXiv CS.AI
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ArXi:2603.03741v2 Announce Type: replace-cross 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 cooperative joint optimization. The resulting learning problem is a general-sum differentiable game, so independent policy-gradient updates can oscillate or diverge without added structure.