H^2MARL: Hierarchical Heterogeneous Multi-Agent Reinforcement Learning Framework for Humanoid Whole-Body Control

The authors propose H^2MARL, a hierarchical heterogeneous multi-agent reinforcement learning framework for humanoid whole-body control. The method performs policy factorization with physical heterogeneity awareness at the joint degree of freedom (JDoF) level, conditioning each torque-actuated joint on descriptors of its own actuation, kinematic, and dynamic properties, and uses closed-loop policy-executor co-optimization to jointly adapt the high-level pose planner and low-level joint executor with shared interaction trajectories.