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This work proposes residual reinforcement learning as an implicit safety-filtering mechanism for safe humanoid control and shows that the residual policy can act as an implicit safety filter.
Achieve natural and dynamically feasible humanoid robot motion by retargeting human motion data with a skeleton-aligned approach, significantly reducing inverse kinematics error compared to task-space methods.
