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Transferring human motion skills to humanoid robots remains a significantchallenge. In this study, we introduce a Wasserstein adversarial imitationlearning system, allowing humanoid robots to replicate natural whole-bodylocomotion patterns and execute seamless transitions by mimicking humanmotions. First, we present a unified primitive-skeleton motion retargeting tomitigate morphological differences between arbitrary human demonstrators andhumanoid robots. An adversarial critic component is integrated withReinforcement Learning (RL) to guide the control policy to produce behaviorsaligned with the data distribution of mixed reference motions. Additionally, weemploy a specific Integral Probabilistic Metric (IPM), namely the Wasserstein-1distance with a novel soft boundary constraint to stabilize the trainingprocess and prevent model collapse. Our system is evaluated on a full-sizedhumanoid JAXON in the simulator. The resulting control policy demonstrates awide range of locomotion patterns, including standing, push-recovery, squatwalking, human-like straight-leg walking, and dynamic running. Notably, even inthe absence of transition motions in the demonstration dataset, robots showcasean emerging ability to transit naturally between distinct locomotion patternsas desired speed changes.

HumanMimic: Learning Natural Locomotion and Transitions for Humanoid Robot via Wasserstein Adversarial Imitation