  Transferring human motion skills to humanoid robots remains a significant
challenge. In this study, we introduce a Wasserstein adversarial imitation
learning system, allowing humanoid robots to replicate natural whole-body
locomotion patterns and execute seamless transitions by mimicking human
motions. First, we present a unified primitive-skeleton motion retargeting to
mitigate morphological differences between arbitrary human demonstrators and
humanoid robots. An adversarial critic component is integrated with
Reinforcement Learning (RL) to guide the control policy to produce behaviors
aligned with the data distribution of mixed reference motions. Additionally, we
employ a specific Integral Probabilistic Metric (IPM), namely the Wasserstein-1
distance with a novel soft boundary constraint to stabilize the training
process and prevent mode collapse. Our system is evaluated on a full-sized
humanoid JAXON in the simulator. The resulting control policy demonstrates a
wide range of locomotion patterns, including standing, push-recovery, squat
walking, human-like straight-leg walking, and dynamic running. Notably, even in
the absence of transition motions in the demonstration dataset, robots showcase
an emerging ability to transit naturally between distinct locomotion patterns
as desired speed changes.
