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 model 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.

翻译：将人类运动技能迁移至人形机器人仍是一项重大挑战。本研究提出一种Wasserstein对抗模仿学习系统，使人形机器人能够通过模仿人类运动，复现全身自然运动模式并执行无缝过渡。首先，我们提出统一基元-骨架运动重定向方法，以消解任意人类演示者与人形机器人之间的形态差异。将对抗性评判组件与强化学习相融合，引导控制策略生成与混合参考运动数据分布一致的行为。进一步采用特定积分概率度量——即带有新型软边界约束的Wasserstein-1距离，以稳定训练过程并防止模型崩溃。该系统在全尺寸人形机器人JAXON的仿真环境中完成验证。最终控制策略展现出包含站立、推拉恢复、蹲姿行走、类人直腿行走及动态奔跑在内的多种运动模式。值得注意的是，即便演示数据集中不包含过渡动作，机器人仍能在期望速度变化时涌现出在不同运动模式间自然过渡的能力。