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

TL;DR

This paper presents a Wasserstein adversarial imitation learning framework enabling humanoid robots to learn natural locomotion and seamless transitions by mimicking human motions, with stable training and diverse behaviors demonstrated in simulation.

Contribution

It introduces a novel imitation learning system combining motion retargeting, Wasserstein distance, and RL to improve natural locomotion and transition capabilities in humanoid robots.

Findings

Robots can perform diverse locomotion patterns including walking, running, and recovery.

The system enables natural transitions between different locomotion modes.

Training stability is improved using Wasserstein-1 distance with a soft boundary constraint.

Abstract

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.