Bi-Level Motion Imitation for Humanoid Robots
Authors: name: Wenshuai Zhao, name: Yi Zhao, name: Joni Pajarinen, name: Michael Muehlebach

Imitation learning from human motion capture (MoCap) data provides a
promising way to train humanoid robots. However, due to differences in
morphology, such as varying degrees of joint freedom and force limits, exact
replication of human behaviors may not be feasible for humanoid robots.
Consequently, incorporating physically infeasible MoCap data in training
datasets can adversely affect the performance of the robot policy. To address
this issue, we propose a bi-level optimization-based imitation learning
framework that alternates between optimizing both the robot policy and the
target MoCap data. Specifically, we first develop a generative latent dynamics
model using a novel self-consistent auto-encoder, which learns sparse and
structured motion representations while capturing desired motion patterns in
the dataset. The dynamics model is then utilized to generate reference motions
while the latent representation regularizes the bi-level motion imitation
process. Simulations conducted with a realistic model of a humanoid robot
demonstrate that our method enhances the robot policy by modifying reference
motions to be physically consistent.