Bi-Level Motion Imitation for Humanoid Robots

Wenshuai Zhao, Yi Zhao, Joni Pajarinen, Michael Muehlebach

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the Conference on Robot Learning
PublisherJMLR
Number of pages19
Publication statusAccepted/In press - 2024
MoE publication typeA4 Conference publication
EventConference on Robot Learning - Munich, Germany
Duration: 6 Nov 20249 Nov 2024
https://www.corl.org/

Conference

ConferenceConference on Robot Learning
Country/TerritoryGermany
CityMunich
Period06/11/202409/11/2024
Internet address

Keywords

  • Humanoid Robots
  • Imitation Learning
  • Latent Dynamics Model

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