Bi-Level Motion Imitation for Humanoid Robots
Loading...
Access rights
openAccess
CC BY
CC BY
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
2025
Major/Subject
Mcode
Degree programme
Language
en
Pages
19
Series
Proceedings of Machine Learning Research, Volume 270
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.Description
Keywords
Humanoid Robots, Imitation Learning, Latent Dynamics Model
Other note
Citation
Zhao, W, Zhao, Y, Pajarinen, J & Muehlebach, M 2025, ' Bi-Level Motion Imitation for Humanoid Robots ', Proceedings of Machine Learning Research, vol. 270 . < https://proceedings.mlr.press/v270/zhao25a.html >