Bi-Level Motion Imitation for Humanoid Robots
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.
672128d621a9bd12aad5bae2
Wenshuai Zhao
672128d621a9bd12aad5bae3
Yi Zhao
672128d621a9bd12aad5bae4
Joni Pajarinen
672128d621a9bd12aad5bae5
Michael Muehlebach
Bi-Level Motion Imitation for Humanoid Robots
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.
A bi-level optimization framework with a generative latent dynamics model enhances robot policy training by making reference motions physically consistent through a self-consistent auto-encoder.
imitation learning human motion capture (MoCap) humanoid robots bi-level optimization generative latent dynamics model self-consistent auto-encoder sparse motion representations structured motion representations reference motions physically consistent