title: Bi-Level Motion Imitation for Humanoid Robots
summary: 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.
paper_authors._id: 672128d621a9bd12aad5bae2
paper_authors.name: Wenshuai Zhao
paper_authors._id: 672128d621a9bd12aad5bae3
paper_authors.name: Yi Zhao
paper_authors._id: 672128d621a9bd12aad5bae4
paper_authors.name: Joni Pajarinen
paper_authors._id: 672128d621a9bd12aad5bae5
paper_authors.name: Michael Muehlebach
paper_title: Bi-Level Motion Imitation for Humanoid Robots
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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