双层运动模仿对人形机器人的应用

Bi-Level Motion Imitation for Humanoid Robots

October 2, 2024

作者: Wenshuai Zhao, Yi Zhao, Joni Pajarinen, Michael Muehlebach

cs.AI

摘要

从人体动作捕捉（MoCap）数据进行模仿学习为训练类人机器人提供了一种有前途的方法。然而，由于形态学上的差异，如不同程度的关节自由度和力量限制，对于类人机器人来说，精确复制人类行为可能并非可行。因此，在训练数据集中加入在物理上不可行的MoCap数据可能会对机器人策略的性能产生不利影响。为了解决这个问题，我们提出了一种基于双层优化的模仿学习框架，该框架在优化机器人策略和目标MoCap数据之间交替进行。具体而言，我们首先利用一种新颖的自洽自动编码器开发了一个生成式潜在动力学模型，该模型学习稀疏且结构化的运动表示，同时捕捉数据集中所需的运动模式。然后利用动力学模型生成参考运动，而潜在表示对双层运动模仿过程进行规范化。通过使用类人机器人的真实模型进行的模拟表明，我们的方法通过修改参考运动以使其在物理上一致，提高了机器人策略的性能。

English

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.