Authors: Chenhao Lu, Xuxin Cheng, Jialong Li, Shiqi Yang, Mazeyu Ji, Chengjing Yuan, Ge Yang, Sha Yi, Xiaolong Wang
Abstract: Humanoid robots require both robust lower-body locomotion and precise
upper-body manipulation. While recent Reinforcement Learning (RL) approaches
provide whole-body loco-manipulation policies, they lack precise manipulation
with high DoF arms. In this paper, we propose decoupling upper-body control
from locomotion, using inverse kinematics (IK) and motion retargeting for
precise manipulation, while RL focuses on robust lower-body locomotion. We
introduce PMP (Predictive Motion Priors), trained with Conditional Variational
Autoencoder (CVAE) to effectively represent upper-body motions. The locomotion
policy is trained conditioned on this upper-body motion representation,
ensuring that the system remains robust with both manipulation and locomotion.
We show that CVAE features are crucial for stability and robustness, and
significantly outperforms RL-based whole-body control in precise manipulation.
With precise upper-body motion and robust lower-body locomotion control,
operators can remotely control the humanoid to walk around and explore
different environments, while performing diverse manipulation tasks.
Source: http://arxiv.org/abs/2412.07773v1
