Authors: Mazeyu Ji, Xuanbin Peng, Fangchen Liu, Jialong Li, Ge Yang, Xuxin Cheng, Xiaolong Wang
Abstract: This paper enables real-world humanoid robots to maintain stability while
performing expressive motions like humans do. We propose ExBody2, a generalized
whole-body tracking framework that can take any reference motion inputs and
control the humanoid to mimic the motion. The model is trained in simulation
with Reinforcement Learning and then transferred to the real world. It
decouples keypoint tracking with velocity control, and effectively leverages a
privileged teacher policy to distill precise mimic skills into the target
student policy, which enables high-fidelity replication of dynamic movements
such as running, crouching, dancing, and other challenging motions. We present
a comprehensive qualitative and quantitative analysis of crucial design factors
in the paper. We conduct our experiments on two humanoid platforms and
demonstrate the superiority of our approach against state-of-the-arts,
providing practical guidelines to pursue the extreme of whole-body control for
humanoid robots.
Source: http://arxiv.org/abs/2412.13196v1
