This paper tackles the challenge of enabling real-world humanoid robots to perform expressive and dynamic whole-body motions while maintaining overall stability and robustness. We propose Advanced Expressive Whole-Body Control (Exbody2), a method for producing whole-body tracking controllers that are trained on both human motion capture and simulated data and then transferred to the real world. We introduce a technique for decoupling the velocity tracking of the entire body from tracking body landmarks. We use a teacher policy to produce intermediate data that better conforms to the robot's kinematics and to automatically filter away infeasible whole-body motions. This two-step approach enabled us to produce a student policy that can be deployed on the robot that can walk, crouch, and dance. We also provide insight into the trade-off between versatility and the tracking performance on specific motions. We observed significant improvement of tracking performance after fine-tuning on a small amount of data, at the expense of the others.
@article{arxiv.2412.13196,
title = {ExBody2: Advanced Expressive Humanoid Whole-Body Control},
author = {Mazeyu Ji and Xuanbin Peng and Fangchen Liu and Jialong Li and Ge Yang and Xuxin Cheng and Xiaolong Wang},
journal= {arXiv preprint arXiv:2412.13196},
year = {2025}
}