ExBody2: Advanced Expressive Humanoid Whole-Body Control

Mazeyu Ji, Xuanbin Peng, Fangchen Liu, Jialong Li, Ge Yang, Xuxin Cheng, Xiaolong Wang

Keywords: Humanoid Robot, Reinforcement Learning, Whole-body Control

Abstract: This paper tackles the challenge of enabling real-world humanoid robots to perform expressive and dynamic whole-body motions while maintaining stability. We propose Advanced Expressive Whole-Body Control (ExBody2), a whole-body tracking framework trained in simulation with Reinforcement Learning and then transferred to the real world. The framework decouples keypoint tracking from velocity control and leverages a privileged teacher policy to distill precise mimic skills into the student policy, enabling robust, high-fidelity reproduction of complex motions such as walking, crouching, and dancing. A significant contribution is the discovery of a fundamental principle for balancing feasibility and diversity in motion datasets, which guides the development of an automatic dataset curation method. This principle facilitates pretraining a versatile model generalizing well across diverse motions and can be fine-tuned for specific tasks to achieve superior tracking accuracy. Extensive experiments demonstrate that Exbody2 outperforms existing baselines, establishing new benchmarks and provides valuable insights for the advancement of whole-body humanoid control.

Serve As Reviewer: ~Mazeyu_Ji1, ~Xuanbin_Peng1

Submission Number: 7