ExBody2: Advanced Expressive Humanoid 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 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 identification of an empirical trade-off between 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 show that Exbody2 achieves consistently better performance than strong baselines and provides insights that may inform future work on whole-body humanoid control.