KungfuBot2: Learning Versatile Motion Skills for Humanoid Whole-Body Control

We deploy VMS on the Unitree G1 humanoid robot, demonstrating its capability to perform a broad category of motion skills with strong stability and generalization. The repertoire includes (a) walking and running, (b) ball throwing and racket swinging, (c) dancing, (d) diverse kicking, (e) Kung Fu and (f) long sequences of martial arts and dance. Abstract-- Learning versatile whole-body skills by tracking various human motions is a fundamental step toward general-purpose humanoid robots. This task is particularly challenging because a single policy must master a broad repertoire of motion skills while ensuring stability over long-horizon sequences. T o this end, we present VMS, a unified whole-body controller that enables humanoid robots to learn diverse and dynamic behaviors within a single policy. Our framework integrates a hybrid tracking objective that balances local motion fidelity with global trajectory consistency, and an Orthogonal Mixture-of-Experts (OMoE) architecture that encourages skill specialization while enhancing generalization across motions. A segment-level tracking reward is further introduced to relax rigid step-wise matching, enhancing robustness when handling global displacements and transient inaccuracies.