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

• URL: http://arxiv.org/abs/2509.16638v1
• Date: Sat, 20 Sep 2025 11:31:14 GMT
• Title: KungfuBot2: Learning Versatile Motion Skills for Humanoid Whole-Body Control
• Authors: Jinrui Han, Weiji Xie, Jiakun Zheng, Jiyuan Shi, Weinan Zhang, Ting Xiao, Chenjia Bai,
• Abstract summary: We present VMS, a unified whole-body controller that enables humanoid robots to learn diverse and dynamic behaviors within a single policy.<n>Our framework integrates a hybrid tracking objective that balances local motion fidelity with global trajectory consistency.<n>We validate VMS specialization extensively in both simulation and real-world experiments, demonstrating accurate imitation of dynamic skills, stable performance over minute-long sequences, and strong generalization to unseen motions.
• Score: 30.738592041595933
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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. To 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. We validate VMS extensively in both simulation and real-world experiments, demonstrating accurate imitation of dynamic skills, stable performance over minute-long sequences, and strong generalization to unseen motions. These results highlight the potential of VMS as a scalable foundation for versatile humanoid whole-body control. The project page is available at https://kungfubot2-humanoid.github.io.

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