Dribble Master: Learning Agile Humanoid Dribbling Through Legged Locomotion

Humanoid soccer dribbling is a highly challenging task that demands dexterous ball manipulation while maintaining dynamic balance. Traditional rule-based methods often struggle to achieve accurate ball control due to their reliance on fixed walking patterns and limited adaptability to real-time ball dynamics. To address these challenges, we propose a two-stage curriculum learning framework that enables a humanoid robot to acquire dribbling skills without explicit dynamics or predefined trajectories. In the first stage, the robot learns basic locomotion skills; in the second stage, we fine-tune the policy for agile dribbling maneuvers. We further introduce a virtual camera model in simulation and design heuristic rewards to encourage active sensing, promoting a broader visual range for continuous ball perception. The policy is trained in simulation and successfully transferred to a physical humanoid robot. Experimental results demonstrate that our method enables effective ball manipulation, achieving flexible and visually appealing dribbling behaviors across multiple environments. This work highlights the potential of reinforcement learning in developing agile humanoid soccer robots.

We sincerely thank Booster Robotics for humanoid robot T1 hardware support and help in experiments. We appreciate valuable suggestions and guidance from Professor Mingguo Zhao. We also appreciate discussions with Professor Rui Chen, Professor Li Liu, Yushi Wang, Penghui Chen, Liqian Ma, Zhenghao Qi, Yongqiang Dou, Gabriel Margolis and Chong Zhang.