Humanoid Whole-Body Badminton via Multi-Stage Reinforcement Learning

Humanoid robots have demonstrated strong capability for interacting with deterministic scenes across locomotion, manipulation, and more challenging loco-manipulation tasks. Yet the real world is dynamic, quasi-static interactions are insufficient to cope with the various environmental conditions. As a step toward more dynamic interaction scenario, we present a reinforcement-learning-based training pipeline that produces a unified whole-body controller for humanoid badminton, enabling coordinated lower-body footwork and upper-body striking without any motion priors or expert demonstrations. Training follows a three-stage curriculum: first footwork acquisition, then precision-guided racket swing generation, and finally task-focused refinement, yielding motions in which both legs and arms serve the hitting objective. For deployment, we incorporate an Extended Kalman Filter (EKF) to estimate and predict shuttlecock trajectories for target striking. We also introduce a prediction-free variant that dispenses with EKF and explicit trajectory prediction. To validate the framework, we conduct five sets of experiment in both simulation and the real world. In simulation, two robots sustain a rally of 21 consecutive hits. Moreover, the prediction-free variant achieves successful hits with comparable performance relative to the target-known policy. In real-world tests, both the prediction and controller module exhibit high accuracy, and on-court hitting achieves an outgoing shuttle speed up to 10 m/s with a mean return landing distance of 3.5 m. These experiment results show that our humanoid robot can deliver highly dynamic while precise goal striking in badminton, and can be adapted to more dynamism critical domains.