BadmintonLab: A Unified Physics-Based Platform for Humanoid Badminton Learning

Recent advances in embodied intelligence are pushing humanoid robots beyond locomotion toward dynamic tasks that require active interaction with the physical world. Racket sports provide a particularly demanding setting, where control must be concentrated within a brief window of precise contact, while the effectiveness of each stroke becomes evident only from the ensuing trajectory. Badminton makes these challenges especially pronounced due to the fast and highly dynamic flight of the shuttlecock and the stringent spatiotemporal precision required for racket control. We introduce BadmintonLab, a unified physics-based platform that provides three progressively richer learning settings for task-oriented skill learning, reference-guided style learning, and competitive rally learning. To enable effective learning across these settings, the platform employs compact event-driven task objectives that provide efficient learning signals for racket–shuttle interaction and shot outcomes. Experiments demonstrate distinct badminton skills, reference-guided motion styles, and competitive rally learning, and further show reuse of the same task and learning interfaces across different humanoid embodiments.