Learning to Play Like a Pro: Adaptive Motion Planning and Tracking for Humanoid Robots

Researchers developed an adaptive motion planning and tracking framework that enables humanoid robots to replicate professional tennis serving and rally styles learned from broadcast video and motion-capture data. The system separates high-level planning from low-level tracking and uses speed-aware control to improve real-world performance, validated on two humanoid platforms including full-size robots serving without motion capture.