Li-Peng Chen

Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking

This work proposes AdaPT, an Adaptive Motion Planning and Tracking framework that learns professional tennis serving and rally styles directly from broadcast videos, and improves tracking robustness by learning to track randomized execution speeds, while conditioning the planner on a learned motion-speed adapter to mitigate compounding errors.