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

What happened

How motion data extracted from broadcast video and motion-capture recordings can be reconstructed, retargeted, and physically corrected into reference motions a humanoid (a robot built in roughly human shape) robot can safely track. Why the sim-to-real transition introduces compounding tracking errors, and how training a tracker on randomized execution speeds, paired with a learned motion-speed adapter in the planner, improves robustness on physical hardware.

How this adaptive framework was validated across two humanoid platforms, including full-size, in-the-wild serving performed without any motion-capture system. This White Paper provides roboticists and control engineers with a comprehensive overview of how motion planning and motion tracking can be decoupled to preserve professional athletic style in humanoid robots, and why adaptive, speed-aware control is essential for closing the gap between simulation and real-world deployment.