Teaching robots to copy human moves without losing the physics

Teaching humanoid robots from human videos requires bridging the gap between different body shapes. Current methods use intermediate kinematic steps that introduce bias and restrict what behaviors robots can learn. This work skips those steps entirely, instead optimizing robot trajectories directly from video using physics simulation and model predictive control. The result: more accurate tracking and faster training for dynamic tasks like balancing. Code will be released.