Humanoid robots learn tennis serves and rallies from pro motion data

A hierarchical framework separates a planner that generates stylistic tennis motion from a tracker that executes it, the article says.

The tracker is trained on randomized execution speeds and the planner uses a learned motion-speed representation to reduce sim-to-real errors. The framework was validated on a research humanoid and a full-size humanoid serving outdoors without motion capture.