Humanoid robots have begun to show real capability in ball sports, but preserving a professional athlete’s motion style while maintaining reliable task performance is still an unresolved problem. This guide examines an adaptive motion planning and tracking framework that learns tennis serving and rally styles directly from broadcast video and motion-capture data. A hierarchical design separates a planner, which generates stylistic kinematic motion, from a tracker, which executes that motion with minimal interference. Because tracking performance inevitably degrades when a policy trained in simulation moves to physical hardware, the framework trains its tracker on randomized execution speeds and conditions its planner on a learned motion-speed representation, reducing the compounding errors introduced by autoregressive planning and noisy perception. The paper documents real-world validation on a research humanoid platform and transfer to a full-size humanoid robot, including serving in an uncontrolled environment without motion capture, and closes with practical, transferable lessons for engineers building future humanoid ball-sports systems.
First register your details to create a user profile for the hub, then login to access all content within the hub.
Already registered? Click here to log in to the hub.
Noitom Robotics builds ModalityNet, the human-centric data substrate that makes the physical world learnable for embodied AI.
IEEE Spectrum Magazine, the flagship publication of the IEEE, explores the development, applications and implications of new technologies. It anticipates trends in engineering, science, and technology, and provides a forum for understanding, discussion and leadership in these areas.
1. Same tier 3 topics + same sponsor [1], 2. Same tier 2 topics + same sponsor [1], 3. Any topic + same sponsor [1], 2. Same tags + same sponsor [1], 3. Same sponsor [1],
