IEEE Spectrum outlines adaptive motion planning for humanoid robots
A new white paper from IEEE Spectrum explores how to make humanoid robots mimic professional athletes’ movements in ball sports. The framework separates motion planning—generating stylistic motions—from tracking—executing those motions reliably. It uses motion data from broadcast video and motion capture to create reference motions, then trains a tracker on varied speeds to reduce errors when…
Key points
- Motion planning and tracking are decoupled to preserve athletic style while ensuring task reliability
- Tracker trained on randomized speeds to reduce sim-to-real errors in physical execution
- Framework validated on two humanoid robots, including a full-size serving demo without motion capture
The approach was tested on two humanoid platforms, including a full-size robot serving without motion capture. The paper highlights practical lessons for engineers aiming to improve humanoid robots’ performance in sports. The white paper is sponsored by Noitom Robotics and available for download.
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Learning to Play Like a Pro: Adaptive Motion Planning and Tracking for Humanoid Robots
IEEE Spectrum AI · 7 October 2026
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This text was published by IEEE Spectrum AI and written by Noitom Robotics. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
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