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Humanoid robots learn athletic styles with new adaptive motion framework

By PulseAugur Editorial · [1 sources] ·

A new white paper from IEEE Spectrum and Wiley, sponsored by Noitom Robotics, details an adaptive motion planning and tracking framework for humanoid robots. This framework aims to enable robots to replicate professional athletic styles in ball sports by decoupling high-level planning from low-level tracking. It utilizes motion data from broadcast video and motion capture, with a focus on improving the sim-to-real transition by training trackers on randomized speeds and incorporating a learned motion-speed adapter. AI

IMPACT This framework could enable more sophisticated and human-like movements in humanoid robots for sports and other applications.

RANK_REASON The cluster describes a white paper detailing a technical framework for robotics research. [lever_c_demoted from research: ic=1 ai=0.7]

Read on IEEE Spectrum — AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Humanoid robots learn athletic styles with new adaptive motion framework

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The cluster describes a white paper detailing a technical framework for robotics research. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. IEEE Spectrum — AI TIER_1 English(EN) · Noitom Robotics ·

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

    <img src="https://spectrum.ieee.org/media-library/noitom-robotics-logo-with-stylized-nr-on-transparent-background.png?id=68382463&amp;width=980" /><br /><br /><p>This White Paper provides roboticists and control engineers with a comprehensive overview of how motion planning and m…

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