Playing Soccer Like Humans Do: Learning Versatile and Agile Humanoid Soccer via Latent Motion Priors
Overview
This research presents soccerbot, a hierarchical reinforcement learning framework that combines agile locomotion and ball manipulation for humanoids. By using latent motion priors distilled from large-scale human motion capture data, the system enables unified skill execution including moving, dribbling, trapping, and kicking with human-like motion quality. The approach transfers zero-shot to the Unitree G1 humanoid robot, demonstrating robust performance in real-world conditions.
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- arXiv Robotics — research abstractsSignalPlaying Soccer Like Humans Do: Learning Versatile and Agile Humanoid Soccer via Latent Motion Priors
This research presents soccerbot, a hierarchical reinforcement learning framework that combines agile locomotion and ball manipulation for humanoids. By using latent motion priors distilled from large-scale human motion capture data, the system enables unified skill execution including moving, dribbling, trapping, and kicking with human-like motion quality. The approach transfers zero-shot to the Unitree G1 humanoid robot, demonstrating robust performance in real-world conditions.
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