ヒューマノイドロボットのサッカースキル学習：段階的知覚-行動フレームワーク
Learning Soccer Skills for Humanoid Robots: A Progressive Perception-Action Framework
サッカースキル獲得を運動スキル獲得・知覚行動統合・sim-to-real転移の3段階に分解し、Unitree G1で多様な条件下でのキックを実現した研究。
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2. 先行研究と比べてどこがすごい？
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著者: Jipeng Kong, Xinzhe Liu, Yuhang Lin, Jinrui Han, Sören Schwertfeger, Chenjia Bai, Xuelong Li
分類: cs.RO
原文アブストラクト
Soccer presents a significant challenge for humanoid robots, demanding tightly integrated perception-action capabilities for tasks like perception-guided kicking and whole-body balance control. Existing approaches suffer from inter-module instability in modular pipelines or conflicting training objectives in end-to-end frameworks. We propose Perception-Action integrated Decision-making (PAiD), a progressive architecture that decomposes soccer skill acquisition into three stages: motion-skill acquisition via human motion tracking, lightweight perception-action integration for positional generalization, and physics-aware sim-to-real transfer. This staged decomposition establishes stable foundational skills, avoids reward conflicts during perception integration, and minimizes sim-to-real gaps. Experiments on the Unitree G1 demonstrate high-fidelity human-like kicking with robust performance under diverse conditions-including static or rolling balls, various positions, and disturbances-while maintaining consistent execution across indoor and outdoor scenarios. Our divide-and-conquer strategy advances robust humanoid soccer capabilities and offers a scalable framework for complex embodied skill acquisition. The project page is available at https://soccer-humanoid.github.io/.
