HierKick: 視覚誘導サッカーロボット制御のための階層的強化学習
HierKick: Hierarchical Reinforcement Learning for Vision-Guided Soccer Robot Control
サッカーロボット制御のための二周波数階層型強化学習フレームワークを提案し、高レベル方策で戦術選択、低レベル制御で精密な関節動作を実現。シミュレーションと実機で高い成功率を達成した。
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著者: Yizhi Chen, Zheng Zhang, Zhanxiang Cao, Yihe Chen, Shengcheng Fu, Liyun Yan, Yang Zhang, Jiali Liu, Haoyang Li, Yue Gao
分類: cs.RO
原文アブストラクト
Controlling soccer robots involves multi-time-scale decision-making, which requires balancing long-term tactical planning and short-term motion execution. Traditional end-to-end reinforcement learning (RL) methods face challenges in complex dynamic environments. This paper proposes HierKick, a vision-guided soccer robot control framework based on dual-frequency hierarchical RL. The framework adopts a hierarchical control architecture featuring a 5 Hz high-level policy that integrates YOLOv8 for real-time detection and selects tasks via a coach model, and a pre-trained 50 Hz low-level controller for precise joint control. Through this architecture, the framework achieves the four steps of approaching, aligning, dribbling, and kicking. Experimental results show that the success rates of this framework are 95.2\% in IsaacGym, 89.8\% in Mujoco, and 80\% in the real world. HierKick provides an effective hierarchical paradigm for robot control in complex environments, extendable to multi-time-scale tasks, with its modular design and skill reuse offering a new path for intelligent robot control.
