分阶段训练人形机器人踢球，实现稳定高效的动作与感知融合。
Learning Soccer Skills for Humanoid Robots: A Progressive Perception-Action Framework
- 分三阶段训练：模仿人类动作、轻量化感知融合、物理仿真到现实迁移
- 在真实环境中对静止或滚动的球均能稳定踢出，抗干扰能力强
- 适合研究复杂具身智能任务的开发，可扩展至其他机器人技能
足球对人形机器人构成重大挑战，需紧密集成感知与动作能力，完成如感知引导踢球和全身平衡控制。现有方法在模块化流程中存在模块间不稳定，或端到端框架中训练目标冲突。我们提出感知-动作一体化决策（PAiD）架构，将足球技能习得分解为三个阶段：通过人体运动追踪获取动作技能，轻量级感知-动作融合实现位置泛化，以及考虑物理特性的仿真到现实迁移。该分阶段设计建立稳定基础技能，避免感知融合时的奖励冲突，并缩小仿真与现实差距。在Unitree G1上的实验表明，系统能高保真复现人类踢球动作，在多种条件下（静止或滚动球、不同位置、外部扰动）表现稳健，且在室内外场景中执行一致。该分而治之策略显著提升了人形机器人足球能力，为复杂具身技能学习提供可扩展框架。项目页面见 https://soccer-humanoid.github.io/。
原文摘要 · Abstract (English)
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/.
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