为人类机器人学习足球技能：一种渐进的感知-行动框架
Learning Soccer Skills for Humanoid Robots: A Progressive Perception-Action Framework
AI总结：
本文提出了一种渐进式感知-行动框架，通过分阶段学习提升人类机器人足球技能，实现了高保真踢球与跨环境鲁棒性。
摘要：
足球对人类机器人来说是一个重大挑战，要求在诸如感知引导踢球和全身平衡控制等任务中紧密集成感知-行动能力。现有方法在模块化管道中存在模块间不稳定或端到端框架中训练目标冲突的问题。我们提出了感知-行动集成决策（PAiD），一种渐进式架构，将足球技能获取分解为三个阶段：通过人类运动追踪进行运动技能获取、轻量级感知-行动整合以实现位置泛化、以及具有物理意识的仿真到现实转移。这种分阶段分解建立了稳定的基技能，避免了感知整合期间的奖励冲突，并最小化了仿真到现实的差距。在Unitree G1上的实验显示，能够实现高保真的人类样踢球，在静态或滚动球、各种位置和干扰等多变条件下表现出稳健的性能，同时在室内外场景中保持一致的执行。我们的分而治之策略推进了稳健的人类足球能力，并为复杂的具身技能获取提供了可扩展的框架。项目页面可在https://soccer-humanoid.github.io/上找到。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
