基于运动的地面人形足球：多方向踢球库的任务门控强化学习
Locomotion-Grounded Humanoid Soccer: Task-Gated Reinforcement Learning of a Multi-Directional Kicking Library
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中文总结 AI 辅助
提出先训练通用命令条件运动策略，再叠加任务门控踢球技能的人形足球框架，在Unitree G1上实现七种多方向踢球技能，提升射门精度与组合效率。
中文摘要 AI 辅助
近期的人形足球系统将运动跟踪作为基础，并从中推导出运动能力，通常通过将运动参考锚点导向球来实现。这产生了强大的射门效果，但运动能力仅在球接近所引发的狭窄、确定性命令分布上进行训练，从未作为独立能力进行评估。我们反转了这一堆栈：首先训练一个通用的、命令条件的运动策略作为基础，然后将N个运动引导的踢球技能作为任务门控层添加在其上，从而可达步态空间由运动课程而非任何参考片段设定。由于每个技能都从这一可命令状态开始并返回该状态，运动也成为一个组合枢纽（O(N)个转换而非O(N^2)），且击球后的稳定化由训练好的控制器处理，而非按片段脚本化。我们在29自由度Unitree G1上实例化此方法，包含七个重定向踢球技能，覆盖259.5度的名义瞄准方向，包括横向、后向和弱脚击球，这些是单一前向参考无法表达的，并报告了射门精度以及附有完整技能库的命令跟踪、地形和推挤恢复结果，这是先前人形足球系统未报告的轴。该技能库在硬件上针对前向、横向、后向和命令化接近进行了验证。
英文摘要
Recent humanoid soccer systems make motion tracking the substrate and derive locomotion from it, typically by steering a motion-reference anchor toward the ball. This yields strong shooting results, but locomotion is trained only on the narrow, deterministic command distribution ball approach induces, never evaluated as a capability in its own right. We invert the stack: a general, command-conditioned locomotion policy is trained first as the substrate, and N motion-guided kicking skills are added on top as task-gated layers, so the reachable gait space is set by the locomotion curriculum rather than any reference clip. Because every skill starts from and returns to this same commandable state, locomotion also becomes a composition hub (O(N) transitions rather than O(N^2)), and post-strike stabilisation is handed back to the trained controller rather than scripted per clip. We instantiate this on a 29-DoF Unitree G1 with seven retargeted kicking skills spanning 259.5 degrees of nominal aim direction, including lateral, rearward and weak-foot strikes a single forward-facing reference cannot express, and report shooting accuracy alongside command-tracking, terrain and push-recovery results with the full skill library attached, an axis prior humanoid soccer systems do not report. The library is validated on hardware across forward, lateral, rearward and commanded approaches.
发表机构
- KAIST（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
