让机器人一次学会踢球多种技能并灵活切换
SkillX: Unified Multi-Skill Policy Learning for Humanoid Soccer
- 用统一策略同时学习控球、停球、射门等基础动作
- 在仿真和真实机器人上均实现长时间技能组合执行
- 适合研究多技能协同控制与人形机器人运动智能
人形足球是动态全身控制的挑战性测试平台，要求机器人在长时序内协调平衡、移动、物体交互及技能切换。现有方法多依赖任务特定的多阶段流程，难以在单一可部署策略中联合学习和组合多种物体交互技能。为此，我们提出SkillX，一种统一的强化学习框架，通过单一命令条件策略学习并组合多种基础足球技能。SkillX融合三项核心设计：技能特异性对抗运动先验、技能特异性评判器，以及物体感知的时间编码器，使机器人能够执行如控球、停球、射门等原子技能，并在它们之间自然切换。仿真与真实Noetix E1人形机器人实验表明，该方法实现了鲁棒的多技能执行、长时序技能组合，以及成功的仿真到现实部署。
原文摘要 · Abstract (English)
Humanoid soccer is a challenging testbed for dynamic whole-body control, requiring robots to coordinate balance, locomotion, object interaction, and skill switching over long horizons. Existing humanoid sports methods often rely on task-specific multi-stage pipelines, making it difficult to jointly learn and compose multiple object-interactive skills within a single deployable policy. To address this, we present SkillX, a unified reinforcement learning framework that learns and composes multiple atomic soccer skills through a single command-conditioned policy. SkillX integrates three core designs: skill-specific adversarial motion priors, skill-specific critics, and an object-aware temporal encoder, enabling the robot to execute atomic skills and transition among them such as dribbling, trapping, and shooting. Experiments in simulation and on a real Noetix E1 humanoid demonstrate robust multi-skill execution, long-horizon skill composition, and successful sim-to-real deployment.
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