发表机构
Noetix Robotics; Tsinghua University（诺蒂克斯机器人公司; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SkillX提出统一强化学习框架，通过命令条件策略整合对抗性运动先验、特定评论家与物体感知编码器，实现人形足球多技能学习与组合，并在仿真和真实机器人上验证。
AI 中文摘要
人形足球是动态全身控制的一个具有挑战性的试验平台，要求机器人在长时间跨度内协调平衡、运动、物体交互和技能切换。现有人形体育方法通常依赖于特定任务的多阶段流水线，这使得在单个可部署策略中联合学习和组合多种物体交互技能变得困难。为了解决这一问题，我们提出了SkillX，一个统一的强化学习框架，通过单一命令条件策略来学习和组合多种原子足球技能。SkillX整合了三个核心设计：技能特定的对抗性运动先验、技能特定的评论家以及物体感知的时间编码器，使机器人能够执行原子技能并在它们之间进行转换，例如运球、停球和射门。在仿真和真实Noetix E1人形机器人上的实验证明了鲁棒的多技能执行、长时程技能组合以及成功的仿真到现实部署。
英文摘要
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.
CommentsAccepted to CoRL 2026. Project page: https://yzc0731.github.io/SkillX/
