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arXiv · 2609.06718

SkillX: Unified Multi-Skill Policy Learning for Humanoid Soccer

Zhangchen Ye·Enxuan Ruan·Yifei Bao·Runhan Huang·Jiankun Yang·Jiakang Jin·Yixiao Huo·Pengyuan Wang·Yinan Han·Huaxing Huang·Wenhao Cui·Yiming Li·Xiaoyu Tian
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Abstract

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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Zhangchen Ye, Enxuan Ruan, Yifei Bao, Runhan Huang, Jiankun Yang, Jiakang Jin, Yixiao Huo, Pengyuan Wang, Yinan Han, Huaxing Huang, Wenhao Cui, Yiming Li, Xiaoyu Tian. 2026-09-06. SkillX: Unified Multi-Skill Policy Learning for Humanoid Soccer. https://arxiv.org/abs/2609.06718

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