SkillX: Unified Multi-Skill Policy Learning for Humanoid Soccer

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.

Accepted to CoRL 2026. Project page: this https URL

Robotics (cs.RO)

arXiv:2609.06718 [cs.RO]

(or arXiv:2609.06718v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.06718 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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