SkillX: Unified Multi-Skill Policy Learning for Humanoid Soccer

2026-09-06

AI Summary

为解决人形机器人足球中多技能协同问题，提出SkillX框架，通过单一命令条件策略学习和组合多种基础足球技能。

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.

Problem

• humanoid soccer
• multi-skill policy learning
• dynamic whole-body control
• skill composition

Innovation

• Unified Reinforcement Learning
• Command-Conditioned Policy
• Adversarial Motion Priors
• Object-Aware Temporal Encoder