Learning Vision-Driven Reactive Soccer Skills for Humanoid Robots

2025-11-06

AI Summary

Humanoid robot soccer suffers from response latency and behavioral discontinuity due to perception–action decoupling—especially under realistic, visually constrained conditions. To address this, we propose a vision-driven end-to-end reinforcement learning framework that unifies perception and control within a single neural controller. Our architecture employs an encoder–decoder structure to jointly model visual observations and motion priors, and introduces a virtual perception system that reconstructs privileged states from imperfect real-world observations, thereby enabling active perception–action coordination. Crucially, this work is the first to extend adversarial motion priors to closed-loop control under real-world perception constraints. Experiments demonstrate that the controller achieves high reactivity, strong robustness, and seamless motion continuity in both simulation and the physical RoboCup platform, significantly improving skill execution efficiency in dynamic, adversarial scenarios.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Humanoid soccer poses a representative challenge for embodied intelligence, requiring robots to operate within a tightly coupled perception-action loop. However, existing systems typically rely on decoupled modules, resulting in delayed responses and incoherent behaviors in dynamic environments, while real-world perceptual limitations further exacerbate these issues. In this work, we present a unified reinforcement learning-based controller that enables humanoid robots to acquire reactive soccer skills through the direct integration of visual perception and motion control. Our approach extends Adversarial Motion Priors to perceptual settings in real-world dynamic environments, bridging motion imitation and visually grounded dynamic control. We introduce an encoder-decoder architecture combined with a virtual perception system that models real-world visual characteristics, allowing the policy to recover privileged states from imperfect observations and establish active coordination between perception and action. The resulting controller demonstrates strong reactivity, consistently executing coherent and robust soccer behaviors across various scenarios, including real RoboCup matches.

Problem

• Developing reactive soccer skills for humanoid robots
• Integrating visual perception with motion control directly
• Overcoming delayed responses in dynamic soccer environments

Innovation

• Unified reinforcement learning controller integrates vision and motion
• Encoder-decoder architecture recovers states from imperfect visual observations
• Extends Adversarial Motion Priors to real-world perceptual settings