Learning Vision-Driven Reactive Soccer Skills for Humanoid Robots
Authors: name: Yushi Wang, name: Changsheng Luo, name: Penghui Chen, name: Jianran Liu, name: Weijian Sun, name: Tong Guo, name: Kechang Yang, name: Biao Hu, name: Yangang Zhang, name: Mingguo Zhao
Organization: ByteDance Seed

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.