Learning Vision-Driven Reactive Soccer Skills for Humanoid Robots
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.
690d62f9ad2597bf6c464d7e
Yushi Wang
690d62f9ad2597bf6c464d7f
Changsheng Luo
690d62f9ad2597bf6c464d80
Penghui Chen
690d62f9ad2597bf6c464d81
Jianran Liu
690d62f9ad2597bf6c464d82
Weijian Sun
690d62f9ad2597bf6c464d83
Tong Guo
690d62f9ad2597bf6c464d84
Kechang Yang
690d62f9ad2597bf6c464d85
Biao Hu
690d62f9ad2597bf6c464d86
Yangang Zhang
690d62f9ad2597bf6c464d87
Mingguo Zhao
Learning Vision-Driven Reactive Soccer Skills for Humanoid Robots
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.
A unified reinforcement learning controller integrates visual perception and motion control for humanoid robots in soccer, using Adversarial Motion Priors and an encoder-decoder architecture to achieve reactive and coherent behaviors.
reinforcement learning Adversarial Motion Priors encoder-decoder architecture virtual perception system privileged states real-world visual characteristics motion imitation visually grounded dynamic control