RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning

RoboNaldo is a novel three-stage motion-guided curriculum reinforcement learning framework designed for humanoid soccer shooting, enhancing stability and accuracy. It employs a single human-kick reference to progressively optimize shooting performance, achieving a 48.6% reduction in free-kick shot error and a 2.96x increase in shoot velocity compared to existing baselines. In real-world tests on a Unitree G1 robot, RoboNaldo demonstrated an average target shooting error of 0.73 m and a post-contact ball velocity of 13.10 m/s, indicating significant advancements in robotic soccer performance that can inform practitioners in robotics and RL applications.