NEW RESEARCH: You can play soccer with your Unitree G1 Humanoid! This project involves @PeterYChong, @YuhangLu2000, @sephy_li, and others. It is a collaboration between OpenDriveLab, the University of Hong Kong, the Chinese University of Hong Kong and Archon Robotics. Called RoboNaldo, it is a reinforcement-learning policy that makes a Unitree G1 humanoid (29 DoF, 35 kg) shoot a soccer ball, both stationary free-kicks and one-touch shots on a moving ball. It is trained by a three-stage motion-guided curriculum in Isaac Lab (4096 parallel environments): - Stage 1 imitates a single human-kick reference (retargeted from a human video via GVHMR and GMR) with no ball or task reward - Stage 2 adds the ball, a target and shooting rewards while randomizing the ball spawn - Stage 3 adds a high-level locomotion command and a kick trigger so it can walk up and volley Perception is onboard and egocentric: a head-mounted Livox MID-360 LiDAR and a chest RealSense D435 track a retro-reflective ball (Kalman-fused), and an AprilTag (or a fixed coordinate) marks the target, all at 50 Hz with no motion capture. What is interesting imho: if you remove Stage 1 (imitating one retargeted human-kick video), the alive rate collapses to 1.2 percent, it topples on nearly every attempt. The human prior's real job is whole-body stability through a violent, high-impulse motion, not style; the hard part of a robot kick is not aiming but not tipping over from your own swing The onboard, no motion capture perception still needs a retro-reflective ball and an AprilTag goal. Ball localization is a head Livox MID-360 LiDAR plus a chest RealSense IR camera tracking a retro-reflective ball, fused by a Kalman filter. The target is read from an AprilTag or hardcoded as a fixed coordinate. It shoots hard but sub-human and only roughly accurate. Peak ball velocity is 13.10 m/s, which it reports as 59 to 71 percent of a professional open-play shot (about two-thirds of a real Ronaldo, fitting the pun). Stationary accuracy is 0.73 m average error from 3 m, with 31.5 percent of shots inside 0.5 m and 80.6 percent inside 1.0 m. The entire motion prior is one human video. There is no kick dataset and no mocap suit: a single human-kick clip retargeted to the 29-DoF G1 via GVHMR and GMR, then curriculum RL across 4096 Isaac Lab environments deviates from it for aim, contact and timing. It is the same one-demonstration-plus-sim-compute recipe as LadderMan and the perceptive-parkour projects I talked about previously. Also fun fact: it is an autonomous-driving lab pivoting to humanoid soccer. RoboNaldo comes from OpenDriveLab (the HKU end-to-end self-driving / UniAD group) with CUHK and the startup Archon Robotics, all Hong Kong based.