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G1 Dribble: End-to-End Reinforcement Learning for Humanoid Robot Soccer Dribbling Control

G1 Dribble uses end-to-end reinforcement learning to achieve humanoid robot soccer dribbling; the walking task's weights are not adjusted, and velocity commands drive both the body and the ball. The author tested it locally in MuJoCo-Warp and recorded eighteen cloud sessions, reporting touch rhythm, the distance band for re-touching the ball, and failure modes, including that a centered start steps over the ball without touching it, a first touch occurs only when offset to the side, and at 1.5 meters per second it knocks the ball away directly.

Part of the event: G1 Dribble End-to-End Reinforcement Learning Dribbling Control Real-World Test

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