A three-stage motion-guided curriculum RL framework trains humanoid robots for soccer shooting, achieving 48.6% lower shot error and 2.96x higher velocity than baselines in simulation and sub-meter accuracy with 13.10 m/s ball speed on a real Unitree G1.
Toward real-world cooperative and competitive soccer with quadrupedal robot teams
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SigLoMa enables dynamic loco-manipulation on quadrupeds from ego-centric 5 Hz vision alone by using Sigma Points for scalable exteroception, an ego-centric Kalman Filter for high-rate state estimation, and an active sampling curriculum, matching expert human teleoperation performance.
A four-stage RL system with teacher-student distillation and online constrained adaptation enables humanoid robots to achieve robust ball-kicking accuracy under noisy perception in simulation and on physical hardware.
citing papers explorer
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RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning
A three-stage motion-guided curriculum RL framework trains humanoid robots for soccer shooting, achieving 48.6% lower shot error and 2.96x higher velocity than baselines in simulation and sub-meter accuracy with 13.10 m/s ball speed on a real Unitree G1.
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SigLoMa: Learning Open-World Quadrupedal Loco-Manipulation from Ego-Centric Vision
SigLoMa enables dynamic loco-manipulation on quadrupeds from ego-centric 5 Hz vision alone by using Sigma Points for scalable exteroception, an ego-centric Kalman Filter for high-rate state estimation, and an active sampling curriculum, matching expert human teleoperation performance.
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Learning Agile Striker Skills for Humanoid Soccer Robots from Noisy Sensory Input
A four-stage RL system with teacher-student distillation and online constrained adaptation enables humanoid robots to achieve robust ball-kicking accuracy under noisy perception in simulation and on physical hardware.