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Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning

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We investigated whether deep reinforcement learning (deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies. We used deep RL to train a humanoid robot to play a simplified one-versus-one soccer game. The resulting agent exhibits robust and dynamic movement skills, such as rapid fall recovery, walking, turning, and kicking, and it transitions between them in a smooth and efficient manner. It also learned to anticipate ball movements and block opponent shots. The agent’s tactical behavior adapts to specific game contexts in a way that would be impractical to manually design. Our agent was trained in simulation and transferred to real robots zero-shot. A combination of sufficiently high-frequency control, targeted dynamics randomization, and perturbations during training enabled good-quality transfer. In experiments, the agent walked 181% faster, turned 302% faster, took 63% less time to get up, and kicked a ball 34% faster than a scripted baseline.


Tuomas Haarnoja, Ben Moran, Guy Lever, Sandy Huang, Dhruva TB, Jan Humplik, Markus Wulfmeier, Saran Tunyasuvunakool, Noah Siegel, Roland Hafner, Michael Bloesch, Arunkumar Byravan, Leonard Hasenclever, Yuval Tassa, Nathan Batchelor, Federico Casarini, Stefano Saliceti, cgame , Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber, Nicole Hurley, Francesco Nori, Raia Hadsell, Nicolas Heess, Kristian Hartikainen*, Fereshteh Sadeghi*


Science Robotics