Learning agile soccer skills for a bipedal robot with deep reinforcement learning.
Sci Robot
; 9(89): eadi8022, 2024 04 10.
Article
em En
| MEDLINE
| ID: mdl-38598610
ABSTRACT
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.
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1
Base de dados:
MEDLINE
Assunto principal:
Futebol
/
Robótica
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article