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

arXiv:2304.13653v2 Announce Type: replace-cross
Abstract: We investigate 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 in dynamic environments. We used Deep RL to train a humanoid robot with 20 actuated joints to play a simplified one-versus-one (1v1) soccer game. The resulting agent exhibits robust and dynamic movement skills such as rapid fall recovery, walking, turning, kicking and more; and …

abstract agile arxiv bipedal bipedal robot cost cs.ai cs.lg cs.ro deep rl dynamic environments humanoid humanoid robot low reinforcement reinforcement learning robot safe skills soccer strategies train type