Abstract
Musculo-skeletal humanoid has many DOFs and flexible structure, so it can adapt to environment using hardware. To realize adaptation to environment, it is important for musculo-skeletal humanoid to modify its actions by using its own sensors because it is difficult to model for its complex structure. In this paper, by vision-based reinforcement learning, musculo-skeletal humanoid Kotaro aquires crank rotation behavior with flexible spine.
