Component Modularized Design of Musculoskeletal Humanoid Platform Musashi to Investigate Learning Control Systems To develop Musashi as a musculoskeletal humanoid platform to investigate learning control systems, we aimed for a body with flexible musculoskeletal structure, redundant sensors, and easily reconfigurable structure. For this purpose, we develop joint modules that can directly measure joint angles, muscle modules that can realize various muscle routes, and nonlinear elastic units with soft structures, etc. Next, we develop MusashiLarm, a musculoskeletal platform composed of only joint modules, muscle modules, generic bone frames, muscle wire units, and a few attachments. Finally, we develop Musashi, a musculoskeletal humanoid platform which extends MusashiLarm to the whole body design, and conduct several basic experiments and learning control experiments to verify the effectiveness of its concept.

Taskspecific Selfbody Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving The musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex flexible body is difficult. Although we have developed an online acquisition method of the nonlinear relationship between joints and muscles, we could not completely match the actual robot and its selfbody image. When realizing a certain task, the direct relationship between the control input and task state needs to be learned. So, we construct a neural network representing the timeseries relationship between the control input and task state, and realize the intended task state by applying the network to a realtime control. In this research, we conduct accelerator pedal control experiments as one application, and verify the effectiveness of this study.