  While the musculoskeletal humanoid has various biomimetic benefits, its
complex modeling is difficult, and many learning control methods have been
developed. However, for the actual robot, the hysteresis of its joint angle
tracking is still an obstacle, and realizing target posture quickly and
accurately has been difficult. Therefore, we develop a feedback control method
considering the hysteresis. To solve the problem in feedback controls caused by
the closed-link structure of the musculoskeletal body, we update a neural
network representing the relationship between the error of joint angles and the
change in target muscle lengths online, and realize target joint angles
accurately in a few trials. We compare the performance of several
configurations with various network structures and loss definitions, and verify
the effectiveness of this study on an actual musculoskeletal humanoid, Musashi.
