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.

翻译：尽管肌肉骨骼人形机器人具有多种仿生优势，但其复杂建模困难，已有许多学习控制方法被提出。然而，对于实际机器人而言，其关节角度跟踪的迟滞问题仍是障碍，难以快速且准确地实现目标姿态。为此，我们开发了一种考虑迟滞的反馈控制方法。为解决肌肉骨骼躯体的闭合连杆结构导致反馈控制中的问题，我们在线更新一个表征关节角度误差与目标肌肉长度变化之间关系的神经网络，从而在少数尝试中准确实现目标关节角度。我们比较了多种网络结构与损失定义配置的性能，并在实际肌肉骨骼人形机器人Musashi上验证了本研究的有效性。