筋骨格構造のヒステリシスを考慮したオンライン学習フィードバック制御
Online Learning Feedback Control Considering Hysteresis for Musculoskeletal Structures
筋骨格ヒューマノイドの関節角度追従に生じるヒステリシスを補償するため、関節角度誤差と目標筋長変化の関係を表すニューラルネットをオンライン更新し、少ない試行で正確な目標姿勢を実現するフィードバック制御法を開発した。
著者: Kento Kawaharazuka, Kei Okada, Masayuki Inaba
分類: cs.RO
原文アブストラクト
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.
