Online Learning Feedback Control Considering Hysteresis for Musculoskeletal Structures

TL;DR

This paper presents an online learning feedback control method that accounts for hysteresis in musculoskeletal humanoids, enabling quick and accurate joint angle tracking through neural network updates.

Contribution

It introduces an online neural network-based feedback control approach that effectively manages hysteresis in musculoskeletal robots, improving posture accuracy.

Findings

Achieved accurate joint angle control in few trials.

Verified effectiveness on the Musashi humanoid.

Compared various neural network configurations.

Abstract

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.