Robust Continuous Motion Strategy Against Muscle Rupture using Online Learning of Redundant Intersensory Networks for Musculoskeletal Humanoids

TL;DR

This paper presents a neural network-based method for musculoskeletal humanoids that detects muscle rupture, updates intersensory relationships online, and maintains robust movement despite muscle failure.

Contribution

It introduces a novel neural network approach for real-time muscle rupture detection and adaptive control in musculoskeletal humanoids, enhancing robustness and functionality.

Findings

Neural network accurately models sensor relationships in humanoids.

The system detects muscle rupture in real-time.

Humanoids maintain movement despite muscle failure.

Abstract

Musculoskeletal humanoids have various biomimetic advantages, of which redundant muscle arrangement is one of the most important features. This feature enables variable stiffness control and allows the robot to keep moving its joints even if one of the redundant muscles breaks, but this has been rarely explored. In this study, we construct a neural network that represents the relationship among sensors in the flexible and difficult-to-modelize body of the musculoskeletal humanoid, and by learning this neural network, accurate motions can be achieved. In order to take advantage of the redundancy of muscles, we discuss the use of this network for muscle rupture detection, online update of the intersensory relationship considering the muscle rupture, and body control and state estimation using the muscle rupture information. This study explains a method of constructing a musculoskeletal humanoid that continues to move and perform tasks robustly even when one muscle breaks.