Online Learning of Danger Avoidance for Complex Structures of Musculoskeletal Humanoids and Its Applications

TL;DR

This paper presents an online learning method for musculoskeletal humanoids that predicts danger probabilities to enhance safety and prevent accidents during operation.

Contribution

It introduces a novel online learning approach for danger prediction in complex humanoid structures, improving safety control mechanisms.

Findings

Effective danger probability prediction for Musashi humanoid

Enhanced safety control through online learning

Verified applicability on complex musculoskeletal robots

Abstract

The complex structure of musculoskeletal humanoids makes it difficult to model them, and the inter-body interference and high internal muscle force are unavoidable. Although various safety mechanisms have been developed to solve this problem, it is important not only to deal with the dangers when they occur but also to prevent them from happening. In this study, we propose a method to learn a network outputting danger probability corresponding to the muscle length online so that the robot can gradually prevent dangers from occurring. Applications of this network for control are also described. The method is applied to the musculoskeletal humanoid, Musashi, and its effectiveness is verified.