Long-Time Self-Body Image Acquisition and Its Application to the Control of Musculoskeletal Structures
Kento Kawaharazuka, Kei Tsuzuki, Shogo Makino, Moritaka Onitsuka, Yuki Asano, Kei Okada, Koji Kawasaki, Masayuki Inaba
Abstract
The tendon-driven musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex muscle and bone structures is difficult and conventional model-based controls cannot realize intended movements. Therefore, a learning control mechanism that acquires nonlinear relationships between joint angles, muscle tensions, and muscle lengths from the actual robot is necessary. In this study, we propose a system which runs the learning control mechanism for a long time to keep the self-body image of the musculoskeletal humanoid correct at all times. Also, we show that the musculoskeletal humanoid can conduct position control, torque control, and variable stiffness control using this self-body image. We conduct a long-time self-body image acquisition experiment lasting 3 h, evaluate variable stiffness control using the self-body image, etc., and discuss the superiority and practicality of the self-body image acquisition of musculoskeletal structures, comprehensively.
BibTeX
@inproceedings{ral2019_longtimeselfbody,
title = {Long-Time Self-Body Image Acquisition and Its Application to the Control of Musculoskeletal Structures},
author = {Kento Kawaharazuka and Kei Tsuzuki and Shogo Makino and Moritaka Onitsuka and Yuki Asano and Kei Okada and Koji Kawasaki and Masayuki Inaba},
booktitle = {RA-L 2019},
year = {2019}
}