Task-specific Self-body Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving
Kento Kawaharazuka, Kei Tsuzuki, Shogo Makino, Moritaka Onitsuka, Koki Shinjo, Yuki Asano, Kei Okada, Koji Kawasaki
Abstract
The musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex flexible body is difficult. Although we have developed an online acquisition method of the nonlinear relationship between joints and muscles, we could not completely match the actual robot and its self-body image. When realizing a certain task, the direct relationship between the control input and task state needs to be learned. So, we construct a neural network representing the time-series relationship between the control input and task state, and realize the intended task state by applying the network to a real-time control. In this research, we conduct accelerator pedal control experiments as one application, and verify the effectiveness of this study.
BibTeX
@inproceedings{iros2019_taskspecificself,
title = {Task-specific Self-body Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving},
author = {Kento Kawaharazuka and Kei Tsuzuki and Shogo Makino and Moritaka Onitsuka and Koki Shinjo and Yuki Asano and Kei Okada and Koji Kawasaki and Masayuki Inaba},
booktitle = {IROS 2019},
year = {2019}
}