Task-specific Self-body Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving

TL;DR

This paper presents a neural network-based method for task-specific control of a musculoskeletal humanoid, demonstrated through accelerator pedal control in autonomous driving, addressing the challenge of modeling complex flexible bodies.

Contribution

It introduces a neural network approach to learn the relationship between control inputs and task states for musculoskeletal humanoids, enabling effective real-time control.

Findings

Successful application to accelerator pedal control

Effective real-time control of musculoskeletal humanoid

Verification of neural network model's effectiveness

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.