Extracting motor synergies from random movements for low-dimensional task-space control of musculoskeletal robots.

Abstract:

:In the field of human motor control, the motor synergy hypothesis explains how humans simplify body control dimensionality by coordinating groups of muscles, called motor synergies, instead of controlling muscles independently. In most applications of motor synergies to low-dimensional control in robotics, motor synergies are extracted from given optimal control signals. In this paper, we address the problems of how to extract motor synergies without optimal data given, and how to apply motor synergies to achieve low-dimensional task-space tracking control of a human-like robotic arm actuated by redundant muscles, without prior knowledge of the robot. We propose to extract motor synergies from a subset of randomly generated reaching-like movement data. The essence is to first approximate the corresponding optimal control signals, using estimations of the robot's forward dynamics, and to extract the motor synergies subsequently. In order to avoid modeling difficulties, a learning-based control approach is adopted such that control is accomplished via estimations of the robot's inverse dynamics. We present a kernel-based regression formulation to estimate the forward and the inverse dynamics, and a sliding controller in order to cope with estimation error. Numerical evaluations show that the proposed method enables extraction of motor synergies for low-dimensional task-space control.

journal_name

Bioinspir Biomim

authors

Fu KC,Dalla Libera F,Ishiguro H

doi

10.1088/1748-3190/10/5/056016

subject

Has Abstract

pub_date

2015-10-08 00:00:00

pages

056016

issue

5

eissn

1748-3182

issn

1748-3190

journal_volume

10

pub_type

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