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Morphological Properties of Mass–Spring Networks for Optimal Locomotion Learning

Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the...

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Autores principales: Urbain, Gabriel, Degrave, Jonas, Carette, Benonie, Dambre, Joni, Wyffels, Francis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5366341/
https://www.ncbi.nlm.nih.gov/pubmed/28396634
http://dx.doi.org/10.3389/fnbot.2017.00016
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author Urbain, Gabriel
Degrave, Jonas
Carette, Benonie
Dambre, Joni
Wyffels, Francis
author_facet Urbain, Gabriel
Degrave, Jonas
Carette, Benonie
Dambre, Joni
Wyffels, Francis
author_sort Urbain, Gabriel
collection PubMed
description Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength. On the one hand, the use of flexible materials can lead to higher power efficiency and more fluent and robust motions. On the other hand, using embodiment in a closed-loop controller, part of the control task itself can be outsourced to the body dynamics. This can significantly simplify the additional resources required for locomotion control. To this goal, a first step consists in an exploration of the trade-offs between morphology, efficiency of locomotion, and the ability of a mechanical body to serve as a computational resource. In this work, we use a detailed dynamical model of a Mass–Spring–Damper (MSD) network to study these trade-offs. We first investigate the influence of the network size and compliance on locomotion quality and energy efficiency by optimizing an external open-loop controller using evolutionary algorithms. We find that larger networks can lead to more stable gaits and that the system’s optimal compliance to maximize the traveled distance is directly linked to the desired frequency of locomotion. In the last set of experiments, the suitability of MSD bodies for being used in a closed loop is also investigated. Since maximally efficient actuator signals are clearly related to the natural body dynamics, in a sense, the body is tailored for the task of contributing to its own control. Using the same simulation platform, we therefore study how the network states can be successfully used to create a feedback signal and how its accuracy is linked to the body size.
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spelling pubmed-53663412017-04-10 Morphological Properties of Mass–Spring Networks for Optimal Locomotion Learning Urbain, Gabriel Degrave, Jonas Carette, Benonie Dambre, Joni Wyffels, Francis Front Neurorobot Neuroscience Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength. On the one hand, the use of flexible materials can lead to higher power efficiency and more fluent and robust motions. On the other hand, using embodiment in a closed-loop controller, part of the control task itself can be outsourced to the body dynamics. This can significantly simplify the additional resources required for locomotion control. To this goal, a first step consists in an exploration of the trade-offs between morphology, efficiency of locomotion, and the ability of a mechanical body to serve as a computational resource. In this work, we use a detailed dynamical model of a Mass–Spring–Damper (MSD) network to study these trade-offs. We first investigate the influence of the network size and compliance on locomotion quality and energy efficiency by optimizing an external open-loop controller using evolutionary algorithms. We find that larger networks can lead to more stable gaits and that the system’s optimal compliance to maximize the traveled distance is directly linked to the desired frequency of locomotion. In the last set of experiments, the suitability of MSD bodies for being used in a closed loop is also investigated. Since maximally efficient actuator signals are clearly related to the natural body dynamics, in a sense, the body is tailored for the task of contributing to its own control. Using the same simulation platform, we therefore study how the network states can be successfully used to create a feedback signal and how its accuracy is linked to the body size. Frontiers Media S.A. 2017-03-27 /pmc/articles/PMC5366341/ /pubmed/28396634 http://dx.doi.org/10.3389/fnbot.2017.00016 Text en Copyright © 2017 Urbain, Degrave, Carette, Dambre and Wyffels. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Urbain, Gabriel
Degrave, Jonas
Carette, Benonie
Dambre, Joni
Wyffels, Francis
Morphological Properties of Mass–Spring Networks for Optimal Locomotion Learning
title Morphological Properties of Mass–Spring Networks for Optimal Locomotion Learning
title_full Morphological Properties of Mass–Spring Networks for Optimal Locomotion Learning
title_fullStr Morphological Properties of Mass–Spring Networks for Optimal Locomotion Learning
title_full_unstemmed Morphological Properties of Mass–Spring Networks for Optimal Locomotion Learning
title_short Morphological Properties of Mass–Spring Networks for Optimal Locomotion Learning
title_sort morphological properties of mass–spring networks for optimal locomotion learning
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5366341/
https://www.ncbi.nlm.nih.gov/pubmed/28396634
http://dx.doi.org/10.3389/fnbot.2017.00016
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