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Morphological properties of mass-spring networks for optimal locomotion learning

Gabriel Urbain UGent, Jonas Degrave UGent, Joni Dambre UGent and Francis wyffels UGent (2017) FRONTIERS IN NEUROROBOTICS . 11. p.1-13
abstract
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
morphological computation, mass–spring networks, morphological control, physical reservoir computing, soft robotics
journal title
FRONTIERS IN NEUROROBOTICS
volume
11
pages
1 - 13
publisher
Frontiers Media SA
Web of Science type
Article
Web of Science id
000397435100001
ISSN
1662-5218
DOI
10.3389/fnbot.2017.00016
language
English
UGent publication?
yes
classification
A1
copyright statement
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
id
8518242
handle
http://hdl.handle.net/1854/LU-8518242
date created
2017-04-20 14:08:42
date last changed
2017-05-04 10:55:38
@article{8518242,
  abstract     = {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{\textquoteright}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.},
  author       = {Urbain, Gabriel and Degrave, Jonas and Dambre, Joni and wyffels, Francis},
  issn         = {1662-5218},
  journal      = {FRONTIERS IN NEUROROBOTICS },
  keyword      = {morphological computation,mass--spring networks,morphological control,physical reservoir computing,soft robotics},
  language     = {eng},
  pages        = {1--13},
  publisher    = {Frontiers Media SA},
  title        = {Morphological properties of mass-spring networks for optimal locomotion learning},
  url          = {http://dx.doi.org/10.3389/fnbot.2017.00016},
  volume       = {11},
  year         = {2017},
}

Chicago
Urbain, Gabriel, Jonas Degrave, Joni Dambre, and Francis wyffels. 2017. “Morphological Properties of Mass-spring Networks for Optimal Locomotion Learning.” Frontiers in Neurorobotics 11: 1–13.
APA
Urbain, G., Degrave, J., Dambre, J., & wyffels, F. (2017). Morphological properties of mass-spring networks for optimal locomotion learning. FRONTIERS IN NEUROROBOTICS , 11, 1–13.
Vancouver
1.
Urbain G, Degrave J, Dambre J, wyffels F. Morphological properties of mass-spring networks for optimal locomotion learning. FRONTIERS IN NEUROROBOTICS . Frontiers Media SA; 2017;11:1–13.
MLA
Urbain, Gabriel, Jonas Degrave, Joni Dambre, et al. “Morphological Properties of Mass-spring Networks for Optimal Locomotion Learning.” FRONTIERS IN NEUROROBOTICS 11 (2017): 1–13. Print.