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Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots

Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions...

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Autores principales: Dasgupta, Sakyasingha, Goldschmidt, Dennis, Wörgötter, Florentin, Manoonpong, Poramate
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4585172/
https://www.ncbi.nlm.nih.gov/pubmed/26441629
http://dx.doi.org/10.3389/fnbot.2015.00010
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author Dasgupta, Sakyasingha
Goldschmidt, Dennis
Wörgötter, Florentin
Manoonpong, Poramate
author_facet Dasgupta, Sakyasingha
Goldschmidt, Dennis
Wörgötter, Florentin
Manoonpong, Poramate
author_sort Dasgupta, Sakyasingha
collection PubMed
description Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots.
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spelling pubmed-45851722015-10-05 Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots Dasgupta, Sakyasingha Goldschmidt, Dennis Wörgötter, Florentin Manoonpong, Poramate Front Neurorobot Neuroscience Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots. Frontiers Media S.A. 2015-09-25 /pmc/articles/PMC4585172/ /pubmed/26441629 http://dx.doi.org/10.3389/fnbot.2015.00010 Text en Copyright © 2015 Dasgupta, Goldschmidt, Wörgötter and Manoonpong. 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
Dasgupta, Sakyasingha
Goldschmidt, Dennis
Wörgötter, Florentin
Manoonpong, Poramate
Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots
title Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots
title_full Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots
title_fullStr Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots
title_full_unstemmed Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots
title_short Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots
title_sort distributed recurrent neural forward models with synaptic adaptation and cpg-based control for complex behaviors of walking robots
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4585172/
https://www.ncbi.nlm.nih.gov/pubmed/26441629
http://dx.doi.org/10.3389/fnbot.2015.00010
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