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Self-Learning Event Mistiming Detector Based on Central Pattern Generator

A repetitive movement pattern of many animals, a gait, is controlled by the Central Pattern Generator (CPG), providing rhythmic control synchronous to the sensed environment. As a rhythmic signal generator, the CPG can control the motion phase of biomimetic legged robots without feedback. The CPG ca...

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Autores principales: Szadkowski, Rudolf, Prágr, Miloš, Faigl, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890245/
https://www.ncbi.nlm.nih.gov/pubmed/33613224
http://dx.doi.org/10.3389/fnbot.2021.629652
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author Szadkowski, Rudolf
Prágr, Miloš
Faigl, Jan
author_facet Szadkowski, Rudolf
Prágr, Miloš
Faigl, Jan
author_sort Szadkowski, Rudolf
collection PubMed
description A repetitive movement pattern of many animals, a gait, is controlled by the Central Pattern Generator (CPG), providing rhythmic control synchronous to the sensed environment. As a rhythmic signal generator, the CPG can control the motion phase of biomimetic legged robots without feedback. The CPG can also act in sensory synchronization, where it can be utilized as a sensory phase estimator. Direct use of the CPG as the estimator is not common, and there is little research done on its utilization in the phase estimation. Generally, the sensory estimation augments the sensory feedback information, and motion irregularities can reveal from comparing measurements with the estimation. In this work, we study the CPG in the context of phase irregularity detection, where the timing of sensory events is disturbed. We propose a novel self-supervised method for learning mistiming detection, where the neural detector is trained by dynamic Hebbian-like rules during the robot walking. The proposed detector is composed of three neural components: (i) the CPG providing phase estimation, (ii) Radial Basis Function neuron anticipating the sensory event, and (iii) Leaky Integrate-and-Fire neuron detecting the sensory mistiming. The detector is integrated with the CPG-based gait controller. The mistiming detection triggers two reflexes: the elevator reflex, which avoids an obstacle, and the search reflex, which grasps a missing foothold. The proposed controller is deployed and trained on a hexapod walking robot to demonstrate the mistiming detection in real locomotion. The trained system has been examined in the controlled laboratory experiment and real field deployment in the Bull Rock cave system, where the robot utilized mistiming detection to negotiate the unstructured and slippery subterranean environment.
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spelling pubmed-78902452021-02-19 Self-Learning Event Mistiming Detector Based on Central Pattern Generator Szadkowski, Rudolf Prágr, Miloš Faigl, Jan Front Neurorobot Neuroscience A repetitive movement pattern of many animals, a gait, is controlled by the Central Pattern Generator (CPG), providing rhythmic control synchronous to the sensed environment. As a rhythmic signal generator, the CPG can control the motion phase of biomimetic legged robots without feedback. The CPG can also act in sensory synchronization, where it can be utilized as a sensory phase estimator. Direct use of the CPG as the estimator is not common, and there is little research done on its utilization in the phase estimation. Generally, the sensory estimation augments the sensory feedback information, and motion irregularities can reveal from comparing measurements with the estimation. In this work, we study the CPG in the context of phase irregularity detection, where the timing of sensory events is disturbed. We propose a novel self-supervised method for learning mistiming detection, where the neural detector is trained by dynamic Hebbian-like rules during the robot walking. The proposed detector is composed of three neural components: (i) the CPG providing phase estimation, (ii) Radial Basis Function neuron anticipating the sensory event, and (iii) Leaky Integrate-and-Fire neuron detecting the sensory mistiming. The detector is integrated with the CPG-based gait controller. The mistiming detection triggers two reflexes: the elevator reflex, which avoids an obstacle, and the search reflex, which grasps a missing foothold. The proposed controller is deployed and trained on a hexapod walking robot to demonstrate the mistiming detection in real locomotion. The trained system has been examined in the controlled laboratory experiment and real field deployment in the Bull Rock cave system, where the robot utilized mistiming detection to negotiate the unstructured and slippery subterranean environment. Frontiers Media S.A. 2021-02-04 /pmc/articles/PMC7890245/ /pubmed/33613224 http://dx.doi.org/10.3389/fnbot.2021.629652 Text en Copyright © 2021 Szadkowski, Prágr and Faigl. 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) and the copyright owner(s) 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
Szadkowski, Rudolf
Prágr, Miloš
Faigl, Jan
Self-Learning Event Mistiming Detector Based on Central Pattern Generator
title Self-Learning Event Mistiming Detector Based on Central Pattern Generator
title_full Self-Learning Event Mistiming Detector Based on Central Pattern Generator
title_fullStr Self-Learning Event Mistiming Detector Based on Central Pattern Generator
title_full_unstemmed Self-Learning Event Mistiming Detector Based on Central Pattern Generator
title_short Self-Learning Event Mistiming Detector Based on Central Pattern Generator
title_sort self-learning event mistiming detector based on central pattern generator
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890245/
https://www.ncbi.nlm.nih.gov/pubmed/33613224
http://dx.doi.org/10.3389/fnbot.2021.629652
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