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A Bio-Inspired Mechanism for Learning Robot Motion From Mirrored Human Demonstrations

Different learning modes and mechanisms allow faster and better acquisition of skills as widely studied in humans and many animals. Specific neurons, called mirror neurons, are activated in the same way whether an action is performed or simply observed. This suggests that observing others performing...

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Autores principales: Zahra, Omar, Tolu, Silvia, Zhou, Peng, Duan, Anqing, Navarro-Alarcon, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963868/
https://www.ncbi.nlm.nih.gov/pubmed/35360830
http://dx.doi.org/10.3389/fnbot.2022.826410
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author Zahra, Omar
Tolu, Silvia
Zhou, Peng
Duan, Anqing
Navarro-Alarcon, David
author_facet Zahra, Omar
Tolu, Silvia
Zhou, Peng
Duan, Anqing
Navarro-Alarcon, David
author_sort Zahra, Omar
collection PubMed
description Different learning modes and mechanisms allow faster and better acquisition of skills as widely studied in humans and many animals. Specific neurons, called mirror neurons, are activated in the same way whether an action is performed or simply observed. This suggests that observing others performing movements allows to reinforce our motor abilities. This implies the presence of a biological mechanism that allows creating models of others' movements and linking them to the self-model for achieving mirroring. Inspired by such ability, we propose to build a map of movements executed by a teaching agent and mirror the agent's state to the robot's configuration space. Hence, in this study, a neural network is proposed to integrate a motor cortex-like differential map transforming motor plans from task-space to joint-space motor commands and a static map correlating joint-spaces of the robot and a teaching agent. The differential map is developed based on spiking neural networks while the static map is built as a self-organizing map. The developed neural network allows the robot to mirror the actions performed by a human teaching agent to its own joint-space and the reaching skill is refined by the complementary examples provided. Hence, experiments are conducted to quantify the improvement achieved thanks to the proposed learning approach and control scheme.
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spelling pubmed-89638682022-03-30 A Bio-Inspired Mechanism for Learning Robot Motion From Mirrored Human Demonstrations Zahra, Omar Tolu, Silvia Zhou, Peng Duan, Anqing Navarro-Alarcon, David Front Neurorobot Neuroscience Different learning modes and mechanisms allow faster and better acquisition of skills as widely studied in humans and many animals. Specific neurons, called mirror neurons, are activated in the same way whether an action is performed or simply observed. This suggests that observing others performing movements allows to reinforce our motor abilities. This implies the presence of a biological mechanism that allows creating models of others' movements and linking them to the self-model for achieving mirroring. Inspired by such ability, we propose to build a map of movements executed by a teaching agent and mirror the agent's state to the robot's configuration space. Hence, in this study, a neural network is proposed to integrate a motor cortex-like differential map transforming motor plans from task-space to joint-space motor commands and a static map correlating joint-spaces of the robot and a teaching agent. The differential map is developed based on spiking neural networks while the static map is built as a self-organizing map. The developed neural network allows the robot to mirror the actions performed by a human teaching agent to its own joint-space and the reaching skill is refined by the complementary examples provided. Hence, experiments are conducted to quantify the improvement achieved thanks to the proposed learning approach and control scheme. Frontiers Media S.A. 2022-03-14 /pmc/articles/PMC8963868/ /pubmed/35360830 http://dx.doi.org/10.3389/fnbot.2022.826410 Text en Copyright © 2022 Zahra, Tolu, Zhou, Duan and Navarro-Alarcon. https://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
Zahra, Omar
Tolu, Silvia
Zhou, Peng
Duan, Anqing
Navarro-Alarcon, David
A Bio-Inspired Mechanism for Learning Robot Motion From Mirrored Human Demonstrations
title A Bio-Inspired Mechanism for Learning Robot Motion From Mirrored Human Demonstrations
title_full A Bio-Inspired Mechanism for Learning Robot Motion From Mirrored Human Demonstrations
title_fullStr A Bio-Inspired Mechanism for Learning Robot Motion From Mirrored Human Demonstrations
title_full_unstemmed A Bio-Inspired Mechanism for Learning Robot Motion From Mirrored Human Demonstrations
title_short A Bio-Inspired Mechanism for Learning Robot Motion From Mirrored Human Demonstrations
title_sort bio-inspired mechanism for learning robot motion from mirrored human demonstrations
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963868/
https://www.ncbi.nlm.nih.gov/pubmed/35360830
http://dx.doi.org/10.3389/fnbot.2022.826410
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