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The Neurorobotics Platform Robot Designer: Modeling Morphologies for Embodied Learning Experiments
The more we investigate the principles of motion learning in biological systems, the more we reveal the central role that body morphology plays in motion execution. Not only does anatomy define the kinematics and therefore the complexity of possible movements, but it now becomes clear that part of t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9083454/ https://www.ncbi.nlm.nih.gov/pubmed/35548779 http://dx.doi.org/10.3389/fnbot.2022.856727 |
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author | Feldotto, Benedikt Morin, Fabrice O. Knoll, Alois |
author_facet | Feldotto, Benedikt Morin, Fabrice O. Knoll, Alois |
author_sort | Feldotto, Benedikt |
collection | PubMed |
description | The more we investigate the principles of motion learning in biological systems, the more we reveal the central role that body morphology plays in motion execution. Not only does anatomy define the kinematics and therefore the complexity of possible movements, but it now becomes clear that part of the computation required for motion control is offloaded to body dynamics (a phenomenon referred to as “Morphological Computation.”) Consequentially, a proper design of body morphology is essential to carry out meaningful simulations on motor control of robotic and musculoskeletal systems. The design should not be fixed for simulation experiments beforehand, but is a central research aspect in every motion learning experiment that requires continuous adaptation during the experimental phase. We herein introduce a plugin for the 3D modeling suite Blender that enables researchers to design morphologies for simulation experiments in, particularly but not restricted to, the Neurorobotics Platform. We include design capabilities for both musculoskeletal bodies, as well as robotic systems in the Robot Designer. Thereby, we hope to not only foster understanding of biological motions and enabling better robot designs, but enabling true Neurorobotic experiments that may consist of biomimetic models such as tendon-driven robot as a mix of both or a transition between both biology and technology. This plugin helps researchers design and parameterize models with a Graphical User Interface and thus simplifies and speeds up the overall design process. |
format | Online Article Text |
id | pubmed-9083454 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90834542022-05-10 The Neurorobotics Platform Robot Designer: Modeling Morphologies for Embodied Learning Experiments Feldotto, Benedikt Morin, Fabrice O. Knoll, Alois Front Neurorobot Neuroscience The more we investigate the principles of motion learning in biological systems, the more we reveal the central role that body morphology plays in motion execution. Not only does anatomy define the kinematics and therefore the complexity of possible movements, but it now becomes clear that part of the computation required for motion control is offloaded to body dynamics (a phenomenon referred to as “Morphological Computation.”) Consequentially, a proper design of body morphology is essential to carry out meaningful simulations on motor control of robotic and musculoskeletal systems. The design should not be fixed for simulation experiments beforehand, but is a central research aspect in every motion learning experiment that requires continuous adaptation during the experimental phase. We herein introduce a plugin for the 3D modeling suite Blender that enables researchers to design morphologies for simulation experiments in, particularly but not restricted to, the Neurorobotics Platform. We include design capabilities for both musculoskeletal bodies, as well as robotic systems in the Robot Designer. Thereby, we hope to not only foster understanding of biological motions and enabling better robot designs, but enabling true Neurorobotic experiments that may consist of biomimetic models such as tendon-driven robot as a mix of both or a transition between both biology and technology. This plugin helps researchers design and parameterize models with a Graphical User Interface and thus simplifies and speeds up the overall design process. Frontiers Media S.A. 2022-04-25 /pmc/articles/PMC9083454/ /pubmed/35548779 http://dx.doi.org/10.3389/fnbot.2022.856727 Text en Copyright © 2022 Feldotto, Morin and Knoll. 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 Feldotto, Benedikt Morin, Fabrice O. Knoll, Alois The Neurorobotics Platform Robot Designer: Modeling Morphologies for Embodied Learning Experiments |
title | The Neurorobotics Platform Robot Designer: Modeling Morphologies for Embodied Learning Experiments |
title_full | The Neurorobotics Platform Robot Designer: Modeling Morphologies for Embodied Learning Experiments |
title_fullStr | The Neurorobotics Platform Robot Designer: Modeling Morphologies for Embodied Learning Experiments |
title_full_unstemmed | The Neurorobotics Platform Robot Designer: Modeling Morphologies for Embodied Learning Experiments |
title_short | The Neurorobotics Platform Robot Designer: Modeling Morphologies for Embodied Learning Experiments |
title_sort | neurorobotics platform robot designer: modeling morphologies for embodied learning experiments |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9083454/ https://www.ncbi.nlm.nih.gov/pubmed/35548779 http://dx.doi.org/10.3389/fnbot.2022.856727 |
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