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Bootstrapping Virtual Bipedal Walkers with Robotics Scaffolded Learning

We reach walking optimality from a very early age by using natural supports, which can be the hands of our parents, chairs, and training wheels, and bootstrap a new knowledge from the recently acquired one. The idea behind bootstrapping is to use the previously acquired knowledge from simpler tasks...

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Autores principales: Zhu, Jiahui, Rong, Chunyan, Iida, Fumiya, Rosendo, Andre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8456033/
https://www.ncbi.nlm.nih.gov/pubmed/34568436
http://dx.doi.org/10.3389/frobt.2021.702599
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author Zhu, Jiahui
Rong, Chunyan
Iida, Fumiya
Rosendo, Andre
author_facet Zhu, Jiahui
Rong, Chunyan
Iida, Fumiya
Rosendo, Andre
author_sort Zhu, Jiahui
collection PubMed
description We reach walking optimality from a very early age by using natural supports, which can be the hands of our parents, chairs, and training wheels, and bootstrap a new knowledge from the recently acquired one. The idea behind bootstrapping is to use the previously acquired knowledge from simpler tasks to accelerate the learning of more complicated ones. In this paper, we propose a scaffolded learning method from an evolutionary perspective, where a biped creature achieves stable and independent bipedal walking while exploiting the natural scaffold of its changing morphology to create a third limb. The novelty of this work is speeding up the learning process with an artificially recreated scaffolded learning. We compare three conditions of scaffolded learning (free, time-constrained, and performance-based scaffolded learning) to reach bipedalism, and we prove that a performance-based scaffold, which is designed by the walking velocity obtained, is the most conducive to bootstrap the learning of bipedal walking. The scope of this work is not to study bipedal locomotion but to investigate the contribution from scaffolded learning to a faster learning process. Beyond a pedagogical experiment, this work presents a powerful tool to accelerate the learning of complex tasks in the Robotics field.
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spelling pubmed-84560332021-09-23 Bootstrapping Virtual Bipedal Walkers with Robotics Scaffolded Learning Zhu, Jiahui Rong, Chunyan Iida, Fumiya Rosendo, Andre Front Robot AI Robotics and AI We reach walking optimality from a very early age by using natural supports, which can be the hands of our parents, chairs, and training wheels, and bootstrap a new knowledge from the recently acquired one. The idea behind bootstrapping is to use the previously acquired knowledge from simpler tasks to accelerate the learning of more complicated ones. In this paper, we propose a scaffolded learning method from an evolutionary perspective, where a biped creature achieves stable and independent bipedal walking while exploiting the natural scaffold of its changing morphology to create a third limb. The novelty of this work is speeding up the learning process with an artificially recreated scaffolded learning. We compare three conditions of scaffolded learning (free, time-constrained, and performance-based scaffolded learning) to reach bipedalism, and we prove that a performance-based scaffold, which is designed by the walking velocity obtained, is the most conducive to bootstrap the learning of bipedal walking. The scope of this work is not to study bipedal locomotion but to investigate the contribution from scaffolded learning to a faster learning process. Beyond a pedagogical experiment, this work presents a powerful tool to accelerate the learning of complex tasks in the Robotics field. Frontiers Media S.A. 2021-09-08 /pmc/articles/PMC8456033/ /pubmed/34568436 http://dx.doi.org/10.3389/frobt.2021.702599 Text en Copyright © 2021 Zhu, Rong, Iida and Rosendo. 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 Robotics and AI
Zhu, Jiahui
Rong, Chunyan
Iida, Fumiya
Rosendo, Andre
Bootstrapping Virtual Bipedal Walkers with Robotics Scaffolded Learning
title Bootstrapping Virtual Bipedal Walkers with Robotics Scaffolded Learning
title_full Bootstrapping Virtual Bipedal Walkers with Robotics Scaffolded Learning
title_fullStr Bootstrapping Virtual Bipedal Walkers with Robotics Scaffolded Learning
title_full_unstemmed Bootstrapping Virtual Bipedal Walkers with Robotics Scaffolded Learning
title_short Bootstrapping Virtual Bipedal Walkers with Robotics Scaffolded Learning
title_sort bootstrapping virtual bipedal walkers with robotics scaffolded learning
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8456033/
https://www.ncbi.nlm.nih.gov/pubmed/34568436
http://dx.doi.org/10.3389/frobt.2021.702599
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