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Robot Programming from Fish Demonstrations
Fish are capable of learning complex relations found in their surroundings, and harnessing their knowledge may help to improve the autonomy and adaptability of robots. Here, we propose a novel learning from demonstration framework to generate fish-inspired robot control programs with as little human...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296172/ https://www.ncbi.nlm.nih.gov/pubmed/37366843 http://dx.doi.org/10.3390/biomimetics8020248 |
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author | Coppola, Claudio Massimo Strong, James Bradley O’Reilly, Lissa Dalesman, Sarah Akanyeti, Otar |
author_facet | Coppola, Claudio Massimo Strong, James Bradley O’Reilly, Lissa Dalesman, Sarah Akanyeti, Otar |
author_sort | Coppola, Claudio Massimo |
collection | PubMed |
description | Fish are capable of learning complex relations found in their surroundings, and harnessing their knowledge may help to improve the autonomy and adaptability of robots. Here, we propose a novel learning from demonstration framework to generate fish-inspired robot control programs with as little human intervention as possible. The framework consists of six core modules: (1) task demonstration, (2) fish tracking, (3) analysis of fish trajectories, (4) acquisition of robot training data, (5) generating a perception–action controller, and (6) performance evaluation. We first describe these modules and highlight the key challenges pertaining to each one. We then present an artificial neural network for automatic fish tracking. The network detected fish successfully in 85% of the frames, and in these frames, its average pose estimation error was less than 0.04 body lengths. We finally demonstrate how the framework works through a case study focusing on a cue-based navigation task. Two low-level perception–action controllers were generated through the framework. Their performance was measured using two-dimensional particle simulations and compared against two benchmark controllers, which were programmed manually by a researcher. The fish-inspired controllers had excellent performance when the robot was started from the initial conditions used in fish demonstrations (>96% success rate), outperforming the benchmark controllers by at least 3%. One of them also had an excellent generalisation performance when the robot was started from random initial conditions covering a wider range of starting positions and heading angles (>98% success rate), again outperforming the benchmark controllers by 12%. The positive results highlight the utility of the framework as a research tool to form biological hypotheses on how fish navigate in complex environments and design better robot controllers on the basis of biological findings. |
format | Online Article Text |
id | pubmed-10296172 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102961722023-06-28 Robot Programming from Fish Demonstrations Coppola, Claudio Massimo Strong, James Bradley O’Reilly, Lissa Dalesman, Sarah Akanyeti, Otar Biomimetics (Basel) Article Fish are capable of learning complex relations found in their surroundings, and harnessing their knowledge may help to improve the autonomy and adaptability of robots. Here, we propose a novel learning from demonstration framework to generate fish-inspired robot control programs with as little human intervention as possible. The framework consists of six core modules: (1) task demonstration, (2) fish tracking, (3) analysis of fish trajectories, (4) acquisition of robot training data, (5) generating a perception–action controller, and (6) performance evaluation. We first describe these modules and highlight the key challenges pertaining to each one. We then present an artificial neural network for automatic fish tracking. The network detected fish successfully in 85% of the frames, and in these frames, its average pose estimation error was less than 0.04 body lengths. We finally demonstrate how the framework works through a case study focusing on a cue-based navigation task. Two low-level perception–action controllers were generated through the framework. Their performance was measured using two-dimensional particle simulations and compared against two benchmark controllers, which were programmed manually by a researcher. The fish-inspired controllers had excellent performance when the robot was started from the initial conditions used in fish demonstrations (>96% success rate), outperforming the benchmark controllers by at least 3%. One of them also had an excellent generalisation performance when the robot was started from random initial conditions covering a wider range of starting positions and heading angles (>98% success rate), again outperforming the benchmark controllers by 12%. The positive results highlight the utility of the framework as a research tool to form biological hypotheses on how fish navigate in complex environments and design better robot controllers on the basis of biological findings. MDPI 2023-06-10 /pmc/articles/PMC10296172/ /pubmed/37366843 http://dx.doi.org/10.3390/biomimetics8020248 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Coppola, Claudio Massimo Strong, James Bradley O’Reilly, Lissa Dalesman, Sarah Akanyeti, Otar Robot Programming from Fish Demonstrations |
title | Robot Programming from Fish Demonstrations |
title_full | Robot Programming from Fish Demonstrations |
title_fullStr | Robot Programming from Fish Demonstrations |
title_full_unstemmed | Robot Programming from Fish Demonstrations |
title_short | Robot Programming from Fish Demonstrations |
title_sort | robot programming from fish demonstrations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296172/ https://www.ncbi.nlm.nih.gov/pubmed/37366843 http://dx.doi.org/10.3390/biomimetics8020248 |
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