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Practical hardware for evolvable robots
The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actual...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475714/ https://www.ncbi.nlm.nih.gov/pubmed/37670906 http://dx.doi.org/10.3389/frobt.2023.1206055 |
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author | Angus, Mike Buchanan, Edgar Le Goff, Léni K. Hart, Emma Eiben, Agoston E. De Carlo, Matteo Winfield, Alan F. Hale, Matthew F. Woolley, Robert Timmis, Jon Tyrrell, Andy M. |
author_facet | Angus, Mike Buchanan, Edgar Le Goff, Léni K. Hart, Emma Eiben, Agoston E. De Carlo, Matteo Winfield, Alan F. Hale, Matthew F. Woolley, Robert Timmis, Jon Tyrrell, Andy M. |
author_sort | Angus, Mike |
collection | PubMed |
description | The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space. |
format | Online Article Text |
id | pubmed-10475714 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104757142023-09-05 Practical hardware for evolvable robots Angus, Mike Buchanan, Edgar Le Goff, Léni K. Hart, Emma Eiben, Agoston E. De Carlo, Matteo Winfield, Alan F. Hale, Matthew F. Woolley, Robert Timmis, Jon Tyrrell, Andy M. Front Robot AI Robotics and AI The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space. Frontiers Media S.A. 2023-08-21 /pmc/articles/PMC10475714/ /pubmed/37670906 http://dx.doi.org/10.3389/frobt.2023.1206055 Text en Copyright © 2023 Angus, Buchanan, Le Goff, Hart, Eiben, De Carlo, Winfield, Hale, Woolley, Timmis and Tyrrell. 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 Angus, Mike Buchanan, Edgar Le Goff, Léni K. Hart, Emma Eiben, Agoston E. De Carlo, Matteo Winfield, Alan F. Hale, Matthew F. Woolley, Robert Timmis, Jon Tyrrell, Andy M. Practical hardware for evolvable robots |
title | Practical hardware for evolvable robots |
title_full | Practical hardware for evolvable robots |
title_fullStr | Practical hardware for evolvable robots |
title_full_unstemmed | Practical hardware for evolvable robots |
title_short | Practical hardware for evolvable robots |
title_sort | practical hardware for evolvable robots |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475714/ https://www.ncbi.nlm.nih.gov/pubmed/37670906 http://dx.doi.org/10.3389/frobt.2023.1206055 |
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