Li, Wei orcid.org/0000-0001-9786-585X, Buchanan Berumen, Edgar orcid.org/0000-0001-6587-8808, Le Goff, Léni et al. (8 more authors) (2024) Evaluation of frameworks that combine evolution and learning to design robots in complex morphological spaces. IEEE Transactions on Evolutionary Computation. pp. 1561-1574. ISSN 1089-778X
Abstract
Jointly optimising both the body and brain of a robot is known to be a challenging task, especially when attempting to evolve designs in simulation that will subsequently be built in the real world. To address this, it is increasingly common to combine evolution with a learning algorithm that can either improve the inherited controllers of new offspring to fine tune them to the new body design or learn them from scratch. In this paper an approach is proposed in which a robot is specified indirectly by two compositional pattern producing networks (CPPN) encoded in a single genome, one which encodes the brain and the other the body. The body part of the genome is evolved using an evolutionary algorithm (EA), with an individual learning algorithm (also an EA) applied to the inherited controller to improve it. The goal of this paper is to determine how to utilise the results of learning process most effectively to improve task performance of the robot. Specifically, three variants are investigated: (1) evolution of the body+controller only; (2) a learning algorithm is applied to the inherited controller with the learned fitness assigned to the genome; (3) learning is applied and the genome is updated with the learned controller, as well as being assigned the learned fitness. Experiments are performed in three different scenarios chosen to favour different bodies and locomotion patterns. It is shown that better performance can be obtained using learning but only if the learned controller is inherited by the offspring.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Keywords: | Morphological Evolution,Evolution and Learning,Embodied Intelligence |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Funding Information: | Funder Grant number EPSRC EP/R03561X/1 |
Depositing User: | Pure (York) |
Date Deposited: | 06 Sep 2023 15:10 |
Last Modified: | 21 Jan 2025 18:09 |
Published Version: | https://doi.org/10.1109/TEVC.2023.3316363 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/TEVC.2023.3316363 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:203144 |
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Description: Wei's IEEE paper Evaluation_of_frameworks_that_combine_evolution_and_learning_to_design_robots_in_complex_morphological_spaces
Licence: CC-BY 2.5