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Morpho-evolution with learning using a controller archive as an inheritance mechanism

Le Goff, Leni K.; Buchanan, Edgar; Hart, Emma; Eiben, Agoston E.; Li, Wei; De Carlo, Matteo; Winfield, Alan F.; Hale, Matthew F.; Woolley, Robert; Angus, Mike; Timmis, Jon; Tyrrell, Andy M.

Authors

Leni K. Le Goff

Edgar Buchanan

Emma Hart

Agoston E. Eiben

Wei Li

Matteo De Carlo

Matthew F. Hale

Robert Woolley

Mike Angus

Jon Timmis

Andy M. Tyrrell



Abstract

Most work in evolutionary robotics centres on evolving a controller for a fixed body-plan. However, previous studies suggest that simultaneously evolving both controller and body-plan could open up many interesting possibilities. However, the joint optimisation of body-plan and control via evolutionary processes can be challenging in rich morphological spaces. This is because offspring can have body-plans that are very different from either of their parents, leading to a potential mismatch between the structure of an inherited neural controller and the new body. To address this, we propose a framework that combines an evolutionary algorithm to generate body-plans and a learning algorithm to optimise the parameters of a neural controller. The topology of this controller is created once the body-plan of each offspring has been generated. The key novelty of the approach is to add an external archive for storing learned controllers that map to explicit ‘types’ of robots (where this is defined with respect to the features of the body-plan). By initiating learning from a controller with an appropriate structure inherited from the archive, rather than from a randomly initialised one, we show that both the speed and magnitude of learning increases over time when compared to an approach that starts from scratch, using two tasks and three environments. The framework also provides new insights into the complex interactions between evolution and learning.

Citation

Le Goff, L. K., Buchanan, E., Hart, E., Eiben, A. E., Li, W., De Carlo, M., …Tyrrell, A. M. (2023). Morpho-evolution with learning using a controller archive as an inheritance mechanism. IEEE Transactions on Cognitive and Developmental Systems, 15(2), 507 - 517. https://doi.org/10.1109/tcds.2022.3148543

Journal Article Type Article
Acceptance Date Jan 16, 2022
Online Publication Date Feb 2, 2022
Publication Date Jun 30, 2023
Deposit Date Mar 23, 2022
Journal IEEE Transactions on Cognitive and Developmental Systems
Print ISSN 2379-8920
Electronic ISSN 2379-8939
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 15
Issue 2
Pages 507 - 517
DOI https://doi.org/10.1109/tcds.2022.3148543
Keywords Artificial Intelligence; Software; Evolutionary robotics; Embodied Intelligence; Robots , Optimization; Topology; Statistics; Sociology; Process control; Aerospace electronics
Public URL https://uwe-repository.worktribe.com/output/9228321