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Bootstrapping artificial evolution to design robots for autonomous fabrication

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


Edgar Buchanan

Wei Li

Agoston E. Eiben

Matteo De Carlo

Alan Winfield

Matthew F. Hale

Robert Woolley

Mike Angus

Jon Timmis

Andy M. Tyrrell


A long-term vision of evolutionary robotics is a technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. Evolutionary Robotics has been widely used due to its capability of creating unique robot designs in simulation. Recent work has shown that it is possible to autonomously construct evolved designs in the physical domain, however this brings new challenges: the autonomous manufacture and assembly process introduces new constraints that are not apparent in simulation. To tackle this, we introduce a new method for producing a repertoire of diverse but manufacturable robots. This repertoire is used to seed an evolutionary loop that subsequently evolves robot designs and controllers capable of solving a maze-navigation task. We show that compared to random initialisation, seeding with a diverse and manufacturable population speeds up convergence and on some tasks, increases performance, while maintaining manufacturability.

Journal Article Type Article
Acceptance Date Dec 2, 2020
Online Publication Date Dec 7, 2020
Publication Date 2020-12
Deposit Date Dec 4, 2020
Publicly Available Date Dec 7, 2020
Electronic ISSN 2218-6581
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 9
Issue 4
Article Number 106
Keywords evolutionary robotics, autonomous robot evolution, autonomous robot fabrication, robot manufacturability
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Bootstrapping Artificial Evolution To Design Robots For Autonomous Fabrication (accepted version) (21.4 Mb)

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Copyright Statement
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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