Learning directed locomotion in modular robots with evolvable morphologies

Gongjin Lan*, Matteo De Carlo, Fuda van Diggelen, Jakub M. Tomczak, Diederik M. Roijers, A. E. Eiben

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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Abstract

The vision behind this paper looks ahead to evolutionary robot systems where morphologies and controllers are evolved together and ‘newborn’ robots undergo a learning process to optimize their inherited brain for the inherited body. The specific problem we address is learning controllers for the task of directed locomotion in evolvable modular robots. To this end, we present a test suite of robots with different shapes and sizes and compare two learning algorithms, Bayesian optimization and HyperNEAT. The experiments in simulation show that both methods obtain good controllers, but Bayesian optimization is more effective and sample efficient. We validate the best learned controllers by constructing three robots from the test suite in the real world and observe their fitness and actual trajectories. The obtained results indicate a reality gap, but overall the trajectories are adequate and follow the target directions successfully.

Original languageEnglish
Article number107688
Pages (from-to)1-17
Number of pages17
JournalApplied Soft Computing
Volume111
Early online date9 Jul 2021
DOIs
Publication statusPublished - Nov 2021

Bibliographical note

Funding Information:
The authors would like to thank Maarten van Hooft for his valuable suggestions and helpful coding. Matteo De Carlo was funded by the ARE@VU project.

Publisher Copyright:
© 2021

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Bayesian optimization
  • Directed locomotion
  • Evolutionary robotics
  • Evolvable morphologies
  • Modular robots

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