Comparing lifetime learning methods for morphologically evolving robots

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The joint evolution of morphologies and controllers of robots leads to a problem: Even if the parents have well-matching bodies and brains, the stochastic recombination can break this match and cause a body-brain mismatch in their offspring. This can be mitigated by having newborn robots perform a learning process that optimizes their inherited brain quickly after birth. An adequate learning method should work on all possible robot morphologies and be efficient. In this paper we apply Bayesian Optimization and Differential Evolution as learning algorithms and compare them on a test suite of different robot bodies.

Original languageEnglish
Title of host publicationGECCO 2021
Subtitle of host publicationProceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Number of pages2
ISBN (Electronic)9781450383516
Publication statusPublished - Jul 2021
Event2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France
Duration: 10 Jul 202114 Jul 2021


Conference2021 Genetic and Evolutionary Computation Conference, GECCO 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021 Owner/Author.

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


  • evolutionary robotics
  • lifetime learning
  • morphological evolution


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