The Effects of Learning in Morphologically Evolving Robot Systems

Jie Luo*, Aart C. Stuurman, Jakub M. Tomczak, Jacintha Ellers, Agoston E. Eiben

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Simultaneously evolving morphologies (bodies) and controllers (brains) of robots can cause a mismatch between the inherited body and brain in the offspring. To mitigate this problem, the addition of an infant learning period has been proposed relatively long ago by the so-called Triangle of Life approach. However, an empirical assessment is still lacking to-date. In this paper, we investigate the effects of such a learning mechanism from different perspectives. Using extensive simulations we show that learning can greatly increase task performance and reduce the number of generations required to reach a certain fitness level compared to the purely evolutionary approach. Furthermore, we demonstrate that the evolved morphologies will be also different, even though learning only directly affects the controllers. This provides a quantitative demonstration that changes in the brain can induce changes in the body. Finally, we examine the learning delta defined as the performance difference between the inherited and the learned brain, and find that it is growing throughout the evolutionary process. This shows that evolution produces robots with an increasing plasticity, that is, consecutive generations become better learners and, consequently, they perform better at the given task. Moreover, our results demonstrate that the Triangle of Life is not only a concept of theoretical interest, but a system methodology with practical benefits.

Original languageEnglish
Article number797393
Pages (from-to)1-18
Number of pages18
JournalFrontiers in Robotics and AI
Volume9
Issue numberMay
Early online date27 May 2022
DOIs
Publication statusPublished - May 2022

Bibliographical note

Publisher Copyright:
Copyright © 2022 Luo, Stuurman, Tomczak, Ellers and Eiben.

Keywords

  • embodied intelligence
  • evolutionary robotics
  • evolvable morphologies
  • lifetime learning
  • modular robots
  • targeted locomotion

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