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Comparing lifetime learning methods for morphologically evolving robots

Authors:

Fuda van Diggelen,

E. Ferrante,

A. E. EibenAuthors Info & Claims

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 93 - 94

https://doi.org/10.1145/3449726.3459530

Published: 08 July 2021 Publication History

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Abstract

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.

References

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Matteo De Carlo, Daan Zeeuwe, Eliseo Ferrante, Gerben Meynen, Jacintha Ellers, and A.E. Eiben. 2020. Influences of Artificial Speciation on Morphological Robot Evolution. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, IEEE Computer Society, Washington, DC, USA, 2272--2279.

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Jakub M Tomczak, Ewelina Weglarz-Tomczak, and A.E. Eiben. 2020. Differential Evolution with Reversible Linear Transformations. arXiv preprint arXiv:2002.02869 (2020).

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Fuda van Diggelen, Robert Babuska, and A.E. Eiben. 2020. The Effects of Adaptive Control on Learning Directed Locomotion. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, IEEE Computer Society, Washington, DC, USA, 2117--2124.

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Cited By

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Luo JZeeuwe DEiben A(2024)Gait-Learning with Morphologically Evolving Robots Generated by L-System2024 International Conference on Electrical, Computer and Energy Technologies (ICECET10.1109/ICECET61485.2024.10698145(1-9)Online publication date: 25-Jul-2024
https://doi.org/10.1109/ICECET61485.2024.10698145
Luo JTomczak JMiras KEiben A(2023)A Comparison of Controller Architectures and Learning Mechanisms for Arbitrary Robot Morphologies2023 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI52147.2023.10371941(1518-1525)Online publication date: 5-Dec-2023
https://doi.org/10.1109/SSCI52147.2023.10371941
Luo JMiras KTomczak JEiben A(2023)Enhancing robot evolution through Lamarckian principlesScientific Reports10.1038/s41598-023-48338-413:1Online publication date: 30-Nov-2023
https://doi.org/10.1038/s41598-023-48338-4

Index Terms

Comparing lifetime learning methods for morphologically evolving robots
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
      1. Robotic control
        Evolutionary robotics
2. Computing methodologies
  1. Machine learning

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Published In

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2021

2047 pages

ISBN:9781450383516

DOI:10.1145/3449726

Editor:
Francisco Chicano
University of Malaga
,
General Chair:
Krzysztof Krawiec
Poznan University of Technology

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 08 July 2021

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GECCO '21: Genetic and Evolutionary Computation Conference

July 10 - 14, 2021

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Luo JZeeuwe DEiben A(2024)Gait-Learning with Morphologically Evolving Robots Generated by L-System2024 International Conference on Electrical, Computer and Energy Technologies (ICECET10.1109/ICECET61485.2024.10698145(1-9)Online publication date: 25-Jul-2024
https://doi.org/10.1109/ICECET61485.2024.10698145
Luo JTomczak JMiras KEiben A(2023)A Comparison of Controller Architectures and Learning Mechanisms for Arbitrary Robot Morphologies2023 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI52147.2023.10371941(1518-1525)Online publication date: 5-Dec-2023
https://doi.org/10.1109/SSCI52147.2023.10371941
Luo JMiras KTomczak JEiben A(2023)Enhancing robot evolution through Lamarckian principlesScientific Reports10.1038/s41598-023-48338-413:1Online publication date: 30-Nov-2023
https://doi.org/10.1038/s41598-023-48338-4

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The impact of different tasks on evolved robot morphologies

Evolving-Controllers Versus Learning-Controllers for Morphologically Evolvable Robots

Out of Time: On the Constrains that Evolution in Hardware Faces When Evolving Modular Robots

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