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.
|Title of host publication||GECCO 2021|
|Subtitle of host publication||Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||2|
|Publication status||Published - Jul 2021|
|Event||2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France|
Duration: 10 Jul 2021 → 14 Jul 2021
|Conference||2021 Genetic and Evolutionary Computation Conference, GECCO 2021|
|Period||10/07/21 → 14/07/21|
Bibliographical notePublisher Copyright:
© 2021 Owner/Author.
Copyright 2021 Elsevier B.V., All rights reserved.
- evolutionary robotics
- lifetime learning
- morphological evolution