Comparing lifetime learning methods for morphologically evolving robots

Evolving morphologies and controllers of robots simultaneously 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. We argue that this can be mitigated by having newborn rob...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-03
Main Authors: Fuda van Diggelen, Ferrante, Eliseo, Eiben, A E
Format: Article
Language:English
Subjects:
Algorithms
Brain
Evolution
Learning
Morphology
Robots
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Evolving morphologies and controllers of robots simultaneously 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. We argue that this can be mitigated by having newborn robots perform a learning process that optimizes their inherited brain quickly after birth. We compare three different algorithms for doing this. To this end, we consider three algorithmic properties, efficiency, efficacy, and the sensitivity to differences in the morphologies of the robots that run the learning process.
ISSN:2331-8422