Comparing lifetime learning methods for morphologically evolving robots

• URL: http://arxiv.org/abs/2203.03967v1
• Date: Tue, 8 Mar 2022 09:49:56 GMT
• Title: Comparing lifetime learning methods for morphologically evolving robots
• Authors: Fuda van Diggelen, Eliseo Ferrante, A.E. Eiben
• Abstract summary: Evolving morphologies and controllers of robots simultaneously leads to a problem. Even if the parents have well-matching bodies and brains, the 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 optimize their inherited brain quickly after birth.
• Score: 3.6095388702618414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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