Morpho-evolution with learning using a controller archive as an inheritance mechanism

Léni Le Goff, Edgar Buchanan Berumen, Emma Hart, A. E. Eiben, Wei Li, Matteo De Carlo, Alan Winfield, Matthew Hale, Robert Woolley, Jon Timmis, Andy Tyrrell

Research output: Contribution to journalArticlepeer-review


The joint optimisation of body-plan and control via
evolutionary processes can be challenging in rich morphological
spaces in which offspring can have body-plans that are very
different from either of their parents. This causes a potential mismatch
between the structure of an inherited controller and the
new body. To address this, we propose a framework that combines
an evolutionary algorithm to generate body-plans and a learning
algorithm to optimise the parameters of a neural controller. The
topology of this controller is created once the body-plan of each
offspring body-plan is generated. The key novelty of the approach
is to add an external archive for storing learned controllers that
map to explicit ‘types’ of robots (where this is defined with respect
the features of the body-plan). By learning from a controller with
an appropriate structure inherited from the archive, rather than
from a randomly initialised one, we show that both the speed
and magnitude of learning increases over time when compared
to an approach that starts from scratch, using three different
test-beds. The framework also provides new insights into the
complex interactions between evolution and learning, and the
role of morphological intelligence in robot design.
Original languageEnglish
Article number15
Pages (from-to)507-517
Number of pages11
JournalIEEE Transactions on Cognitive and Developmental Systems
Issue number2
Early online date2 Feb 2022
Publication statusPublished - 1 Jun 2023

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  • Evolutionary robotics
  • Embodied Intelligence

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