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

• URL: http://arxiv.org/abs/2104.04269v1
• Date: Fri, 9 Apr 2021 09:32:36 GMT
• Title: Morpho-evolution with learning using a controller archive as an inheritance mechanism
• Authors: L\'eni K. Le Goff, Edgar Buchanan, Emma Hart, Agoston E. Eiben, Wei Li, Matteo De Carlo, Alan F. Winfield, Matthew F. Hale, Robert Woolley, Mike Angus, Jon Timmis, Andy M. Tyrrell
• Abstract summary: 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. By inheriting an appropriate controller from an archive rather than learning from a randomly initialised one, we show that both the speed and magnitude of learning increases over time. The framework also provides new insights into the complex interactions between evolution and learning, and the role of morphological intelligence in robot design.
• Score: 4.364091192392204
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In evolutionary robotics, several approaches have been shown to be capable of the joint optimisation of body-plans and controllers by either using only evolution or combining evolution and learning. When working in rich morphological spaces, it is common for offspring to have body-plans that are very different from either of their parents, which can cause difficulties with respect to inheriting a suitable controller. 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 where 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 inheriting an appropriate controller from the archive rather than learning 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.

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