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Neuro-evolutionary methods have proven effective in addressing a wide rangeof tasks. However, the study of the robustness and generalisability of evolvedartificial neural networks (ANNs) has remained limited. This has immenseimplications in the fields like robotics where such controllers are used incontrol tasks. Unexpected morphological or environmental changes duringoperation can risk failure if the ANN controllers are unable to handle thesechanges. This paper proposes an algorithm that aims to enhance the robustnessand generalisability of the controllers. This is achieved by introducingmorphological variations during the evolutionary process. As a results, it ispossible to discover generalist controllers that can handle a wide range ofmorphological variations sufficiently without the need of the informationregarding their morphologies or adaptation of their parameters. We perform anextensive experimental analysis on simulation that demonstrates the trade-offbetween specialist and generalist controllers. The results show thatgeneralists are able to control a range of morphological variations with a costof underperforming on a specific morphology relative to a specialist. Thisresearch contributes to the field by addressing the limited understanding ofrobustness and generalisability in neuro-evolutionary methods and proposes amethod by which to improve these properties.

Evolving generalist controllers to handle a wide range of morphological variations