Neuro-evolutionary methods have proven effective in addressing a wide range of tasks. However, the study of the robustness and generalisability of evolved artificial neural networks (ANNs) has remained limited. This has immense implications in the fields like robotics where such controllers are used in control tasks. Unexpected morphological or environmental changes during operation can risk failure if the ANN controllers are unable to handle these changes. This paper proposes an algorithm that aims to enhance the robustness and generalisability of the controllers. This is achieved by introducing morphological variations during the evolutionary process. As a results, it is possible to discover generalist controllers that can handle a wide range of morphological variations sufficiently without the need of the information regarding their morphologies or adaptation of their parameters. We perform an extensive experimental analysis on simulation that demonstrates the trade-off between specialist and generalist controllers. The results show that generalists are able to control a range of morphological variations with a cost of underperforming on a specific morphology relative to a specialist. This research contributes to the field by addressing the limited understanding of robustness and generalisability in neuro-evolutionary methods and proposes a method by which to improve these properties.

翻译：神经进化方法已被证明在处理广泛任务中具有有效性。然而，对进化人工神经网络（ANN）鲁棒性与泛化能力的研究仍较为有限。这在机器人等领域具有重大影响——当此类控制器用于控制任务时，若人工神经网络控制器无法应对运行中意外的形态或环境变化，可能面临失效风险。本文提出一种旨在提升控制器鲁棒性与泛化能力的算法。该算法通过在进化过程中引入形态变化来实现目标。实验表明，该方法能够发现通用控制器，使其无需依赖形态信息或参数调整，便能充分处理多种形态变化。我们基于仿真进行了大量实验分析，揭示了专用控制器与通用控制器之间的权衡关系。结果显示，通用控制器能有效控制一系列形态变化，但代价是在特定形态上的性能劣于专用控制器。本研究通过解决神经进化方法中鲁棒性与泛化性认知不足的问题，并提出改进这些特性的方法，为该领域做出了贡献。