Neuro-evolutionary methods have proven effective in addressing a wide range of tasks. However, the study of the robustness and generalizability 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 generalizability of the controllers. This is achieved by introducing morphological variations during the evolutionary training 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 generalizability and proposes a method by which to improve these properties.

翻译：神经进化方法已被证明能有效解决广泛的任务。然而，关于演化人工神经网络（ANNs）的鲁棒性和泛化能力的研究仍然有限。这在机器人学等领域具有重大意义，因为此类控制器被用于控制任务。如果在运行过程中出现意外的形态或环境变化，而ANN控制器无法处理这些变化，则可能导致失败。本文提出一种旨在增强控制器鲁棒性和泛化能力的算法。这是通过在进化训练过程中引入形态变化来实现的。因此，有可能发现能够充分处理广泛形态变化的通用控制器，而无需关于其形态的信息或调整其参数。我们在仿真中进行了广泛的实验分析，展示了专用控制器与通用控制器之间的权衡。结果表明，通用控制器能够控制一系列形态变化，但代价是在特定形态上相对于专用控制器表现欠佳。本研究通过解决对鲁棒性和泛化能力理解有限的问题，并提出一种改进这些特性的方法，为该领域做出了贡献。