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各类任务。然而，关于进化人工神经网络（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——此类控制器被用于控制任务时，若ANN控制器无法应对运行过程中出现的意外形态或环境变化，将面临失效风险。本文提出一种旨在增强控制器鲁棒性与泛化能力的算法，通过在进化训练过程中引入形态变化来实现该目标。结果表明，无需获取形态信息或调整参数，即可发现能够充分应对多种形态变化的通用控制器。我们在仿真环境中进行了广泛的实验分析，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，通用控制器虽能以特定形态性能低于专用控制器为代价，但能有效控制多种形态变化。本研究通过弥补对鲁棒性与泛化能力认知的不足，并提出改进方法，为该领域做出贡献。