Robustness and generalizability are the key properties of artificial neural network (ANN)-based controllers for maintaining a reliable performance in case of changes. It is demonstrated that exposing the ANNs to variations during training processes can improve their robustness and generalization capabilities. However, the way in which this variation is introduced can have a significant impact. In this paper, we define various training schedules to specify how these variations are introduced during an evolutionary learning process. In particular, we focus on morphological robustness and generalizability concerned with finding an ANN-based controller that can provide sufficient performance on a range of physical variations. Then, we perform an extensive analysis of the effect of these training schedules on morphological generalization. Furthermore, we formalize the process of training sample selection (i.e., morphological variations) to improve generalization as a reinforcement learning problem. Overall, our results provide deeper insights into the role of variability and the ways of enhancing the generalization property of evolved ANN-based controllers.

翻译：鲁棒性与泛化能力是基于人工神经网络（ANN）的控制器在工况变化时保持可靠性能的关键特性。研究表明，在训练过程中使人工神经网络接触变化能提升其鲁棒性与泛化能力。然而，引入这种变化的方式可能产生显著影响。本文定义了多种训练计划，用以明确在进化学习过程中如何引入这些变化。我们特别关注形态鲁棒性与泛化能力，旨在寻找能在多种物理变化条件下提供足够性能的基于ANN的控制器。随后，我们对这些训练计划如何影响形态泛化进行了深入分析。此外，我们将训练样本选择（即形态变化）以提升泛化能力的过程形式化为强化学习问题。总体而言，我们的研究结果为理解变异性的作用以及增强进化ANN控制器泛化能力的途径提供了更深刻的见解。