  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.
