Internal topographical structure in training autonomous robot

In this research we propose a trainable controller for a mobile robot based on a layered neural network, in which the hidden layer is a topographical map. In this study we focus not only on building a general controller that can be embedded to mobile robots running in physical environment, but also on building controllers with good internal plausibility. We consider that internal plausibility of the physical functionality acquired by robots during their learning phase is important in increasing their usability in real world tasks. The internal plausibility in this study can be obtained by associating the topographical map formed internally with the actions of the robots. Here, we run some experiments using a small robot, e-puck, and report the preliminary results of our study.