Spatial learning with perceptually grounded representations

The goal of this paper is to develop the foundation for a spatial navigation without objective representations. Rather than building the spatial representations on a Euclidean space, a weaker conception of space is used which has a closer connection to perception. A type of spatial representation is described that uses perceptual information directly to define regions in space. By combining such regions, it is possible to derive a number of useful spatial representations such as place-fields, paths and topological maps. Compared to other methods, the representations of the presented approach have the advantage that they are always grounded in the perceptual abilities of the robot, and thus, more likely to function correctly.