Anchoring Symbols in Hybrid Autonomous Systems Using Isomap Sequences

We describe a novel approach to anchoring symbols in the sensory data of a hybrid autonomous system. Using an autonomous mobile robot as a test platform we show how an Isomap can be used to detect and properly classify time-series of sensory data. In the past, similar approaches have used self-organizing maps (SOMs) for this purpose. Isomap can be regarded as an improved technique for generating a topology preserving low-dimensional embedding of higher-dimensional data. The interactions of the robot with objects in its environment produce a sequence of points on the map. For object recognition, these sequences need to be correctly classified. This technique forms the basis of our symbol anchoring architecture.