Sensory Channel Group and Structure from Uninterpreted Sensor Data

In this paper we focus on the problem of making a model of the sensory apparatus from raw uninterpreted sensory data as defined by Pierce and Kuipers (Artificial Intelligence 92:169-227, 1997). The method relies on generic properties of the agent’s world such as piecewise smooth effects of movement on sensory features. We extend a previously described algorithm with an information-theoretic distance metric that can find informational structure not found by the original algorithm. We also use the method to create metric projections of the sensory and motor systems of a robot. Data from a real robot show that the metric projections for example can be used to distinguish the vision sensors from all other sensors and also to find their functional layout. Finally we present an application of the method where the real layout of the vision sensors is found from scrambled vision data.