Location processor for inferencing and learning based on sensorimotor input data

Abstract

An inference system performs inference, such as object recognition, based on sensory inputs generated by sensors and control information associated with the sensory inputs. The sensory inputs describe one or more features of the objects. The control information describes movement of the sensors or known locations of the sensors relative to a reference point. For a particular object, an inference system learns a set of object-location representations of the object. An object-location representation is a unique characterization of an object-centric location relative to the particular object. The inference system also learns a set of feature-location representations associated with the object-location representation that indicate presence of features at the corresponding object-location pair. The inference system can perform inference on an unknown object by identifying candidate object-location representations consistent with feature-location representations observed from the sensory input data and control information.