Active Sensorimotor Object Recognition in Three-Dimensional Space

Spatial interaction of biological agents with their environment is based on the cognitive processing of sensory as well as motor information. There are many models for sole sensory processing but only a few for integrating sensory and motor information into a unifying sensorimotor approach. Additionally, neither the relations shaping the integration are yet clear nor how the integrated information can be used in an underlying representation. Therefore, we propose a probabilistic model for integrated processing of sensory and motor information by combining bottom-up feature extraction and top-down action selection embedded in a Bayesian inference approach. The integration of sensory perceptions and motor information brings about two main advantages: (i) Their statistical dependencies can be exploited by representing the spatial relationships of the sensor information in the underlying joint probability distribution and (ii) a top-down process can compute the next most informative region according to an information gain strategy. We evaluated our system in two different object recognition tasks. We found that the integration of sensory and motor information significantly improves active object recognition, in particular when these movements have been chosen by an information gain strategy.