Hierarchical sensory-motor fusion model with neural networks

Human beings recognize the physical world by integrating a variety of sensory inputs, information acquired by their own actions, and their knowledge of the world using a hierarchical parallel distributed mechanism. Sensor fusion technology focuses on imitating this mechanism and is intended for advanced sensing systems with abilities exceeding those of unimodal sensory information processing systems. Our study of sensor fusion aims to develop a hierarchical sensory motor fusion mechanism capable of intentional sensing: the concept that sensing has a goal and sensing behavior must be oriented to achieve this goal. In this paper, we propose a hierarchical sensory motor fusion model with neural networks for intentional sensing. The model propagates intentions, tightly couples recognition and action, and can perform different tasks flexibly. The model includes an algorithm called iterative inversion, which we also propose for making use of multilayer neural networks as a way to solve inverse problems of sensory information processing. We applied the hierarchical sensory-motor fusion model to a three-dimensional object recognition system and demonstrated the effectiveness of the model by computer simulations