Learnablity of a spiking neural network for perception of a partner robot

This paper discusses a perceptual system for a partner robot from the viewpoint of human visual perception. Recently, various types of robots equip various types of sensors for perceiving the environment. However, the robot must perceive the necessary information from too much information to take a flexible action like a human. In this study, we emphasize the importance of human vision for the robot to realize perception and action flexibility. Especially we focus on the perceptual system based on perceiving-acting cycle discussed in ecological psychology. First, we propose a retinal model for a laser range finder based on human retinal structure, and the information extraction method using a spiking neural network based on perceiving-acting cycle. Next, we apply the proposed method for a human tracing task in a dynamic environment. As an experimental result, we show the robot can directly perceive the necessary information by the attention mechanism for the flexible perception according to spatiotemporal context based on the spiking neural network.