Spatial recognition model by extracting correlated information between vision and motion information using neural-network

Spatial recognition ability can be obtained by learning from visual experiments. The author thinks that this ability is to extract correlated information between vision and motion signals. Learning to extract correlated information among multiple inputs can be done without supervisor. The author built a robot with a vision sensor in a computer and it was confirmed that the robot was able to recognize the distance vector of two dimensions from itself to a target object using neural-network without any supervisors.