Self-Organized Learning Model for behavior emergence and its application to mobile robot

In this paper, we propose a self-organized learning model that can generate behaviors for successfully performing various tasks, unlike conventional systems that could perform only a limited task in a limited environment. The model memorizes various relationships between changes in sensory information and a motor command through learning. After the learning, the model can perform various tasks by generating the various behaviors automatically. We investigated the performance of the model by applying it to a mobile robot simulation. In the simulation, we applied two different tasks to the model, which performed the same learning. The results indicate that suitable behaviors for all the tasks emerged spontaneously.