Abstract
In this paper, we propose a sequential learning model that can generate behaviors for successfully performing various tasks. The model generates the actions based on change in state pattern. The model updates the memorized relationships between changes in sensory information and a motor command through sequential learning. We confirmed the performance of the model by applying it to a mobile robot simulation. The results indicate that suitable behaviors for all the tasks emerged by the sequential learning.
