Multiresolutional intelligent controller for baby robot

Abstract

This paper presents an algorithm of unsupervised learning for applications in robotics. Minimum initial knowledge is presumed ("bootstrap knowledge"). The learning system uses the newly arrived information to extract rules of motion and construct the world representation. The concept of recursive generalization is explored as the main tool of rule extraction and knowledge organization. The experiment in learning is described based upon simulation of a 2D and a 3D mobile system.