New approach to get autonomous and fast robot learning processes

Research on robot techniques that are fast, user-friendly, and require little application-specific knowledge by the user, is more and more encouraged in a society where the demand of home-care or domestic-service robots is increasing continuously. In this context we propose a methodology which is able to achieve fast convergences towards good robot-control policies, and reduce the random explorations the robot needs to carry out in order to find the solutions. The performance of our approach is due to the mutual influence that three different elements exert on each other reinforcement learning, genetic algorithms, and a dynamic representation of the environment around the robot. The performance of our proposal is shown through its application to solve two common tasks in mobile robotics.