Autonomous and fast robot learning through motivation

✍ Scribed by M. Rodríguez; R. Iglesias; C.V. Regueiro; J. Correa; S. Barro

• Book ID: 104090802
• Publisher: Elsevier Science
• Year: 2007
• Tongue: English
• Weight: 877 KB
• Volume: 55
• Category: Article
• ISSN: 0921-8890
• DOI: 10.1016/j.robot.2007.05.005

✦ Synopsis

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 combines reinforcement learning and genetic algorithms to teach a robot how to perform a task when only the specification of the main restrictions of the desired behaviour is provided. Through this combination, both paradigms must be merged in such a way that they influence each other to achieve a fast convergence towards a good robot-control policy, and reduce the random explorations the robot needs to carry out in order to find a solution.

Another advantage of our proposal is that it is able to easily incorporate any kind of domain-dependent knowledge about the task. This is very useful for improving a robot controller, for applying a robot-controller to move a different robot-platform, or when we have certain "feelings" about how the task should be solved.

The performance of our proposal is shown through its application to solve a common problem in mobile robotics.