TY - JOUR
T1 - Autonomous and fast robot learning through motivation
AU - Rodríguez, M.
AU - Iglesias, R.
AU - Regueiro, C. V.
AU - Correa, J.
AU - Barro, S.
PY - 2007/9/30
Y1 - 2007/9/30
N2 - 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.
AB - 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.
KW - Autonomous agents
KW - Genetic algorithms
KW - Reinforcement learning
KW - Robot control
UR - http://www.scopus.com/inward/record.url?scp=34548040193&partnerID=8YFLogxK
U2 - 10.1016/j.robot.2007.05.005
DO - 10.1016/j.robot.2007.05.005
M3 - Article
AN - SCOPUS:34548040193
SN - 0921-8890
VL - 55
SP - 735
EP - 740
JO - Robotics and Autonomous Systems
JF - Robotics and Autonomous Systems
IS - 9
ER -