Model-based and model-free reinforcement learning for visual servoing

A.M. Farahmand, A. Shademan, M. Jagersand, C. Szepesvari
2009 2009 IEEE International Conference on Robotics and Automation  
To address the difficulty of designing a controller for complex visual-servoing tasks, two learning-based uncalibrated approaches are introduced. The first method starts by building an estimated model for the visual-motor forward kinematic of the vision-robot system by a locally linear regression method. Afterwards, it uses a reinforcement learning method named Regularized Fitted Q-Iteration to find a controller (i.e. policy) for the system (model-based RL). The second method directly uses
more » ... es coming from the robot without building any intermediate model (model-free RL). The simulation results show that both methods perform comparably well despite not having any a priori knowledge about the robot.
doi:10.1109/robot.2009.5152834 dblp:conf/icra/FarahmandSJS09 fatcat:khbxelp76jh5bcv6snf3nwxlxm