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

— 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 samples 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.

• Type: Conference
• Year: 2009
• Where: ICRA
• Authors: Amir Massoud Farahmand, Azad Shademan, Martin Jägersand, Csaba Szepesvári
• Keywords: ICRA 2009, Learning-based Uncalibrated Approaches, Linear Regression Method, Robotics, first Method