PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Model-based and Model-free Reinforcement Learning for Visual Servoing
Amir massoud Farahmand, Azad Shademan, Martin Jagersand and Csaba Szepesvari
In: ICRA(2009).


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:4928
Deposited By:Csaba Szepesvari
Deposited On:24 March 2009