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

Full Text: farahmand2009a.pdf PDF

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.

Citation

A. Farahmand, A. Shademan, M. Jagersand, C. Szepesvari. "Model-based and model-free reinforcement learning for visual servoing". IEEE International Conference on Robotics and Automation (ICRA), pp 2917-2924, May 2009.

Keywords: Reinforcement Learning, Robotics, Vision, Visual Servoing
Category: In Conference

BibTeX

@incollection{Farahmand+al:ICRA09,
  author = {A.M. Farahmand and Azad Shademan and Martin Jagersand and Csaba
    Szepesvari},
  title = {Model-based and model-free reinforcement learning for visual
    servoing},
  Pages = {2917-2924},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  year = 2009,
}

Last Updated: January 05, 2012
Submitted by Azad Shademan

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