%0 Conference Paper %1 farahmand2009a %A Farahmand, A.m. %A Shademan, A. %A Jägersand, M. %A Szepesvári, Cs. %B ICRA %D 2009 %K application, learning reinforcement robotics, vision, %P 2917--2924 %T Model-based and Model-free Reinforcement Learning for Visual Servoing %X 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.

@inproceedings{farahmand2009a, abstract = {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.}, added-at = {2020-03-17T03:03:01.000+0100}, author = {Farahmand, A.{m}. and Shademan, A. and J{\"a}gersand, M. and Szepesv{\'a}ri, {Cs}.}, biburl = {https://www.bibsonomy.org/bibtex/2d06e23df3c66bbf4b27a1377a8074c6c/csaba}, booktitle = {ICRA}, date-added = {2010-08-28 17:38:14 -0600}, date-modified = {2010-09-02 13:09:16 -0600}, interhash = {31b72e44d2a754456d518af48326e981}, intrahash = {d06e23df3c66bbf4b27a1377a8074c6c}, keywords = {application, learning reinforcement robotics, vision,}, pages = {2917--2924}, pdf = {papers/MBRL4VisualServoing.pdf}, timestamp = {2020-03-17T03:03:01.000+0100}, title = {Model-based and Model-free Reinforcement Learning for Visual Servoing}, year = 2009 }