ICRA'09 Paper Abstract

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Paper FrD6.4

Farahmand, Amir massoud (University of Alberta), Shademan, Azad (University of Alberta), Jagersand, Martin (University of Alberta), Szepesvari, Csaba (University of Alberta)

Model-Based and Model-Free Reinforcement Learning for Visual Servoing

Scheduled for presentation during the Regular Sessions "Learning and Adaptive Systems - IV" (FrD6), Friday, May 15, 2009, 16:30−16:50, Room: 404

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 21, 2022

Keywords Visual Servoing, Learning and Adaptive Systems, Motion and Path Planning

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.

 

 

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