Optimizing robot trajectories using reinforcement learning
Name
191913909-MIT.pdf
Description
Full printable version
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6.03 MB
Format
Adobe PDF
Checksum (MD5)
94b99ae469f240dc9a22be8623acb50e
Author(s)
Kollar, Thomas (Thomas Fleming)
Advisor(s)
Nicholas Roy.
Date Issued
2007
Publisher
Massachusetts Institute of Technology
Abstract
The mapping problem has received considerable attention in robotics recently. Mature techniques now allow practitioners to reliably and consistently generate 2-D and 3-D maps of objects, office buildings, city blocks and metropolitan areas with a comparatively small number of errors. Nevertheless, the ease of construction and quality of map are strongly dependent on the exploration strategy used to acquire sensor data. Most exploration strategies concentrate on selecting the next best measurement to take, trading off information gathering for regular relocalization. What has not been studied so far is the effect the robot controller has on the map quality. Certain kinds of robot motion (e.g, sharp turns) are hard to estimate correctly, and increase the likelihood of errors in the mapping process. We show how reinforcement learning can be used to generate better motion control. The learned policy will be shown to reduce the overall map uncertainty and squared error, while jointly reducing data-association errors.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (leaves 93-96).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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