Robotic grasping using POMDPs and machine learning
Name
1192966361-MIT.pdf
Size
8.68 MB
Format
Adobe PDF
Checksum (MD5)
8c47b3f3c66033c441acc3581d63c126
Author(s)
Perez Bedoya, Ignacio.
Advisor(s)
Tomas Lozano-Perez.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Robotic grasping is a fundamental problem in robotics. Currently, there is no single approach for finding good policies that are robust enough to deal with real-world uncertainty, a variety of different objects, and real-time execution. In this thesis, I designed and implemented a grasping algorithm that aims to address these shortcomings. The algorithm is based on two key ideas. First, it uses a POMDP to represent the grasping problem, a physics simulator to approximate the real world, and an offline POMDP solver to generate grasping policies. Then, it uses an RNN to learn from the generated policies given a variety of objects to create a real-time robust policy for grasping. Code can be found at git@github.mit.edu:ignapb/grasping.git
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 59-60).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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