Robotic grasping using POMDPs and machine learning
Author(s)Perez Bedoya, Ignacio.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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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 firstname.lastname@example.org:ignapb/grasping.git
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020Cataloged from the official PDF of thesis.Includes bibliographical references (pages 59-60).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.