| dc.contributor.advisor | Tomas Lozano-Perez. | en_US |
| dc.contributor.author | Perez Bedoya, Ignacio. | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2020-09-15T21:58:13Z | |
| dc.date.available | 2020-09-15T21:58:13Z | |
| dc.date.copyright | 2020 | en_US |
| dc.date.issued | 2020 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1721.1/127442 | |
| dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020 | en_US |
| dc.description | Cataloged from the official PDF of thesis. | en_US |
| dc.description | Includes bibliographical references (pages 59-60). | en_US |
| dc.description.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 | en_US |
| dc.description.statementofresponsibility | by Ignacio Perez Bedoya. | en_US |
| dc.format.extent | 60 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | 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. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | Robotic grasping using POMDPs and machine learning | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M. Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
| dc.identifier.oclc | 1192966361 | en_US |
| dc.description.collection | M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science | en_US |
| dspace.imported | 2020-09-15T21:58:13Z | en_US |
| mit.thesis.degree | Master | en_US |
| mit.thesis.department | EECS | en_US |
