2026-09-20T22:40:27Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1134972026-06-06T00:55:19Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Nicholas Roy.
Tucker, Mycal (Mycal D.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-02-08T16:26:30Z
2018-02-08T16:26:30Z
2016
2016
http://hdl.handle.net/1721.1/113497
1020068951
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 91-96).
Research in automatic natural language grounding, in which robots understand how phrases relate to real-world objects or actions, offers a compelling reality in which untrained humans can operate highly sophisticated robots. Current techniques for training robots to understand natural language, however, assume that there is a fixed set of phrases or objects that the robot will encounter during deployment. Instead, the real world is full of confusing jargon and unique objects that are nearly impossible to anticipate and therefore train for. This thesis presents a model called the Distributed Correspondence Graph - Unknown Phrase, Unknown Percept - Away (DCG-UPUP-Away) that augments the state of the art Distributed Correspondence Graph by recognizing unknown phrases and objects as unknown, as well as reasoning about objects that are not currently perceived. Furthermore, experimental results in simulation, as well as a trial run on a turtlebot platform, validate the effectiveness of DCG-UPUP-Away in grounding phrases and learning new phrases.
by Mycal Tucker.
M.Eng.
96 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
DCG-UPUP-Away : automatic symbol acquisition through grounding to unknowns
Distributed Correspondence Graph - Unknown Phrase, Unknown Percept
Automatic symbol acquisition through grounding to unknowns
Thesis
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<Abstract>Research in automatic natural language grounding, in which robots understand how phrases relate to real-world objects or actions, offers a compelling reality in which untrained humans can operate highly sophisticated robots. Current techniques for training robots to understand natural language, however, assume that there is a fixed set of phrases or objects that the robot will encounter during deployment. Instead, the real world is full of confusing jargon and unique objects that are nearly impossible to anticipate and therefore train for. This thesis presents a model called the Distributed Correspondence Graph - Unknown Phrase, Unknown Percept - Away (DCG-UPUP-Away) that augments the state of the art Distributed Correspondence Graph by recognizing unknown phrases and objects as unknown, as well as reasoning about objects that are not currently perceived. Furthermore, experimental results in simulation, as well as a trial run on a turtlebot platform, validate the effectiveness of DCG-UPUP-Away in grounding phrases and learning new phrases.</Abstract>
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