Following directions using statistical machine translation

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MatuszekHRI2010DirectionFollowing.pdf (3.53 MB)

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http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5453189&isnumber=5453161

Permanent Link

10.1109/HRI.2010.5453189
 http://hdl.handle.net/11603/11257

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UMBC Computer Science and Electrical Engineering Department

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Author/Creator ORCID

Date

2010-04-22

Type of Work

conference papers and proceedings preprints
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Citation of Original Publication

C. Matuszek, D. Fox and K. Koscher, "Following directions using statistical machine translation," 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Osaka, 2010, pp. 251-258.

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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.

Subjects

Natural languages
Humans
Mobile robots
Bridges
Robotics and automation
Training data
Automatic control
Process control
Uncertainty
Labeling
Interactive Robotics and Language Lab

Abstract

Mobile robots that interact with humans in an intuitive way must be able to follow directions provided by humans in unconstrained natural language. In this work we investigate how statistical machine translation techniques can be used to bridge the gap between natural language route instructions and a map of an environment built by a robot. Our approach uses training data to learn to translate from natural language instructions to an automatically-labeled map. The complexity of the translation process is controlled by taking advantage of physical constraints imposed by the map. As a result, our technique can efficiently handle uncertainty in both map labeling and parsing. Our experiments demonstrate the promising capabilities achieved by our approach.