Inferring beliefs for search and rescue from natural language
Name
1084654173-MIT.pdf
Description
Full printable version
Size
9.92 MB
Format
Adobe PDF
Checksum (MD5)
0a253d7ae23c505ec0def04b628763f7
Author(s)
Schurr, Naomi D. (Naomi Danika)
Advisor(s)
Nicholas Roy.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
A learned natural language robotic interface can allow a human operator to intuitively communicate instructions to a robot. A number of models, including probabilistic grounding graphs, have been used to ground natural language input to the real-world tasks a robot must perform. In this thesis, I provide two extensions to existing work in grounding natural language instructions. First, I apply an existing probabilistic grounding graph model in the context of outdoor search and rescue, introducing a new set of groundings to allow a continuous cost map to be inferred from the natural language. Second, I incorporate pool-based active learning into the training of the probabilistic grounding graph model, which shows promise for reducing the number of labeled examples needed to train the model.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 97-101).
Subjects
Aeronautics and Astronautics.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
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.
Persistent DSpace Link
