Inferring beliefs for search and rescue from natural language
Author(s)Schurr, Naomi D. (Naomi Danika)
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics.
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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.
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).
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics.
Massachusetts Institute of Technology
Aeronautics and Astronautics.