Inferring beliefs for search and rescue from natural language

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Schurr, Naomi D. (Naomi Danika)

Advisor dc:contributor.advisor

• Nicholas Roy.

• Aeronautics and Astronautics.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/120439

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/120439