Learning to understand spatial language for robotic navigation and mobile manipulation

This thesis focuses on understanding task-constrained natural language commands, where a person gives a natural language command to the robot and the robot infers and executes the corresponding plan. Understanding natural language is difficult because a system must infer the location of landmarks such as "the computer cluster," and actions corresponding to spatial relations such as "to" or "around" and verbs such as "put" or "take." each of which may be composed in complex ways. In addition, different people may give very different types of commands to perform the same action. The first chapter of this thesis focuses on simple natural language commands such as "Find the computer." where a person commands the robot to find an object or place and the robot must infer a corresponding plan. This problem would be easy if we constrained the set of words that the robot might need to reason about. However, if a person says, "find the computer," and the robot has not previously detected a "computer," then it is not clear where the robot should look. We present a method that uses previously detected objects and places in order to bias the search process toward areas of the environment where a previously unseen object is likely to be found. The system uses a semantic map of the environment together with a model of contextual relationships between objects to infer this plan, which finds the query object with minimal travel time. The contextual relationships are learned from the captions of a large dataset of photos downloaded from Flickr. Simulated and realworld experiments show that a small subset of detectable objects and scenes are able to predict the location of previously unseen objects and places. In the second chapter, we take steps toward building a robust spatial language understanding system for three different domains: route directions, visual inspection, and indoor mobility. We take as input a natural language command such as "Go through the double doors and down the hallway," extract a semantic structure called a Spatial Description Clause (SDC) from the language, and ground each SDC in a partial or complete semantic map of the environment. By extracting a flat sequence of SDCs, we are able to ground the language by using a probabilistic graphical model that is factored into three key components. First, a landmark component grounds novel noun phrases such as "'the computers" in the perceptual frame of the robot by exploiting object co-occurrence statistics between unknown noun phrases and known perceptual features.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2011

Author dc:creator

• Kollar, Thomas (Thomas Fleming)

Advisor dc:contributor.advisor

• Nicholas Roy.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about 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/1721.1/66012

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/66012