Learning Semantic Maps from Natural Language Descriptions

Education - Colloquium

Friday, April 25, 2014
2:00 PM-3:00 PM

Higgins Laboratories
102

Abstract: Whether they are providing personalized care, assisting people with
cognitive or physical impairments, or carrying out household chores,
robots have the potential to improve our quality of life in
revolutionary ways. In order to realize this potential, we must
develop robots that people can efficiently command and naturally
interact with. This interaction demands that robots be able to reason
over models of their environments as rich as those of their human
partners. However, today's robots understand their environment through
representations that are either limited to low-level metric properties
or that require domain experts to hard-code higher-level semantic
knowledge.

In this talk, I will describe an algorithm that I developed to enable
robots to efficiently learn shared cognitive models of their
surroundings from a user's natural language descriptions. The novelty
lies in inferring spatial and semantic knowledge from these
descriptions and fusing this information with the metric measurements
from the robot's sensor streams. The method maintains a joint
distribution over a hybrid metric, topological, and semantic
representation of the environment, which provides a common framework
in which to integrate these disparate sources of information. I will
demonstrate that the algorithm allows people to share meaningful,
human-centric properties of their environment simply by speaking to
the robot. I will conclude by describing ongoing efforts in
human-robot dialog and planning that build upon this semantic mapping
algorithm to enable a voice-commandable wheelchair and other robots to
follow free-form spoken instructions.

Bio: Matthew Walter is a research scientist in the Computer Science and
Artificial Intelligent Laboratory at the Massachusetts Institute of
Technology. His research focuses on probabilistic approaches to
perception and natural language understanding that make it possible
for robots to work effectively alongside humans. Matthew received his
Ph.D. in Mechanical Engineering from the Massachusetts Institute of
Technology and the Woods Hole Oceanographic Institution.

Suggested Audiences: College, Adult

E-mail: rbe@wpi.edu

Last Modified: April 15, 2014 at 10:03 AM

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