AUTOR:     Stefanie Tellex
AFILIACJA: MIT i Brown University
TYTUŁ:     Natural Language and Robotics


Natural language can be a powerful, flexible way for people to
interact with robots.  A particular challenge for designers of
embodied robots, in contrast to disembodied methods such as
phone-based information systems, is that natural language
understanding systems must map between linguistic elements and aspects
of the external world, thereby solving the so-called "grounded
language" problem.  This talk describes a probabilistic framework for
robust interpretation of grounded natural language, called Generalized
Grounding Graphs (G^3).  The G^3 framework leverages the structure of
language to define a probabilistic graphical model that maps between
elements in the language and aspects of the external world.  It can
compose learned word meanings to understand novel commands that may
have never been seen during training.  Taking a probabilistic approach
enables the robot to employ information theoretic dialog strategies,
asking targeted questions to reduce uncertainty about different parts
of a natural language command.  This approach points the way toward
more general models of grounded language understanding, which will
lead to robots capable of building world models from both linguistic
and non-linguistic input, following complex grounded natural language
commands, and engaging in fluid, flexible dialog with their human


Stefanie Tellex is a Research Scientist at the MIT Computer Science
and Artificial Intelligence Laboratory at MIT and an adjunct assistant
professor in the Brown University Department of Computer Science
(starting full-time in fall of 2013).  She completed her Ph.D. at the
MIT Media Lab in 2010, where she developed models for the meanings of
spatial prepositions and motion verbs.  She has published at SIGIR,
HRI, AAAI, IROS, and ICMI, winning Best Student Paper at SIGIR and
ICMI.  Her research interests include probabilistic graphical models,
human-robot interaction, and grounded language understanding.