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Spring 2014 GRASP Seminar: Stefanie Tellex, Brown University, “Natural Language and Robotics”

March 28, 2014 @ 11:00 am - 12:00 pm

Abstract: 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 symbol grounding 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.  By inverting the model, the robot can generated targeted natural language requests for help from a human
partner.  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 partners.


- Learn More

Stefanie Tellex is an Assistant Professor of Computer Science and Assistant Professor of Engineering at Brown University. Her group,
the Humans To Robots Lab, creates robots that seamlessly collaborate with people to meet their needs using language, gesture, and
probabilistic inference. 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. Her postdoctoral work at MIT CSAIL
focused on creating robots that understand natural language. She has
published at SIGIR, HRI, RSS, AAAI, IROS, and ICMI, winning Best
Student Paper at SIGIR and ICMI. She was named one of IEEE Spectrum’s
AI’s 10 to Watch and won the Richard B. Salomon Faculty Research Award at Brown University. Her research interests include probabilistic
graphical models, human-robot interaction, and grounded language


March 28, 2014
11:00 am - 12:00 pm
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