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Spring 2014 GRASP Seminar: Stefanie Tellex, Brown University, "Natural Language And Robotics"
Presenter: Stefanie Tellex (Homepage)
Friday March 28, 2014 from 11:00am to 12:00pm
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.
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 seemlessly 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 understanding.