RI seminar: Designing learning interactions for robots
Andrea Thomaz
Georgia Tech

February 22, 2013

Abstract
In this talk, I present recent work from Georgia Tech's Socially Intelligent Machines Lab. One of our lab's focuses is on socially supervised machine learning, which involves building robotic systems that can learn from regular human teachers. We look at standard Machine Learning interactions and redesign interfaces and algorithms to support collecting learning input from naive humans. This talk begins with initial research comparing machine learning and social learning, which informs our recent work on active learning for robots. Next, I will present the results of a study on active learning by robots, which poses two challenges: the right timing of the interaction, and asking good questions. To address the first challenge, we build computational models of reciprocal social interactions. And to address the second challenge, we develop algorithms for generating Active Learning queries in embodied learning tasks.

Speaker Biography
Andrea L. Thomaz is an Assistant Professor of Interactive Computing at the Georgia Institute of Technology. She heads the Socially Intelligent Machines lab, which is affiliated with the Robotics and Intelligent Machines (RIM) Center and the Graphics Visualization and Usability (GVU) Center. She received a BS in Electrical and Computer Engineering from the University of Texas at Austin in 1999, and Sc.M. and Ph.D. degrees from MIT in 2002 and 2006. Dr. Thomaz publishes in the areas of artificial intelligence, robotics, human-robot interaction and human-computer interaction. She received an ONR Young Investigator Award in 2008 and an NSF CAREER award in 2010. Her work was featured on the front page of the New York Times and in 2009 she was named one of MIT Technology Review's TR 35.

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