Carnegie Mellon University

Advanced Search   
  Look in
       Title    Full-text
  Date Range

RI Seminar: Andrea Thomaz
Designing Learning Interactions for Robots

Andrea Thomaz
Georgia Tech

February 22, 2013, 3:30 to 4:30, NSH 1305

Play Video

In this talk I present recent work from the Socially Intelligent Machines Lab at Georgia Tech. One of the focuses of our lab is on Socially Guided Machine Learning, building robot systems that can learn from everyday human teachers. We look at standard Machine Learning interactions and redesign interfaces and algorithms to support the collection of learning input from naive humans. This talk starts with an initial investigation comparing self and social learning which motivates our recent work on Active Learning for robots. Then, I will present results from a study of robot active learning, which motivates two challenges: getting interaction timing right, and asking good questions. To address the first challenge we are building computational models of reciprocal social interactions. And to address the second challenge we are developing algorithms for generating Active Learning queries in embodied learning tasks.

Additional Information

Host: Aaron Steinfeld

Appointments: Stephanie Matvey

Speaker Biography

Andrea L. Thomaz is an Assistant Professor of Interactive Computing at the Georgia Institute of Technology. She directs the Socially Intelligent Machines lab, which is affiliated with the Robotics and Intelligent Machines (RIM) Center and with the Graphics Visualization and Usability (GVU) Center. She earned a B.S. 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 is published 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 has been featured in the front page of the New York Times, and in 2009 she was named one of MIT Technology Review’s TR 35.