Abstract

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 covers results on building computational models of reciprocal social interactions, high-level task goal learning, low-level skill learning, and active learning interactions using several humanoid robot platforms.  

Bio

Andrea L. Thomaz is an associate professor in the School of Interactive Computing at Georgia Tech. She joined the faculty in 2007. Thomaz 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. She has published in the areas of Artificial Intelligence, Robotics, and Human-Robot Interaction. Currently, she directs the Socially Intelligent Machines lab, which is affiliated with the Institute for Robotics and Intelligent Machines (IRIM) and the GVU Center. Her research aims to computationally model mechanisms of human social learning in order to build social robots and other machines that are intuitive for everyday people to teach.