Robot Learning from Human Teachers: (Synthesis Lectures on Artificial Intelligence and Machine Le)

Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain.

About the Author :
Sonia Chernova is an Assistant Professor of Computer Science and Robotics Engineering at Worcester Polytechnic Institute and the director of the Robot Autonomy and Interactive Learning (RAIL) lab. She earned B.S. and Ph.D. degrees in Computer Science from Carnegie Mellon University in 2003 and 2009, and was a Postdoctoral Associate at the MIT Media Lab prior to joining WPI. Dr. Chernova's research is focused on interactive machine learning, adjustable autonomy, crowdsourcing, and human-robot interaction. She has received funding support from NSF, ONR, and DARPA, including an NSF CAREER award on Learning from Demonstration in 2012. Andrea L. Thomaz is an Associate 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 has published in the areas of Artificial Intelligence, Robotics, and Human- Robot Interaction. She has received recognition as a young leader in her field, receiving an ONR Young Investigator Award in 2008, and an NSF CAREER award in 2010. Her work has been featured on the front page of The New York Times, on NOVA Science Now, she was named one of MIT Technology Review's Top 35 under 35 in 2009, and on Popular Science Magazine's Brilliant 10 list in 2012.