Robot Autonomy through the Crowd
Recent innovations in crowd computing, crowdsourcing and remote access technologies have altered the way in which many traditional artificial intelligence and robotics studies are designed, conducted and evaluated. Research on shared autonomy, human-robot interaction and robot learning has particularly benefited from the greater access to data and users that such techniques enable, leading to new data-driven techniques and more extensive evaluations. In this talk, I will present ongoing projects aimed at enabling robots to learn from everyday people, examining how access to thousands of potential robot users can be leveraged to overcome several challenges common in robotics research.
Bio: 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. Her research is focused on interactive machine learning, adjustable autonomy, crowdsourcing and human-robot interaction, and her research is supported in part by NSF, ONR and DARPA awards.