Event Location: NSH 1305
Bio: Julie Shah is an Assistant Professor in the Department of Aeronautics & Astronautics and leads the Interactive Robotics Group of the Computer Science & Artificial Intelligence Laboratory. Shah received her SB (2004) and SM (2006)from the Department of Aeronautics and Astronautics at MIT, and her PhD (2010) in Autonomous Systems from MIT. Before joining the faculty, she worked at Boeing Research and Technology on robotics applications for aerospace manufacturing. She has developed innovative methods for enabling fluid human-robot teamwork in time-critical, safety-critical domains, ranging from manufacturing to surgery to space exploration. Her group draws on expertise in artificial intelligence, human factors, and systems engineering to develop interactive robots that emulate the qualities of effective human team members to improve the efficiency of human-robot teamwork. This work was recognized by the Technology Review as one of the 10 Breakthrough Technologies of 2013, and has received international recognition in the form of best paper awards and nominations from the International Conference on Automated Planning and Scheduling, the American Institute of Aeronautics and Astronautics, the IEEE/ACM International Conference on Human-Robot Interaction, and the International Symposium on Robotics.

Abstract: Recent advances in computation, sensing, and hardware enable robotics to perform an increasing percentage of traditionally manual tasks in manufacturing. Yet, often the assembly mechanic cannot be removed entirely from the process. This provides new economic motivation to explore opportunities where assembly mechanics and industrial robots may work in close physical collaboration. In this talk, I present adaptive work-sharing and scheduling algorithms to collaborate with industrial robots on two levels: one-to-one human robot teamwork, and factory-level sequencing and scheduling of human and robotic tasks. I discuss our recent work developing adaptive control methods that incorporate high-level, person-specific planning and execution mechanisms to promote predictable, convergent team behavior. We apply human factors modeling coupled with statistical methods for planning and control to derive quantitative methods for assessing the quality and convergence of learnt teaming models, and to perform risk-sensitive robot control on the production line. I also discuss computationally efficient methods for coordinating human and robotic sequencing and scheduling at the factory-level. Tight integration of human workers and robotic resources involves complex dependencies. Even relatively small increases in process time variability lead to schedule inefficiencies and performance degradation. Our methods allow fast, dynamic computation of robot tasking and scheduling to respond to people working and coordinating in shared physical space, and provide real-time guarantees that schedule deadlines and other operational constraints will be met.