Performance Metrics for AI in Manufacturing HRI
Recent years have witnessed the birth of a new era in industrial robotics in which collaborative systems, designed to work safety beside the human workforce, are integrated into historically manual processes. Such technologies represent a relatively low-risk gateway solution to transitioning facilities and operations to a state of partial automation, but retain many of the characteristics of their non-collaborative predecessors. Specifically, collaborative robots largely remain difficult to program, integrate, and maintain. Although the skills of the labor force are expected to increase in the coming years, the collaborative capabilities of next-generation industrial robots must evolve to bridge the technology gap, leading to more effective human-robot teaming in manufacturing application. It is expected advances in artificial intelligence will play a significant role in aiding this transition, but prior to adoption such technologies must be validated and hardened for the industrial environment. In this talk, Dr. Jeremy Marvel from the U.S. National Institute of Standards and Technology (NIST) will discuss work in assessing and assuring artificial intelligence technology for human-robot interaction through measurement science. Dr. Marvel will present an overview of the current technology landscape, and provide some initial metrology results from NIST’s ongoing Performance of Collaborative Robot Systems project. Focal topics include artificial intelligence for collaborative robot safety, robotic system integration, and situation awareness for human-robot teaming.
Jeremy A. Marvel is a research scientist and project leader at the U.S. National Institute of Standards and Technology (NIST), a non-regulatory branch of the U.S. Department of Commerce. Prior to NIST, Dr. Marvel was a research scientist at the Institute for Research in Engineering and Applied Physics at the University of Maryland, College Park, MD. He joined the Intelligent Systems Division at NIST in 2012, and has over thirteen years of robotics research experience in both industry and government. His research expertise include intelligent and adaptive solutions for robot applications, with particular attention paid to human-robot and robot-robot collaborations, multirobot coordination, industrial robot safety, machine learning, perception, and automated parameter optimization. Dr. Marvel currently leads a team of scientists and engineers in metrology efforts at NIST toward collaborative robot performance, and developing tools to enable small and medium-sized enterprises to effectively deploy robot solutions. Dr. Marvel received the Bachelor’s degree in computer science from Boston University in 2003, the Master’s degree in computer science from Brandeis University in 2005, and the Ph.D. degree in computer engineering from Case Western Reserve University in 2010.