Abstract

Current autonomous systems, comprising of intelligent machines, devices, and software that are aware of and interact with their environment, have the potential to accomplish a previously intractable scope of tasks. Their ever growing capabilities are already enabling them operate in the real- world, making independent decisions and even learning in uncertain, unstructured, and unpredictable environments. These new systems will soon be able to replace humans in hazardous environments and tedious jobs, provide them with up-to-the-minute situational awareness, assist them in difficult or repetitive tasks, and enhance their capabilities. For autonomous systems to show their full potential, new methods are necessary that will allow them to seamlessly infer, reason, and act in the real-world using large amounts of data, learn from their experiences and improve their performance, accept naturally expressed instruction from humans, adapt and respond to unpredictable situations, and effectively interact with each other and humans to accomplish collaborative tasks.

Bio

Michael M. Zavlanos received the Diploma in mechanical engineering from the National Technical University of Athens, Greece, in 2002, and the M.S.E. and Ph.D. degrees in electrical and systems engineering from the University of Pennsylvania, Philadelphia, PA, in 2005 and 2008, respectively. He is currently the Yoh Family Associate Professor in the Department of Mechanical Engineering and Materials Science at Duke University, Durham, NC. He also holds a secondary appointment in the Department of Electrical and Computer Engineering and the Department of Computer Science. His research focuses on control theory, optimization, learning, and AI and, in particular, autonomous systems and robotics, networked and distributed control systems, and cyber-physical systems. Dr. Zavlanos is a recipient of various awards including the 2014 ONR YIP Award and the 2011 NSF CAREER Award.