Human interaction with autonomous cyber-physical systems is becoming ubiquitous in consumer products, transportation systems, manufacturing, and many other domains. This project seeks constructive methods to answer the question: How can we design cyber-physical systems to be responsive and personalized, yet also provide high-confidence assurances of reliability? Cyber-physical systems that adapt to the human, and account for the human's ongoing adaptation to the system, could have enormous impact in everyday life as well as in specialized domains (biomedical devices and systems, transportation systems, manufacturing, military applications), by significantly reducing training time, increasing the breadth of the human's experiences with the system prior to operation in a safety-critical environment, improving safety, and improving both human and system performance. Architectures that support dynamic interactions, enabled by advances in computation, communication, and control, can leverage strengths of the human and the automation to achieve new levels of performance and safety.
This research investigates a human-centric architecture for "cognitive autonomy" that couples human psychophysiological and behavioral measures with objective measures of performance. The architecture has four elements: 1) a computable cognitive model which is amenable to control, yet highly customizable, responsive to the human, and context dependent; 2) a predictive monitor, which provides a priori probabilistic verification as well as real-time short-term predictions to anticipate problematic behaviors and trigger the appropriate action; 3) cognitive control, which collaboratively assures both desired safety properties and human performance metrics; and 4) transparent communication, which helps maintain trust and situational awareness through explanatory reasoning. The education and outreach plan focuses on broadening participation of underrepresented minorities through a culturally responsive undergraduate summer research program, which will also provide insights about learning environments that support participation and retention. All research and educational material generated by the project are being made available to the public through the project webpage.
Meeko Oishi received the Ph.D. (2004) and M.S. (2000) in Mechanical Engineering from Stanford University (Ph.D. minor, Electrical Engineering), and a B.S.E. in Mechanical Engineering from Princeton University (1998). She is a Professor of Electrical and Computer Engineering at the University of New Mexico. Her research interests include human-centric control, stochastic optimal control, and autonomous systems. She previously held a faculty position at the University of British Columbia at Vancouver, and postdoctoral positions at Sandia National Laboratories and at the National Ecological Observatory Network. She was a Visiting Researcher at AFRL Space Vehicles Directorate, and a Science and Technology Policy Fellow at The National Academies. She is the recipient of the NSF CAREER Award and a member of the 2021-2023 DoD Defense Science Study Group.