Correctness and Control for Human-Cyber-Physical Systems

Cyber-physical systems deployed in societal-scale applications almost always interact with humans, e.g. semi-autonomous vehicles interacting with drivers in the car or on the road, semi-autonomous aerial vehicles interacting with human operators, or medical robots interacting with doctors. Due to the safety-critical nature of these human-cyber-physical systems (h-CPS), we, as designers, need to be able to provide guarantees about their safety and performance. My work focuses on creating a new formal design methodology for control and verification of h-CPS closely interfacing with data-driven models in order to ensure provable guarantees.

Today, I will first talk about a formal framework, where we synthesize controllers that satisfy desired logic specifications under reactive and uncertain environments. For instance, in semiautonomous driving, we design self-aware autonomous controllers that are prescient, and can detect infeasiblities in the control optimization in advance. To achieve the goal of controller synthesis, accurate models of humans and awareness of the role of human as part of the environment, system or the control loop is the key. I will then demonstrate our work in learning models of human-driven vehicles. We design autonomous controllers that are aware of models of drivers, and their effects on each other. We then plan accordingly to leverage these effects for better safety and efficiency.

Dorsa Sadigh is a Ph.D. candidate in the Electrical Engineering and Computer Sciences department at UC Berkeley. Her research interests lie in the intersection of control theory, formal methods and human-robot interactions. Specifically, she works on developing provable guarantees for human-cyber-physical systems such as semiautonomous driving. Dorsa received her B.S. from Berkeley EECS in 2012. She was awarded the NDSEG and NSF graduate research fellowships in 2013. She was the recipient of 2016 Leon O. Chua award and 2011 Arthur M. Hopkin award for achievement in the field of nonlinear science, and she received the Google Anita Borg Scholarship in 2016.