Abstract: The ability of a system to correctly respond to a sudden adverse event is critical for high-level autonomy in complex, changing, or remote environments. By assuming continuing structural knowledge about the system, classical methods of adaptive or robust control largely attempt to design control laws which enable the system to complete its original task even after an adverse event. However, catastrophic events such as physical system damage may simply render the original task impossible to complete. In that case, design of any control law that attempts to complete the task is doomed to be unsuccessful. Instead, an autonomous planner should recognize the task as impossible to complete, propose an alternative that can certifiably be completed given the current knowledge, and formulate a control law that drives the system to complete this new task. To do so, in this talk I will present the emergent twin frameworks of quantitative resilience and guaranteed reachability. Combining methods of optimal control, online learning, and reachability analysis, these frameworks first compute a set of temporal tasks completable under all system dynamics consistent with the planner’s partial knowledge. These tasks can then be pursued by online learning and adaptation methods. The talk will consider three scenarios: actuator degradation, loss of control authority, and structural change in system dynamics, and will briefly present several applications to aerial, space, and maritime vehicles, as well as infrastructure design. Finally, I will identify promising future directions of research, including real-time safety-assured mission planning, resilience of networks, and perception-based task assignment.
Bio: Melkior Ornik is an assistant professor in the Department of Aerospace Engineering at the University of Illinois Urbana-Champaign, also affiliated with the Coordinated Science Laboratory, as well as the Discovery Partners Institute in Chicago. He received his Ph.D. degree from the University of Toronto in 2017. His research focuses on developing theory and algorithms for control, learning and task planning in autonomous systems that operate in uncertain, changing, or adversarial environments, as well as in scenarios where only limited knowledge of the system is available. His recent work has been funded by several NASA grants and Department of Defense programs.