Autonomous Systems in the Intersection of Control, Learning, and Formal Methods
Speaker: Ufuk Topcu | Professor in the Department of Aerospace Engineering & Engineering Mechanics and Director of the Autonomous Systems Group, University of Texas, Austin
Date Presented: February 19, 2025 | Klaus Computing Building
Overview: Autonomous systems are emerging as a driving technology for countlessly many applications. Numerous disciplines tackle the challenges toward making these systems trustworthy, adaptable, user-friendly, and economical. On the other hand, the existing disciplinary boundaries delay and possibly even obstruct progress. I argue that the nonconventional problems that arise in designing and verifying autonomous systems require hybrid solutions at the intersection of control, learning, and formal methods (among other disciplines). I will present examples of such hybrid solutions in the context of learning in sequential decision-making processes. These results offer novel means for effectively integrating physics-based, contextual, or structural prior knowledge into data-driven learning algorithms. They improve data efficiency by several orders of magnitude and generalizability to environments and tasks the system had not previously experienced. I will conclude with remarks on a few promising future research directions.
Speaker: Ufuk Topcu | Professor in the Department of Aerospace Engineering & Engineering Mechanics and Director of the Autonomous Systems Group, University of Texas, Austin
Date Presented: February 19, 2025 | Klaus Computing Building
Overview: Autonomous systems are emerging as a driving technology for countlessly many applications. Numerous disciplines tackle the challenges toward making these systems trustworthy, adaptable, user-friendly, and economical. On the other hand, the existing disciplinary boundaries delay and possibly even obstruct progress. I argue that the nonconventional problems that arise in designing and verifying autonomous systems require hybrid solutions at the intersection of control, learning, and formal methods (among other disciplines). I will present examples of such hybrid solutions in the context of learning in sequential decision-making processes. These results offer novel means for effectively integrating physics-based, contextual, or structural prior knowledge into data-driven learning algorithms. They improve data efficiency by several orders of magnitude and generalizability to environments and tasks the system had not previously experienced. I will conclude with remarks on a few promising future research directions.
