Title:
Autonomous Systems in the Intersection of Control, Learning, and Formal Methods
Autonomous Systems in the Intersection of Control, Learning, and Formal Methods
Author(s)
Topcu, Ufuk
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Abstract
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. He argues 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). He presents 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. He concludes with remarks on a few promising future research directions.
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Date Issued
2025-02-19
Extent
63:25 minutes
Resource Type
Moving Image
Resource Subtype
Lecture
Rights Statement
Unless otherwise noted, all materials are protected under U.S. Copyright Law and all rights are reserved