Learning, Layering, and Adaptation for Safety-Critical Control of Autonomous Systems
The autonomy stacks of modern engineering systems, from self-driving vehicles, to unmanned aerial drones, to humanoid robots, typically consist of a complex layering of control, planning, and perception feedback loops. These layered architectures have demonstrated practical success in enabling autonomous behaviors for a variety of robotic systems but present new challenges for certifying their behaviors, particularly when such architectures use learning-enabled components. In this talk, I will present my work on safety-critical layered control architectures (LCAs) for autonomous systems. I will first discuss the theoretical foundations of safety in the context of control systems and LCAs, followed by practical applications of this developed theory. Next, I will discuss how adaptive and learning-based approaches address some common pitfalls of LCAs. Finally, I will present ongoing work from my group at the intersection of learning, perception, and control in the context of LCAs.
Max Cohen
Assistant Professor, NC State University on August 21, 2026 at 10:15 AM in EB2 1231
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Max Cohen is an Assistant Professor in the Department of Electrical and Computer Engineering at NC State, where he directs the Autonomy, Controls, and Robotics Engineering (ACRE) Lab. He earned his B.S. from the University of Florida in 2018, his M.S. from Boston University in 2022, and his Ph.D. from Boston University in 2023. Prior to joining NC State, he served as a postdoctoral scholar at Caltech from 2023 to 2025. His awards include an NSF Graduate Research Fellowship, an Outstanding Dissertation award from Boston University, and the best paper award at the 2025 Conference on Learning for Dynamics and Control. His research interests lie at the intersection of control theory, robotics, and autonomous systems.
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