Collaborative Research: SLES: Improving Safety by Synthesizing Interacting Model-based and Model-free Learning Approaches

Abstract

Learning-enabled systems have been rapidly increasing in size, acquiring new capabilities. These systems are typically deployed in complex operating environments, so their safety is extremely important. Ensuring safety requires that systems are robust to extreme events while we can monitor them for anomalous and unsafe behavior. While traditional machine learning systems are evaluated pointwise with respect to a fixed test set, such static coverage provides only limited assurance when exposed to unprecedented conditions in complex operating environments. One key question that still remains unanswered is: How can we design and deploy learning-enabled systems that can be robust to extreme events while monitoring them for anomalous and unsafe behavior by synthesizing model-free and model-based techniques?<br/><br/>The overarching goal of the proposed research is to establish a framework that leads to the design and implementation of learning-enabled systems in which safety is ensured with high levels of confidence. The framework will leverage tools from control theory, multi-agent autonomy, and formal methods for rigorously probabilistic reasoning to yield safe learning-enabled systems. The expected outcome of this project will yield safe model-free, mode-based, and interacting model-free and model-based learning-enabled systems with sound design principles that practitioners could leverage to achieve safety specifications. The proposed research could effectively facilitate safe learning-enabled systems even within complex environments while monitoring them for anomalous and unsafe behavior. It will yield learning-enabled systems that could be deployed in complex operating environments while ensuring that the systems will be robust to extreme events and monitoring them for anomalous and unsafe behavior. The fundamental knowledge created in the proposed research will be the basis upon which future-safe autonomous systems with embodied intelligence can be built.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Kyriakos G Vamvoudakis

Kyriakos G. Vamvoudakis was born in Athens, Greece. He earned his Diploma in Electronic and Computer Engineering (equivalent to a Master of Science) from the Technical University of Crete, Greece, in 2006, graduating with highest honors. After relocating to the United States, he pursued further studies at The University of Texas at Arlington under the guidance of Frank L. Lewis, obtaining his M.S. and Ph.D. in Electrical Engineering in 2008 and 2011, respectively. From May 2011 to January 2012, he served as an Adjunct Professor and Faculty Research Associate at the University of Texas at Arlington and the Automation and Robotics Research Institute. Between 2012 and 2016, he was a project research scientist at the Center for Control, Dynamical Systems, and Computation at the University of California, Santa Barbara. He then joined the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech as an assistant professor, a position he held until 2018.

He currently serves as the Dutton-Ducoffe Endowed Professor at The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. He holds a secondary appointment in the School of Electrical and Computer Engineering. His expertise is in reinforcement learning, control theory, game theory, cyber-physical security, bounded rationality, and safe/assured autonomy.

He has received numerous prestigious awards, including the 2019 ARO YIP Award, the 2018 NSF CAREER Award, the 2018 DoD Minerva Research Initiative Award, and the 2021 GT Chapter Sigma Xi Young Faculty Award. His work has also been recognized with several best paper nominations and international awards, such as the 2016 International Neural Network Society Young Investigator (INNS) Award, the Best Paper Award for Autonomous/Unmanned Vehicles at the 27th Army Science Conference in 2010, the Best Presentation Award at the World Congress of Computational Intelligence in 2010, and the Best Researcher Award from the Automation and Robotics Research Institute in 2011. Dr. Vamvoudakis has served on various international program committees and has organized special sessions, workshops, and tutorials for several international conferences. He is the Editor-in-Chief of Aerospace Science and Technology and currently serves on the IEEE Control Systems Society Conference Editorial Board. Additionally, he is an Associate Editor for several journals, including Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Artificial Intelligence, Neural Networks, and the Journal of Optimization Theory and Applications. He is also a Senior Guest Editor for the IEEE Open Journal of Control Systems for the special issue on the intersection of machine learning with control. Previously, Dr. Vamvoudakis has served as a Guest Editor for various special issues, including those in IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Industrial Informatics, and IEEE Transactions on Intelligent Transportation Systems. He is a registered Professional Engineer (PE) in Electrical/Computer Engineering, a member of the Technical Chamber of Greece, an Associate Fellow of AIAA, and a Senior Member of IEEE.

Performance Period: 10/01/2024 - 09/30/2027

Award Number: 2415479