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Presented By: Aerospace Engineering

Chair's Distinguished Lecture: Algorithmic Foundations of Resilient Collaborative Autonomy: From Robust Combinatorial Optimization to Perception

Vasileios Tzoumas, Research scientist, Massachusetts Institute of Technology

Tzoumas Tzoumas
Vasileios Tzoumas
Research scientist
Massachusetts Institute of Technology

Collaborative autonomous vehicles hold the promise to revolutionize transportation, disaster response, and space exploration. Already, micro-aerial vehicles with on-board cameras have become a multi-billion-dollar industry; and as we enter the new decade, teams of semi-autonomous flying cars, jet fighters, and space-exploration vehicles are being launched. An era of ubiquitous aerospace vehicles is becoming a reality, and along with it autonomous vehicles that can form teams, agree on navigation plans, and perceive the world. However, this future is threatened by denial-of-service (DoS) and deceptive attacks and failures that can compromise the vehicles’ teams, navigation plans, and perception capabilities. These threats lie outside the reach of cybersecurity, and of estimation and control against malicious data. Instead, algorithms at the intersection of perception, planning, and non-convex optimization are needed. I will present two algorithms from my research, and my vision for a resilient collaborative autonomy.

First, I will discuss the first provably optimal algorithms for robust combinatorial optimization against any numbers of DoS attacks. The algorithms can robustify for the first time teams and their navigation plans against DoS attacks. I will demonstrate this via search and rescue, and surveillance experiments. Second, I will present algorithms that robustify visual perception capabilities against deceptive failures (outliers). The algorithms achieve extreme outlier-robustness in near real-time for the first time. I will illustrate this across various perception problems, on datasets for localization and mapping (SLAM), object recognition, and 3D-reconstruction. I will conclude with my vision for a collaborative autonomy that is not only robust but also resilient: I will argue the need for a technological convergence between (i) “cyber” capabilities for a distributed artificial intelligence, driven by adaptive learning and data-driven perception and navigation algorithms, and (ii) “physical” capabilities of morphable structures, self-healing materials, and smart devices.

About the speaker...

Vasileios Tzoumas is a research scientist at the Department of Aeronautics and Astronautics (AeroAstro), and the Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology (MIT). Before that, he was a post-doctoral associate at AeroAstro and LIDS for a bit over a year. He received his Ph.D. in 2018 at the Department of Electrical and Systems Engineering, University of Pennsylvania (UPenn). In 2017, he was a visiting Ph.D. student at the Institute for Data, Systems, and Society, MIT. He holds a Diploma in Electrical and Computer Engineering from the National Technical University of Athens (2012); a Master of Science in Electrical Engineering from UPenn (2016); and a Master of Arts in Statistics from the Wharton School of Business at UPenn (2016). He aims to enable autonomous, collaborative cyber-physical systems that are resilient against denial-of-service and deceptive attacks and failures. His theoretical focus is at the interplay of perception, control, communication, and computing. His application and experimental focus include multi-robot tasks of autonomous (visual) navigation, information gathering, and surveillance. Vasileios builds on fundamental tools of control theory, robotic perception, computational complexity, and combinatorial and non-convex optimization. He was a Best Student Paper Award finalist at the 2017 IEEE Conference in Decision and Control (CDC).
Tzoumas Tzoumas

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