Resilient Decision-Making for Multi-Robot Systems in the Presence of Adversaries
| This talk presents a unified perspective on resilient decision-making for multi-robot systems operating in environments where adversaries may influence routing, search, and detection tasks. Over the past decade, significant progress has been made in multi-robot systems research, particularly in coordination, control, and navigation, largely fueled by the rapid commercialization of unmanned aerial systems and drones. Yet, deploying these systems in real-world environments remains difficult. Most research assumes cooperative or optimally performing agents, neglecting cases where agents behave adversarially or suboptimally due to uncertainty, faults, or environmental disturbances. Resiliency to malicious or malfunctioning agents remains a key limitation, as such agents can degrade system efficiency and destabilize routing policies, where stability is typically defined as bounded cost over time. Existing stability guarantees for cooperative fleets collapse when agents deviate from plans, underscoring the need for adversarially aware planning and routing theory, particularly in safety-critical aerospace applications.
In my work, I establish theoretical foundations for coordination, control, and learning under uncertainty and adversarial influence, focusing on routing and traffic management for fleets of aerial and ground agents, with Urban Air Mobility as a compelling application. I develop adaptive algorithms that ensure provable stability, resilience, and safety. I will present new results on resilient multi-agent policies that operate effectively despite adversaries in the decision loop, including routing under adversarial influence from agents that manipulate data while remaining within the system. Finally, I will show how resilient cooperative search strategies address challenges posed by intelligent, coordinated external adversaries, providing provable guarantees for coverage and detection in settings such as search and rescue and pursuit-evasion. I conclude by outlining future research directions at the intersection of safety, learning, and large-scale autonomy. Roee M. Francos is currently a Computer Science Postdoctoral Fellow at the Robotics, Embedded Autonomy, and Communication Theory (REACT) Lab at Harvard University focusing on development of multi-agent resilient decision-making and coordination algorithms. In 2023, he completed his PhD in Computer Science at the Multi-Agent Robotic Systems Laboratory, the Technion-Israel Institute of Technology. He received the B.Sc. in Electrical and Computer Engineering from Ben-Gurion University. His research interests are in multi-agent teamwork, autonomous robotics, intelligent transportation systems, bio-inspired robotics and computer vision, focusing on collaborative algorithms for motion planning of autonomous vehicles, multi-robot learning , and air traffic management and coordination of unmanned vehicles. Roee is a recipient of the 2023 Robotics Science and Systems (RSS) Pioneers Award and the 2025 IEEE Multi-Robot & Multi-Agent Systems (MRS) Young Pioneer Award.
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