MIT Robotics Seminar: Stephanie Gil

"Resilient Multi-Robot Systems: Coordination and Learning in the Real-World"

Multi-robot systems are increasingly integrated into real-world applications, from autonomous vehicle fleets to search-and-rescue teams. Ensuring their coordination algorithms remain robust against unreliable communication, security threats, and corrupted data is essential. This talk explores how control and information exchange can enhance situational awareness and security in multi-robot networks. For example, consensus problems are at the core of many multi-agent coordination tasks -- a key challenge is that classical results fail when malicious agents exceed half of the network connectivity. This quickly leads to limitations in the practicality of many multi-robot coordination tasks. However, with the growing prevalence of cyber-physical systems comes novel opportunities for detecting attacks by using cross-validation with physical channels of information. We introduce the concept of stochastic observations of trust, where an agent’s trustworthiness is modeled probabilistically. Under this framework, consensus can be restored even when the number of malicious agents surpasses classical limits. We will present both theoretical insights and experimental results to this end. Complementing these results, we will briefly discuss reinforcement learning approaches that fuse real-time sensor data with learned policies for sequential decision-making in dynamic, partially observable environments. Case studies include autonomous delivery routing and real-time rendezvous with sperm whales in Dominica. Together, these advances move toward multi-robot systems that are both secure and capable for the real-world.