Welcome to the Reliable Autonomous Systems Lab at MIT (REALM)! We design, analyze, and verify safe control systems. Our work lies at the intersection of control theory, machine learning, and formal methods, with a particular focus on safety in systems with nonlinear, high-dimensional, and difficult-to-model dynamics.


Some highlights from recent projects...

To enable safe control of up to 1024 distinct agents, each with nonlinear dynamcis, we learn a barrier-function-based neural controller to ensure scalable collision-free navigation. This image shows 1024 nonlinear quadrotors executing a maneuver using this controller.

To solve reach-avoid control problems for complex systems with nonlinear dynamics, we develop robust Lyapunov/Barrier functions that certify the safety of a learned controller. This image shows a certificate for collision avoidance, which learns the outlines of obstacles as super-level sets (blue)