As autonomous systems expand their deployment regions into unstructured, open-world environments, they face potential hazardous Out-of-Distribution (OOD) failure scenarios that differ from their training data. Current methods rely on handcrafted intervention policies, limiting their ability to plan generalizable, safe motions. FORTRESS introduces a novel framework that generates and reasons about semantically safe fallback strategies in real time to prevent OOD failures by bridging open-world, multi-modal reasoning with dynamics-aware planning.
A journal publication surveying the recent literature on scalable Hamilton-Jacobi reachability estimation in reinforcement learning to provide a foundational basis for research into reliability in high-dimensional systems. We review how this technique has been employed to solve challenging tasks like those with dynamic obstacles and lidar-based or RGB image-based observations.
Hamilton-Jacobi reachability estimation for model-free safe RL in deterministic and stochastic environments with safety guarantees and convergence analysis. Tasks include lidar-based observations, dynamic obstacles, and multiple hard and soft constraints.
Learning Lyapunov-like models offline from observation-only, expert data to solve stabilization control tasks online. Deployed in hardware for robustness testing.