Reachability Analysis

Reachability analysis is a computational technique used to determine the set of all possible future states a system can reach, given its initial state and dynamics. Current research focuses on extending reachability analysis to increasingly complex systems, including those with neural network controllers, stochasticity, and high-dimensional state spaces, employing methods like deep reinforcement learning, Hamilton-Jacobi reachability, and interval analysis. These advancements are crucial for verifying the safety and robustness of autonomous systems in various domains, such as robotics, autonomous driving, and aerospace, providing guarantees that are essential for deploying these systems in safety-critical applications. The development of efficient and scalable algorithms remains a key challenge, particularly for systems with nonlinear dynamics or uncertainties.