Data-driven Reachable Set Estimation with Tunable Adversarial and Wasserstein Distributional Guarantees

ArXi:2604.12654v1 Announce Type: cross We study finite horizon reachable set estimation for unknown discrete-time dynamical systems using only sampled state trajectories. Rather than treating scenario optimization as a black-box tool, we show how it can be tailored to reachable set estimation, where one must learn a family of sets based on whole trajectories, while preserving probabilistic guarantees on future trajectory inclusion for the entire horizon.