Data-driven Reachable Set Estimation with Tunable Adversarial and Wasserstein Distributional Guarantees
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. To this end, we formulate a relaxed scenario program with slack variables that yields a tunable trade-off between reachable set size and out-of-sample trajectory inclusion over the horizon, thereby reducing sensitivity to outliers. Leveraging the recent results in adversariall...
| Authors | Georgios Pantazis, Michelle S. Chong |
|---|---|
| Primary category | math.OC |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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