LATIDIA · Robótica

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arXiv:2607.18606v2 Anuncio Tipo: reemplazar Resumen: El análisis de alcanzabilidad es fundamental para el control crítico de seguridad, la robótica y la verificación de redes neuronales, pero los métodos computacionales clásicos, como Hamilton-Jacobi re

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arXiv:2607.18606v2 Announce Type: replace Abstract: Reachability analysis is central to safety-critical control, robotics, and neural network verification, but classical computational methods, such as Hamilton--Jacobi reachability and set propagation, scale poorly with state dimension. Sampling-based methods have emerged as a promising alternative, often providing finite-sample guarantees that bound the probability-mass left uncovered. However, an explicit account of how the geometry of the initial set, the dynamics, and the sampling law affect the accuracy of the estimator is not fully available in the literature. We study this by casting sampling-based reachable-set recovery as geometric support estimation over a family of problems specified by an initial set, its dynamics, and a sampling law.