Papers › A Simple and Efficient Sampling-based Algorithm for General Reachability Analysis

A Simple and Efficient Sampling-based Algorithm for General Reachability Analysis

10 Dec 2021arXiv:2112.05745archive 2025-07-28

Thomas Lew, Lucas Janson, Riccardo Bonalli, Marco Pavone

In this work, we analyze an efficient sampling-based algorithm for general-purpose reachability analysis, which remains a notoriously challenging problem with applications ranging from neural network verification to safety analysis of dynamical systems. By sampling inputs, evaluating their images in the true reachable set, and taking their ϵ-padded convex hull as a set estimator, this algorithm applies to general problem settings and is simple to implement. Our main contribution is the derivation of asymptotic and finite-sample accuracy guarantees using random set theory. This analysis informs algorithmic design to obtain an ϵ-close reachable set approximation with high probability, provides insights into which reachability problems are most challenging, and motivates safety-critical applications of the technique. On a neural network verification task, we show that this approach is more accurate and significantly faster than prior work. Informed by our analysis, we also design a robust model predictive controller that we demonstrate in hardware experiments.

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1ran · honoured contract
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2ran · fixture could not drive it
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Y_Q_matrix stanfordasl/randup/sensitivity.py official repository ran · fixture could not drive it MIT (permissive) · 1d77aa14be7c1ccf · report
f stanfordasl/randup/sensitivity.py official repository ran · fixture could not drive it MIT (permissive) · 526f27c0c50ce6a1 · report
weighted_sample_pts_unit_ball stanfordasl/randup/sensitivity.py official repository ran · honoured contract fingerprinted MIT (permissive) · 73b78f002a2c6e25 · report
list_of_strings stanfordasl/stochasticedl/benchmark_main.py community (archive-listed) ran · our draft was wrong fingerprinted MIT (permissive) · 19ee4683eda506c6 · report
compute_error_metric stanfordasl/stochasticedl/benchmark_utils.py community (archive-listed) unverified MIT (permissive) · a3cc4c698c0b859d · report
compute_variance_metric stanfordasl/stochasticedl/benchmark_utils.py community (archive-listed) unverified MIT (permissive) · cb85abfc02a206cf · report
simulate_state_trajectory_monte_carlo stanfordasl/stochasticedl/mars_powered_descent.py community (archive-listed) unverified MIT (permissive) · 75cfa1cd68369e21 · report

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