21 papers
Clustered Randomized Smoothing for Stochastic Prediction Functions
Eduardo Figueiredo, Frederik Mathiesen, Julian Schumann +3
Modern stochastic predictors can model rich, multi-modal outcome distributions. However, this expressive power comes with challenges in ensuring robust predictions a critical r…
StochasticBarrier.jl: A Toolbox for Stochastic Barrier Function Synthesis
Rayan Mazouz, Frederik Baymler Mathiesen, Luca Laurenti +1
We present StochasticBarrier.jl, an open-source Julia-based toolbox for generating Stochastic Barrier Functions (SBFs) for safety verification of discrete-time stochastic systems w…
Certified Neural Approximations of Nonlinear Dynamics
Frederik Baymler Mathiesen, Nikolaus Vertovec, Francesco Fabiano +2
Neural networks hold great potential to act as approximate models of nonlinear dynamical systems, with the resulting neural approximations enabling verification and control of such…
Provably Safe Motion Planning Under Unknown Disturbances
Ibon Gracia, Qi Heng Ho, Luca Laurenti +1
We present a provably safe sampling-based motion planning algorithm for robotic systems affected by random disturbances of unknown distribution. We consider systems with linear or…
Verification of Unknown Dynamical Systems via Autoencoder Latent Space
Robert Reed, Luca Laurenti, Morteza Lahijanian
Formal verification provides a powerful framework for proving that dynamical systems satisfy their specifications. However, these techniques face scalability challenges in high-dim…
Stochastic Barrier Certificates in the Presence of Dynamic Obstacles
Rayan Mazouz, Luca Laurenti, Morteza Lahijanian
Safety of stochastic dynamic systems in environments with dynamic obstacles is studied in this paper through the lens of stochastic barrier functions. We introduce both time-invari…