6 papers
Training with Hard Constraints: Learning Neural Certificates and Controllers for SDEs
Chun-Wei Kong, Sebastian Escobar, Ibon Gracia +2
Due to their expressive power, neural networks (NNs) are promising templates for functional optimization problems, particularly for reach-avoid certificate generation for systems g…
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…
On the Optimality of Uncertain MDP Abstractions
Ibon Gracia, Morteza Lahijanian
We study the asymptotic optimality of abstraction-based control synthesis algorithms. Specifically, we consider uncertain MDP (UMDP) abstraction, and investigate whether refinement…
Data-Driven Control via Conditional Mean Embeddings: Formal Guarantees via Uncertain MDP Abstraction
Ibon Gracia, Morteza Lahijanian
Controlling stochastic systems with unknown dynamics and under complex specifications is specially challenging in safety-critical settings, where performance guarantees are essenti…
Beyond Interval MDPs: Tight and Efficient Abstractions of Stochastic Systems
Ibon Gracia, Morteza Lahijanian
This work addresses the general problem of control synthesis for continuous-space, discrete-time stochastic systems with probabilistic guarantees via finite abstractions. While est…
Temporal Logic Control for Nonlinear Stochastic Systems Under Unknown Disturbances
Ibon Gracia, Luca Laurenti, Manuel Mazo +2
In this paper, we present a novel framework to synthesize robust strategies for discrete-time nonlinear systems with random disturbances that are unknown, against temporal logic sp…