27 citations · 152 across the 28 of their papers we have counts for
12 papers · 1 filter
Inner approximations of stochastic programs for data-driven stochastic barrier function design
Frederik Baymler Mathiesen, Licio Romao, Simeon C. Calvert +2
This paper proposes a new framework to compute finite-horizon safety guarantees for discrete-time piece-wise affine systems with stochastic noise of unknown distributions. The appr…
Interval Markov Decision Processes with Continuous Action-Spaces
Giannis Delimpaltadakis, Morteza Lahijanian, Manuel Mazo +1
Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of res…
Safety Certification for Stochastic Systems via Neural Barrier Functions
Frederik Baymler Mathiesen, Simeon Calvert, Luca Laurenti
Providing non-trivial certificates of safety for non-linear stochastic systems is an important open problem that limits the wider adoption of autonomous systems in safety-critical…
Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions
Rayan Mazouz, Karan Muvvala, Akash Ratheesh +2
Neural Networks (NNs) have been successfully employed to represent the state evolution of complex dynamical systems. Such models, referred to as NN dynamic models (NNDMs), use iter…
Formal Control Synthesis for Stochastic Neural Network Dynamic Models
Steven Adams, Morteza Lahijanian, Luca Laurenti
Neural networks (NNs) are emerging as powerful tools to represent the dynamics of control systems with complicated physics or black-box components. Due to complexity of NNs, howeve…
Formal Analysis of the Sampling Behaviour of Stochastic Event-Triggered Control
Giannis Delimpaltadakis, Luca Laurenti, Manuel Mazo
Analyzing Event-Triggered Control's (ETC) sampling behaviour is of paramount importance, as it enables formal assessment of its sampling performance and prediction of its sampling…
Formal Verification of Unknown Dynamical Systems via Gaussian Process Regression
John Skovbekk, Luca Laurenti, Eric Frew +1
Leveraging autonomous systems in safety-critical scenarios requires verifying their behaviors in the presence of uncertainties and black-box components that influence the system dy…
Synergistic Offline-Online Control Synthesis via Local Gaussian Process Regression
John Jackson, Luca Laurenti, Eric Frew +1
Autonomous systems often have complex and possibly unknown dynamics due to, e.g., black-box components. This leads to unpredictable behaviors and makes control design with performa…
Strategy Synthesis for Partially-known Switched Stochastic Systems
John Jackson, Luca Laurenti, Eric Frew +1
We present a data-driven framework for strategy synthesis for partially-known switched stochastic systems. The properties of the system are specified using linear temporal logic (L…
Abstracting the Sampling Behaviour of Stochastic Linear Periodic Event-Triggered Control Systems
Giannis Delimpaltadakis, Luca Laurenti, Manuel Mazo
Recently, there have been efforts towards understanding the sampling behaviour of event-triggered control (ETC), for obtaining metrics on its sampling performance and predicting it…
Safety Verification of Unknown Dynamical Systems via Gaussian Process Regression
John Jackson, Luca Laurenti, Eric Frew +1
The deployment of autonomous systems that operate in unstructured environments necessitates algorithms to verify their safety. This can be challenging due to, e.g., black-box compo…
Efficiency through Uncertainty: Scalable Formal Synthesis for Stochastic Hybrid Systems
Nathalie Cauchi, Luca Laurenti, Morteza Lahijanian +3
This work targets the development of an efficient abstraction method for formal analysis and control synthesis of discrete-time stochastic hybrid systems (SHS) with linear dynamics…