54 citations · 177 across the 51 of their papers we have counts for
24 papers · 1 filter
Learning Nonlinear Continuous-Time Systems for Formal Uncertainty Propagation and Probabilistic Evaluation
Peter Amorese, Morteza Lahijanian
Nonlinear ordinary differential equations (ODEs) are powerful tools for modeling real-world dynamical systems. However, propagating initial state uncertainty through nonlinear dyna…
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…
Bayesian Diagnosability and Active Fault Identification
Chun-Wei Kong, Jay McMahon, Morteza Lahijanian
We study fault identification in discrete-time nonlinear systems subject to additive Gaussian white noise. We introduce a Bayesian framework that explicitly accounts for unmodeled…
Piecewise Control Barrier Functions for Stochastic Systems
Rayan Mazouz, Luca Laurenti, Morteza Lahijanian
This paper presents a method for the simultaneous synthesis of a barrier certificate and a safe controller for discrete-time nonlinear stochastic systems. Our approach, based on pi…
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…
Data-Driven Strategy Synthesis for Stochastic Systems with Unknown Nonlinear Disturbances
Ibon Gracia, Dimitris Boskos, Luca Laurenti +1
In this paper, we introduce a data-driven framework for synthesis of provably-correct controllers for general nonlinear switched systems under complex specifications. The focus is…
Uncertainty Propagation in Stochastic Systems via Mixture Models with Error Quantification
Eduardo Figueiredo, Andrea Patane, Morteza Lahijanian +1
Uncertainty propagation in non-linear dynamical systems has become a key problem in various fields including control theory and machine learning. In this work we focus on discrete-…
IntervalMDP.jl: Accelerated Value Iteration for Interval Markov Decision Processes
Frederik Baymler Mathiesen, Morteza Lahijanian, Luca Laurenti
In this paper, we present IntervalMDP.jl, a Julia package for probabilistic analysis of interval Markov Decision Processes (IMDPs). IntervalMDP.jl facilitates the synthesis of opti…
A Unifying Perspective for Safety of Stochastic Systems: From Barrier Functions to Finite Abstractions
Luca Laurenti, Morteza Lahijanian
Providing safety guarantees for stochastic dynamical systems is a central problem in various fields, including control theory, machine learning, and robotics. Existing methods eith…
Formal Abstraction of General Stochastic Systems via Noise Partitioning
John Skovbekk, Luca Laurenti, Eric Frew +1
Verifying the performance of safety-critical, stochastic systems with complex noise distributions is difficult. We introduce a general procedure for the finite abstraction of nonli…
Promises of Deep Kernel Learning for Control Synthesis
Robert Reed, Luca Laurenti, Morteza Lahijanian
Deep Kernel Learning (DKL) combines the representational power of neural networks with the uncertainty quantification of Gaussian Processes. Hence, it is potentially a promising to…
Sampling-based Reactive Synthesis for Nondeterministic Hybrid Systems
Qi Heng Ho, Zachary N. Sunberg, Morteza Lahijanian
This paper introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints…
Efficient Strategy Synthesis for Switched Stochastic Systems with Distributional Uncertainty
Ibon Gracia, Dimitris Boskos, Morteza Lahijanian +2
We introduce a framework for the control of discrete-time switched stochastic systems with uncertain distributions. In particular, we consider stochastic dynamics with additive noi…
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…
Pareto Optimal Strategies for Event-Triggered Estimation
Anne Theurkauf, Nisar Ahmed, Morteza Lahijanian
Although resource-limited networked autonomous systems must be able to efficiently and effectively accomplish tasks, better conservation of resources often results in worse task pe…
Automaton-Guided Control Synthesis for Signal Temporal Logic Specifications
Qi Heng Ho, Roland B. Ilyes, Zachary N. Sunberg +1
This paper presents an algorithmic framework for control synthesis of continuous dynamical systems subject to signal temporal logic (STL) specifications. We propose a novel algorit…
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 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…
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…