54 citations · 177 across the 51 of their papers we have counts for
54 papers
Safe-by-Design Learning via Energy-based Neural Networks
Simone Betteti, Morteza Lahijanian, Luca Laurenti
Learning neural-network models of dynamical systems with safety guarantees is a fundamental requirement for their deployment in safety-critical settings. Safety is commonly establi…
Multi-Objective Kinodynamic Motion Planning with Asymptotic Pareto Optimality
Yusif Razzaq, Anne Theurkauf, Nisar Ahmed +1
In this paper, we address the challenge of multi-objective motion planning for systems under kinodynamic constraints. We consider three problem classes: (i) lexicographic optimizat…
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…
Universal Learning of Stochastic Dynamics for Exact Belief Propagation using Bernstein Normalizing Flows
Peter Amorese, Morteza Lahijanian
Predicting the distribution of future states in a stochastic system, known as belief propagation, is fundamental to reasoning under uncertainty. However, nonlinear dynamics often m…
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…
On Polynomial Stochastic Barrier Functions: Bernstein Versus Sum-of-Squares
Peter Amorese, Morteza Lahijanian
Stochastic Barrier Functions (SBFs) certify the safety of stochastic systems by formulating a functional optimization problem, which state-of-the-art methods solve using Sum-of-Squ…
Extended Version: Multi-Robot Motion Planning with Cooperative Localization
Anne Theurkauf, Nisar Ahmed, Morteza Lahijanian
We consider the uncertain multi-robot motion planning (MRMP) problem with cooperative localization (CL-MRMP), under both motion and measurement noise, where each robot can act as a…
Falsification of Autonomous Systems in Rich Environments
Khen Elimelech, Morteza Lahijanian, Lydia E. Kavraki +1
Validating the behavior of autonomous Cyber-Physical Systems (CPS) and Artificial Intelligence (AI) agents, which rely on automated controllers, is an objective of great importance…
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…
Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs
Chun-Wei Kong, Luca Laurenti, Jay McMahon +1
Stochastic differential equations are commonly used to describe the evolution of stochastic processes. The state uncertainty of such processes is best represented by the probabilit…
Error Bounds For Gaussian Process Regression Under Bounded Support Noise With Applications To Safety Certification
Robert Reed, Luca Laurenti, Morteza Lahijanian
Gaussian Process Regression (GPR) is a powerful and elegant method for learning complex functions from noisy data with a wide range of applications, including in safety-critical do…
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
Steven Adams, Andrea Patanè, Morteza Lahijanian +1
Infinitely wide or deep neural networks (NNs) with independent and identically distributed (i.i.d.) parameters have been shown to be equivalent to Gaussian processes. Because of th…
Online Pareto-Optimal Decision-Making for Complex Tasks using Active Inference
Peter Amorese, Shohei Wakayama, Nisar Ahmed +1
When a robot autonomously performs a complex task, it frequently must balance competing objectives while maintaining safety. This becomes more difficult in uncertain environments w…
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…
Data-Driven Permissible Safe Control with Barrier Certificates
Rayan Mazouz, John Skovbekk, Frederik Baymler Mathiesen +3
This paper introduces a method of identifying a maximal set of safe strategies from data for stochastic systems with unknown dynamics using barrier certificates. The first step is…
Piecewise Stochastic Barrier Functions
Rayan Mazouz, Frederik Baymler Mathiesen, Luca Laurenti +1
This paper presents a novel stochastic barrier function (SBF) framework for safety analysis of stochastic systems based on piecewise (PW) functions. We first outline a general form…
Stochastic Games for Interactive Manipulation Domains
Karan Muvvala, Andrew M. Wells, Morteza Lahijanian +2
As robots become more prevalent, the complexity of robot-robot, robot-human, and robot-environment interactions increases. In these interactions, a robot needs to consider not only…
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…
Recursively-Constrained Partially Observable Markov Decision Processes
Qi Heng Ho, Tyler Becker, Benjamin Kraske +5
Many sequential decision problems involve optimizing one objective function while imposing constraints on other objectives. Constrained Partially Observable Markov Decision Process…
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…