16 papers
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…
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…
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…
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…
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…