1 citations · 2 across the 9 of their papers we have counts for
11 papers
Free Final Time Adaptive Mesh Covariance Steering via Sequential Convex Programming
Joshua Pilipovsky
In this paper we develop a sequential convex programming (SCP) framework for free-final-time covariance steering of nonlinear stochastic differential equations (SDEs) subject to bo…
SAVER: A Toolbox for Sampling-Based, Probabilistic Verification of Neural Networks
Vignesh Sivaramakrishnan, Krishna C. Kalagarla, Rosalyn Devonport +3
We present a neural network verification toolbox to 1) assess the probability of satisfaction of a constraint, and 2) synthesize a set expansion factor to achieve the probability o…
DUST: A Framework for Data-Driven Density Steering
Joshua Pilipovsky, Panagiotis Tsiotras
We consider the problem of data-driven stochastic optimal control of an unknown LTI dynamical system. Assuming the process noise is normally distributed, we pose the problem of ste…
Distributionally Robust Density Control with Wasserstein Ambiguity Sets
Joshua Pilipovsky, Panagiotis Tsiotras
Precise control under uncertainty requires a good understanding and characterization of the noise affecting the system. This paper studies the problem of steering state distributio…
Data-Driven Robust Covariance Control for Uncertain Linear Systems
Joshua Pilipovsky, Panagiotis Tsiotras
The theory of covariance control and covariance steering (CS) deals with controlling the dispersion of trajectories of a dynamical system, under the implicit assumption that accura…
Computationally Efficient Chance Constrained Covariance Control with Output Feedback
Joshua Pilipovsky, Panagiotis Tsiotras
This paper studies the problem of developing computationally efficient solutions for steering the distribution of the state of a stochastic, linear dynamical system between two bou…
Data-Driven Covariance Steering Control Design
Joshua Pilipovsky, Panagiotis Tsiotras
This paper studies the problem of steering the distribution of a linear time-invariant system from an initial normal distribution to a terminal normal distribution under no knowled…
Probabilistic Verification of ReLU Neural Networks via Characteristic Functions
Joshua Pilipovsky, Vignesh Sivaramakrishnan, Meeko M. K. Oishi +1
Verifying the input-output relationships of a neural network so as to achieve some desired performance specification is a difficult, yet important, problem due to the growing ubiqu…
Distributionally Robust Covariance Steering with Optimal Risk Allocation
Venkatraman Renganathan, Joshua Pilipovsky, Panagiotis Tsiotras
This article extends the optimal covariance steering (CS) problem for discrete time linear stochastic systems modeled using moment-based ambiguity sets. To hedge against the uncert…
Distribution Steering for Discrete-Time Linear Systems with General Disturbances using Characteristic Functions
Vignesh Sivaramakrishnan, Joshua Pilipovsky, Meeko M. K. Oishi +1
We propose to solve a constrained distribution steering problem, i.e., steering a stochastic linear system from an initial distribution to some final, desired distribution subject…
Covariance Steering with Optimal Risk Allocation
Joshua Pilipovsky, Panagiotis Tsiotras
This paper extends the optimal covariance steering problem for linear stochastic systems subject to chance constraints to account for optimal risk allocation. Previous works have a…