42 citations · 122 across the 34 of their papers we have counts for
40 papers
Forward Reachability for Discrete-Time Nonlinear Stochastic Systems via Mixed-Monotonicity and Stochastic Order
Vignesh Sivaramakrishnan, Rosalyn A. Devonport, Murat Arcak +1
We present a method to overapproximate forward stochastic reach sets of discrete-time, stochastic nonlinear systems with interval geometry. This is made possible by extending the t…
Blameless and Optimal Control under Prioritized Safety Constraints
Natalia Pavlasek, Sarah H. Q. Li, Behçet Açıkmeşe +2
In many resource-limited optimal control problems, multiple constraints may be enforced that are jointly infeasible due to external factors such as subsystem failures, unexpected d…
Stochastic Reachability of Uncontrolled Systems via Probability Measures: Approximation via Deep Neural Networks
Karthik Sivaramakrishnan, Vignesh Sivaramakrishnan, Rosalyn Alex Devonport +1
This paper poses a theoretical characterization of the stochastic reachability problem in terms of probability measures, capturing the probability measure of the state of the syste…
Chance Constrained Stochastic Optimal Control Based on Sample Statistics With Almost Surely Probabilistic Guarantees
Shawn Priore, Meeko Oishi
While techniques have been developed for chance constrained stochastic optimal control using sample disturbance data that provide a probabilistic confidence bound for chance constr…
Stochastic Optimal Control For Gaussian Disturbances with Unknown Mean and Variance Based on Sample Statistics
Shawn Priore, Meeko Oishi
We propose an open loop methodology based on sample statistics to solve chance constrained stochastic optimal control problems with probabilistic safety guarantees for linear syste…
Chance Constrained Stochastic Optimal Control for Arbitrarily Disturbed LTI Systems Via the One-Sided Vysochanskij-Petunin Inequality
Shawn Priore, Meeko Oishi
While many techniques have been developed for chance constrained stochastic optimal control with Gaussian disturbance processes, far less is known about computationally efficient m…
Chance Constrained Stochastic Optimal Control for Linear Systems with a Time Varying Random Control Matrix
Shawn Priore, Meeko Oishi
This work proposes an open-loop methodology to solve chance constrained stochastic optimal control problems for linear systems with a stochastic control matrix. We consider a joint…
Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control
Adam J. Thorpe, Cyrus Neary, Franck Djeumou +2
Data-driven control algorithms use observations of system dynamics to construct an implicit model for the purpose of control. However, in practice, data-driven techniques often req…
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…
Approximate Stochastic Optimal Control for Linear Time Invariant Systems with Heavy-tailed Disturbances
Shawn Priore, Christopher Petersen, Meeko Oishi
We propose an open loop control scheme for linear time invariant systems perturbed by multivariate disturbances through the use of quantile reformulations. The multivariate …
Chance Constrained Stochastic Optimal Control for Linear Systems with Time Varying Random Plant Parameters
Shawn Priore, Ali Bidram, Meeko Oishi
We propose an open loop control scheme for linear systems with time-varying random elements in the plant's state matrix. This paper focuses on joint chance constraints for potentia…
Data-Driven Stochastic Optimal Control Using Kernel Gradients
Adam J. Thorpe, Jake A. Gonzales, Meeko M. K. Oishi
We present an empirical, gradient-based method for solving data-driven stochastic optimal control problems using the theory of kernel embeddings of distributions. By embedding the…
Characterizing Within-Driver Variability in Driving Dynamics During Obstacle Avoidance Maneuvers
Kendric R. Ortiz, Adam J. Thorpe, AnaMaria Perez +3
Variability in human response creates non-trivial challenges for modeling and control of human-automation systems. As autonomy becomes pervasive, methods that can accommodate human…
SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods
Adam J. Thorpe, Meeko M. K. Oishi
We present SOCKS, a data-driven stochastic optimal control toolbox based in kernel methods. SOCKS is a collection of data-driven algorithms that compute approximate solutions to st…
Data-Driven Chance Constrained Control using Kernel Distribution Embeddings
Adam J. Thorpe, Thomas Lew, Meeko M. K. Oishi +1
We present a data-driven algorithm for efficiently computing stochastic control policies for general joint chance constrained optimal control problems. Our approach leverages the t…
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…
Approximate Quantiles for Stochastic Optimal Control of LTI Systems with Arbitrary Disturbances
Shawn Priore, Christopher Petersen, Meeko Oishi
We propose a method for open-loop stochastic optimal control of LTI systems based on Taylor approximations of quantile functions. This approach enables efficient computation of qua…
Stochastic Optimal Control via Hilbert Space Embeddings of Distributions
Adam J. Thorpe, Meeko M. K. Oishi
Kernel embeddings of distributions have recently gained significant attention in the machine learning community as a data-driven technique for representing probability distribution…
Learning Approximate Forward Reachable Sets Using Separating Kernels
Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi
We present a data-driven method for computing approximate forward reachable sets using separating kernels in a reproducing kernel Hilbert space. We frame the problem as a support e…
SReachTools Kernel Module: Data-Driven Stochastic Reachability Using Hilbert Space Embeddings of Distributions
Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi
We present algorithms for performing data-driven stochastic reachability as an addition to SReachTools, an open-source stochastic reachability toolbox. Our method leverages a class…
Convexified Open-Loop Stochastic Optimal Control for Linear Non-Gaussian Systems
Vignesh Sivaramakrishnan, Abraham P. Vinod, Meeko M. K. Oishi
We consider stochastic optimal control of linear dynamical systems with additive non-Gaussian disturbance. We propose a novel, sampling-free approach, based on Fourier transformati…
State-Based Confidence Bounds for Data-Driven Stochastic Reachability Using Hilbert Space Embeddings
Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi
In this paper, we compute finite sample bounds for data-driven approximations of the solution to stochastic reachability problems. Our approach uses a nonparametric technique known…
Trust-based user-interface design for human-automation systems
Abraham P. Vinod, Adam J. Thorpe, Philip A. Olaniyi +2
We present a method for dynamics-driven, user-interface design for a human-automation system via sensor selection. We define the user-interface to be the output of a MIMO LTI syste…
Fast, Convexified Stochastic Optimal Open-Loop Control For Linear Systems Using Empirical Characteristic Functions
Vignesh Sivaramakrishnan, Meeko M. K. Oishi
We consider the problem of stochastic optimal control in the presence of an unknown disturbance. We characterize the disturbance via empirical characteristic functions, and employ…