42 citations · 122 across the 34 of their papers we have counts for
16 papers · 1 filter
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…
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…
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…
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…
Model-Free Stochastic Reachability Using Kernel Distribution Embeddings
Adam J. Thorpe, Meeko M. K. Oishi
We present a solution to the terminal-hitting stochastic reach-avoid problem for a Markov control process. This solution takes advantage of a nonparametric representation of the st…
Voronoi Partition-based Scenario Reduction for Fast Sampling-based Stochastic Reachability Computation of LTI Systems
Hossein Sartipizadeh, Abraham P. Vinod, Behcet Acikmese +1
In this paper, we address the stochastic reach-avoid problem for linear systems with additive stochastic uncertainty. We seek to compute the maximum probability that the states rem…
Stochastic reachability of a target tube: Theory and computation
Abraham P. Vinod, Meeko M. K. Oishi
Probabilistic guarantees of safety and performance are important in constrained dynamical systems with stochastic uncertainty. We consider the stochastic reachability problem, whic…
Computational Techniques for Reachability Analysis of Partially Observable Discrete Time Stochastic Hybrid Systems
Kendra Lesser, Meeko Oishi
Reachability analysis of hybrid systems has been used as a safety verification tool to assess offline whether the state of a system is capable of remaining within a designated safe…
Reachability for Partially Observable Discrete Time Stochastic Hybrid Systems
Kendra Lesser, Meeko Oishi
When designing optimal controllers for any system, it is often the case that the true state of the system is unknown to the controller, for example due to noisy measurements or par…