8 citations · 20 across the 7 of their papers we have counts for
9 papers
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…
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…
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…