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