no code implementations • 9 Jan 2023 • Adam J. Thorpe, Cyrus Neary, Franck Djeumou, Meeko M. K. Oishi, Ufuk Topcu
Our proposed approach incorporates prior knowledge of the system dynamics as a bias term in the kernel learning problem.
no code implementations • 3 Dec 2022 • Joshua Pilipovsky, Vignesh Sivaramakrishnan, Meeko M. K. Oishi, Panagiotis Tsiotras
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 ubiquity of neural nets in many engineering applications.
1 code implementation • 12 Mar 2022 • Adam J. Thorpe, Meeko M. K. Oishi
We present SOCKS, a data-driven stochastic optimal control toolbox based in kernel methods.
no code implementations • 8 Feb 2022 • Adam J. Thorpe, Thomas Lew, Meeko M. K. Oishi, Marco Pavone
We present a data-driven algorithm for efficiently computing stochastic control policies for general joint chance constrained optimal control problems.
1 code implementation • 11 Oct 2018 • Abraham P. Vinod, Meeko M. K. Oishi
Of special interest is the stochastic reach set, the set of all initial states from which it is possible to stay in the target tube with a probability above a desired threshold.
Optimization and Control Systems and Control
1 code implementation • 19 Mar 2018 • Abraham P. Vinod, Meeko M. K. Oishi
We present theory and algorithms for the computation of probability-weighted "keep-out" sets to assure probabilistically safe navigation in the presence of multiple rigid body obstacles with stochastic dynamics.
Systems and Control Optimization and Control
