no code implementations • 25 Mar 2024 • Kevin S. Miller, Adam J. Thorpe, Ufuk Topcu
We present an active learning algorithm for learning dynamics that leverages side information by explicitly incorporating prior domain knowledge into the sampling process.
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 • 2 Jun 2022 • Kendric R. Ortiz, Adam J. Thorpe, AnaMaria Perez, Maya Luster, Brandon J. Pitts, Meeko Oishi
We propose an easily computable modeling framework which takes advantage of a metric to assess variability in individual human response in a dynamic task that subjects repeat over several trials.
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.