12 citations · 33 across the 13 of their papers we have counts for
4 papers · 1 filter
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…
Characterizing Within-Driver Variability in Driving Dynamics During Obstacle Avoidance Maneuvers
Kendric R. Ortiz, Adam J. Thorpe, AnaMaria Perez +3
Variability in human response creates non-trivial challenges for modeling and control of human-automation systems. As autonomy becomes pervasive, methods that can accommodate human…
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…