12 citations · 33 across the 13 of their papers we have counts for
14 papers
Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling Process
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. Our prop…
Refining Human-Centered Autonomy Using Side Information
Adam J. Thorpe
Data-driven algorithms for human-centered autonomy use observed data to compute models of human behavior in order to ensure safety, correctness, and to avoid potential errors that…
Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control
Adam J. Thorpe, Cyrus Neary, Franck Djeumou +2
Data-driven control algorithms use observations of system dynamics to construct an implicit model for the purpose of control. However, in practice, data-driven techniques often req…
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…
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…
State-Based Confidence Bounds for Data-Driven Stochastic Reachability Using Hilbert Space Embeddings
Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi
In this paper, we compute finite sample bounds for data-driven approximations of the solution to stochastic reachability problems. Our approach uses a nonparametric technique known…
Trust-based user-interface design for human-automation systems
Abraham P. Vinod, Adam J. Thorpe, Philip A. Olaniyi +2
We present a method for dynamics-driven, user-interface design for a human-automation system via sensor selection. We define the user-interface to be the output of a MIMO LTI syste…
Approximate Stochastic Reachability for High Dimensional Systems
Adam J. Thorpe, Vignesh Sivaramakrishnan, Meeko M. K. Oishi
We present a method to compute the stochastic reachability safety probabilities for high-dimensional stochastic dynamical systems. Our approach takes advantage of a nonparametric l…
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…