Adam J. Thorpe

According to our database, Adam J. Thorpe authored at least 17 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling Process.

Kevin S. Miller, Adam J. Thorpe, Ufuk Topcu

CoRR, 2024

2023

Refining Human-Centered Autonomy Using Side Information.

Adam J. Thorpe

CoRR, 2023

Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control.

Adam J. Thorpe, Cyrus Neary, Franck Djeumou, Meeko M. K. Oishi, Ufuk Topcu

CoRR, 2023

Data-Driven Stochastic Optimal Control Using Kernel Gradients.

Adam J. Thorpe, Jake A. Gonzales, Meeko M. K. Oishi

Proceedings of the American Control Conference, 2023

2022

Sensor Selection for Dynamics-Driven User-Interface Design.

Abraham P. Vinod, Adam J. Thorpe, Philip A. Olaniyi, Tyler H. Summers, Meeko M. K. Oishi

IEEE Trans. Control. Syst. Technol., 2022

Characterizing Within-Driver Variability in Driving Dynamics During Obstacle Avoidance Maneuvers.

Kendric R. Ortiz, Adam J. Thorpe, AnaMaria Perez, Maya Luster, Brandon J. Pitts, Meeko Oishi

CoRR, 2022

State-based confidence bounds for data-driven stochastic reachability using Hilbert space embeddings.

Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi

Autom., 2022

Data-Driven Chance Constrained Control using Kernel Distribution Embeddings.

Adam J. Thorpe, Thomas Lew, Meeko Oishi, Marco Pavone

Proceedings of the Learning for Dynamics and Control Conference, 2022

SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods.

Adam J. Thorpe, Meeko Oishi

Proceedings of the HSCC '22: 25th ACM International Conference on Hybrid Systems: Computation and Control, Milan, Italy, May 4, 2022

2021

Learning Approximate Forward Reachable Sets Using Separating Kernels.

Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

SReachTools Kernel Module: Data-Driven Stochastic Reachability Using Hilbert Space Embeddings of Distributions.

Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

Stochastic Optimal Control via Hilbert Space Embeddings of Distributions.

Adam J. Thorpe, Meeko M. K. Oishi

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

Approximate Stochastic Reachability for High Dimensional Systems.

Adam J. Thorpe, Vignesh Sivaramakrishnan, Meeko M. K. Oishi

Proceedings of the 2021 American Control Conference, 2021

2020

Model-Free Stochastic Reachability Using Kernel Distribution Embeddings.

Adam J. Thorpe, Meeko M. K. Oishi

IEEE Control. Syst. Lett., 2020

Data-Driven Stochastic Reachability Using Hilbert Space Embeddings.

Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi

CoRR, 2020

Trust-based user-interface design for human-automation systems.

Abraham P. Vinod, Adam J. Thorpe, Philip A. Olaniyi, Tyler H. Summers, Meeko M. K. Oishi

CoRR, 2020

2019

Stochastic Reachability for Systems up to a Million Dimensions.

Adam J. Thorpe, Vignesh Sivaramakrishnan, Meeko M. K. Oishi

CoRR, 2019