Kerrick Johnstonbaugh — accepted AI-conference papers

2024

Cylindrical Thompson Sampling for High-Dimensional Bayesian Optimization

Bahador Rashidi, Kerrick Johnstonbaugh, Chao Gao

AISTATS 2024poster

Many industrial and scientific applications require optimization of one or more objectives by tuning dozens or hundreds of input parameters. While Bayesian optimization has been a popular approach for the efficient optimization of blackbox functions, its performance decreases drastically as the dime…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗

2023

Learning State Conditioned Linear Mappings for Low-Dimensional Control of Robotic Manipulators

Michael Przystupa, Kerrick Johnstonbaugh, Zichen Zhang, Laura Petrich, Masood Dehghan, Faezeh Haghverd +1

ICRA 2023poster

Identifying an appropriate task space can simplify solving robotic manipulation problems. One solution is deploying control algorithms in a learned low-dimensional action space. Linear and nonlinear action mapping methods have trade-offs between simplicity and the ability to express motor commands o…

Cited by 3 Source ↗Scholar ↗