Kerrick Johnstonbaugh
Papers
1
Total Citations
4
H-Index
1
About
Kerrick Johnstonbaugh is a robotics researcher focused on advancing low-dimensional control for robotic manipulators. His work centers on learning efficient, state-conditioned linear mappings that bridge the gap between simplicity and expressiveness in action spaces—a critical challenge in dexterous manipulation. In his most cited paper, "Learning State Conditioned Linear Mappings for Low-Dimensional Control of Robotic Manipulators" (2023, 4 citations), Johnstonbaugh explores how linear action mapping methods can offer computational efficiency while still capturing complex motor commands, providing a principled alternative to nonlinear approaches. This contribution is particularly impactful for real-time control systems where computational resources are limited. By systematically analyzing trade-offs between linear and nonlinear representations, his research helps define when and how low-dimensional action spaces can be effectively deployed. Though early in his career, Johnstonbaugh’s work is already informing the design of more practical, scalable control algorithms for robotic systems, making him a promising voice in the intersection of machine learning and robotics.
Research Focus
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Top Papers
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