Jeb Stefarr

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About

Jeb Stefarr is a researcher at the intersection of robotics, control theory, and decision-making under uncertainty. His primary contributions lie in developing algorithms that enable robots to plan efficient paths while accounting for both physical constraints and the cognitive limitations of human operators or autonomous systems. In his most-cited work, "Rationally Inattentive Path-Planning via RRT" (2021, 10 citations), Stefarr introduces a novel path length metric for mobile robots navigating configuration spaces with obstacles and stochastic disturbances. By integrating this metric with the RRT* algorithm, he addresses the challenge of rationally allocating attention—a concept borrowed from information theory—to balance exploration and exploitation in uncertain environments. This work bridges the gap between optimal path planning and bounded rationality, offering a framework for robots to make computationally frugal yet robust decisions. Though early in his career, Stefarr’s research has already influenced discussions on how autonomous systems can operate efficiently under real-world constraints, making him a promising voice in the fields of robotic navigation and decision theory.