Rachel Heald

Papers

Total Citations

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About

Rachel Heald is a leading researcher in human-robot teaming, with a focus on optimizing performance in high-stress, safety-critical environments. Her work centers on developing diagnostic workload assessment algorithms that enable robots to dynamically adapt their interactions with human partners in settings like NASA control rooms and first-response operations. Heald’s major contribution is a computational framework that measures human cognitive workload in real time, allowing collaborative and supervisory robot teams to adjust their behavior—reducing human error and preventing costly or life-threatening mistakes. Her most-cited paper, “A Diagnostic Human Workload Assessment Algorithm for Collaborative and Supervisory Human–Robot Teams” (2019), has garnered 27 citations and is recognized for its practical impact on designing safer, more efficient human-robot systems. An earlier version of this work (2018) laid the groundwork for these advances. Heald’s research bridges cognitive science and robotics, offering actionable tools for mission-critical domains. Her achievements highlight a commitment to enhancing human performance through intelligent automation, making her a key voice in the future of human-robot collaboration.