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About

Sandra Mau’s research lies at the critical intersection of human-robot interaction and multirobot systems, with a focus on making human supervision of robot teams both safe and efficient. Her work addresses a fundamental challenge in robotics: how to enable a single human operator to effectively manage multiple autonomous robots, particularly in high-stakes environments like space exploration. Mau’s major contributions include pioneering scheduling algorithms for human-multirobot supervisory control. Her 2007 paper on “Scheduling for humans in multirobot supervisory control” (21 citations) introduced a method to prioritize human tasks and maximize team size, while her 2018 follow-up proposed the double-Shifted Shortest Processing Time (dSSPT) algorithm to minimize robot downtime. Her research on tele-supervised autonomous robots for space exploration (21 citations) directly supports NASA’s vision for safe, efficient lunar and planetary missions, where robots handle dangerous tasks to protect human astronauts. With over 70 total citations across her most-cited works, Mau’s contributions are foundational for future multirobot systems in space, disaster response, and industrial automation—anywhere humans must supervise multiple robots without being overwhelmed.