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About

Serena Booth is a researcher at the intersection of human-robot interaction (HRI), trust, and security. Her work critically examines how people’s social and cognitive biases toward robots can be exploited, most famously in her highly cited 2017 study, “Piggybacking Robots” (82 citations). In this landmark experiment, Booth demonstrated that individuals were as likely to help a robot breach a secure-access dormitory as they would a human, revealing a dangerous vulnerability in physical security systems. This work has become a foundational reference for understanding overtrust in autonomous systems. Beyond security, Booth explores how humans form conceptual models of robot behavior—a key challenge for effective teaching and learning in HRI. Her 2022 paper, “Revisiting Human-Robot Teaching and Learning Through the Lens of Human Concept Learning” (12 citations), reframes robot training by drawing on cognitive science, while her contributions to variable autonomy (VAT, 2023) and robot controller understanding (RoCUS, 2020) push toward more transparent, adaptable human-robot teams. She also co-organizes the VAM-HRI workshop series, fostering cross-disciplinary dialogue on mixed reality for robotics. With a growing citation footprint and a focus on real-world vulnerabilities, Booth is shaping how we design robots that are not only capable, but trustworthy.