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About

Yaohui Guo is a rising researcher whose work sits at the intersection of human-robot interaction, trust dynamics, and autonomous systems. His research centers on a fundamental challenge in modern robotics: how can robots accurately model, predict, and respond to human trust in real time, rather than relying on static, end-of-experiment assessments? Guo's most influential contribution, "Modeling and Predicting Trust Dynamics in Human–Robot Teaming: A Bayesian Inference Approach" (2020), has garnered over 125 citations and introduced a personalized, probabilistic framework for tracking how trust evolves moment-to-moment during human-robot collaboration. This work fundamentally shifted the field's perspective from snapshot evaluations to continuous, dynamic modeling. Building on this foundation, Guo expanded his scope to increasingly complex scenarios. His Trust Inference and Propagation (TIP) model addresses multi-human, multi-robot teams — a largely unexplored frontier in the field — while his work on reward shaping tackles the delicate balance between task performance and trust maintenance. His exploration of "reverse psychology" in trust-aware HRI further demonstrates his creative, nuanced approach to robot decision-making. Collectively, Guo's contributions are shaping a more sophisticated, human-centered understanding of autonomous teaming.