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About

Wenxuan Mou is a researcher whose work bridges the frontiers of robotic perception and human-robot interaction. Her primary research areas include tactile object recognition, haptic sensing, and the psychology of trust in autonomous systems. Mou’s most significant contribution is the development of the Iterative Closest Labeled Point (iCLAP) algorithm, a novel method that fuses proprioceptive and tactile data for robust, shape-based object recognition. This work, cited over 90 times across its key publications, enables robots to identify objects regardless of translation or rotation, addressing a fundamental challenge in robotic manipulation. In parallel, Mou has made impactful strides in understanding human-robot trust. Her highly cited studies on Theory of Mind (ToM) demonstrate that robots capable of inferring human beliefs and intentions can significantly improve trust in iterative human-robot games. With over 65 citations in this domain, her research provides critical insights for designing socially intelligent robots. Mou’s work is notable for its interdisciplinary approach, combining engineering rigor with cognitive science principles, and her findings are essential reading for anyone developing robots that must both perceive the physical world and earn the trust of the humans they serve.