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About

Zejian Yuan is a robotics and computer vision researcher whose work spans human-robot interaction, autonomous navigation, and neural signal processing. His research bridges perception and intelligence, tackling some of the most demanding challenges in making robots understand and respond to the humans around them. Yuan's most recognized contributions include his attention-oriented framework for real-time action recognition in human-robot interaction scenarios, which addresses a critical gap in the field by tailoring recognition models specifically to the demands of interactive environments — earning 16 citations since 2021. Equally notable is his interdisciplinary work on EEG-fMRI fusion using Functional Source Separation, applied to steady-state visual evoked potentials, reflecting his engagement with neurorobotics and biologically inspired systems, also garnering 16 citations. His earlier contributions to mobile robotics remain foundational, particularly his improved global localization technique combining Hough Scan Matching with grid-based methods, cited 15 times, and his adaptive SLAM algorithm leveraging KLD sampling and MCMC techniques to handle time-varying state uncertainty. Together, these works demonstrate a research trajectory that moves fluidly between low-level robot navigation and high-level human-machine communication — positioning Yuan as a versatile contributor to intelligent robotic systems.