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About

Yen-Lun Chen is a prominent robotics and computer vision researcher whose work centers on human-robot interaction, gesture recognition, and robot perception systems. His most influential contributions focus on enabling natural, intuitive communication between humans and robots through dynamic gesture recognition — a field where his 2014 paper on online dynamic gesture recognition has garnered 70 citations, establishing him as a key voice in the discipline. Chen's early work addressed fundamental challenges in real-time hand and face localization under varying illumination conditions, combining skin-color detection with depth information to achieve robust performance in complex environments. His 2012 system for depth-perception-based gesture recognition (50 citations) tackled longstanding limitations of traditional methods, including sensitivity to lighting and cluttered backgrounds — obstacles that had long hindered practical deployment. Beyond interaction, Chen made significant contributions to robot navigation through particle swarm optimization techniques and advanced the field of robot grasping via binocular stereo vision and camera calibration frameworks. His work on occluded object grasping and non-overlapping camera calibration demonstrates a thorough, systems-level approach to robotic perception. His household service robot project further reflects a commitment to translating research into practical, accessible applications for elderly care — making his body of work both technically rigorous and socially meaningful.