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About

Zhenyu Pan is a leading researcher in physical human–robot interaction (pHRI) and robotic manipulation, with a focus on multimodal sensing, deep learning, and autonomous grasping. His most cited work, a comprehensive 2020 review on multimodal fusion methods in pHRI (68 citations), synthesizes advances in real-time perception for safe, collaborative human–robot systems, establishing a foundational framework for the field. Pan has made significant contributions to robotic picking in dense clutter, developing domain-invariant learning from synthetic rendering (2021, 13 citations) and a deep learning pipeline for picking-point detection under occlusion and disorder (2017, 8 citations). His innovative Bayesian Grasp approach (2019, 3 citations) integrates prior tactile knowledge with visual input to achieve stable grasps without costly regrasping, advancing efficiency in unstructured environments. With a career spanning foundational reviews and novel algorithms, Pan’s work directly impacts industrial automation and assistive robotics, demonstrating how deep learning and sensor fusion can enable robots to perceive and act reliably in complex, cluttered settings. His research continues to shape the future of intelligent, human-aware robotic systems.