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About

Lijun Zhao is a robotics researcher whose work spans mobile robot navigation, human-robot interaction, and 3D environmental perception. With a career trajectory moving from foundational robot dynamics and control to cutting-edge deep learning applications, Zhao has established himself as a versatile contributor to intelligent robotics systems. His early work addressed core challenges in human-robot collaboration and robot learning, including an augmented discrete-time framework for human-robot interaction (29 citations) and an extended Dynamic Movement Primitives framework enabling robots to learn variable stiffness manipulation from humans (25 citations). These contributions helped lay groundwork for more intuitive robot teaching methodologies, exemplified by his teleoperation-based learning system combining visual interaction and extreme learning machines (50 citations). Zhao's research increasingly focused on robot perception and navigation, producing influential work on visual semantic navigation using deep learning (31 citations), multi-view fusion-based 3D object detection (28 citations), and multi-channel CNN-based indoor environmental perception (24 citations). His multi-obstacle path planning work became his most-cited contribution (83 citations), reflecting its practical significance. More recently, his Poly-MOT framework for 3D multi-object tracking (52 citations) has garnered rapid attention for advancing motion-aware robot navigation. Collectively, his publications reflect a coherent vision of building robots capable of perceiving, reasoning about, and safely navigating complex real-world environments.