Papers
123
Total Citations
3,451
H-Index
32
About
Zhaojie Ju is a prominent robotics and human-robot interaction researcher whose work sits at the intersection of intelligent systems, machine learning, and collaborative robotics. His research spans several interconnected domains, including robot learning, dexterous manipulation, physical human-robot collaboration, and hand gesture recognition — areas where he has made substantial and widely recognized contributions. Among his most influential work, Ju has advanced the field of robot learning by comprehensively surveying deep reinforcement learning, imitation learning, and transfer learning techniques for intelligent manipulation (222 citations). His pioneering multisensory framework for human hand motion analysis laid essential groundwork for natural human-robot interfaces, while his subsequent deep learning-based gesture recognition systems have driven significant progress in intuitive robot control. Notably, his research extends into socially impactful applications, including developing supervised autonomous systems for robot-assisted therapy with children with autism spectrum disorder (138 citations). Ju's contributions to physical human-robot collaboration and trajectory tracking control using symplectic optimal methods further demonstrate his breadth across both applied and theoretical robotics. With a body of work collectively amassing over 1,300 citations across his top ten papers alone, his research has meaningfully shaped how robots perceive, learn from, and collaborate with humans — making his work essential reading for anyone entering the field of intelligent robotics.
Research Focus
Key Achievements
Top Papers
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- 3Zooming image based false matches elimination algorithms for robot navigation137 citations · 2017
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- 5Human Hand Motion Analysis With Multisensory Information126 citations · 2013
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