Papers
35
Total Citations
535
H-Index
12
About
Junpei Zhong is a prominent researcher at the intersection of robotics, machine learning, and human-robot interaction, whose work has made substantial contributions to how intelligent systems learn, adapt, and communicate with humans. With a career spanning over a decade, Zhong has established expertise in neural network-based learning and control, imitation learning, gesture recognition, and multimodal robotic skill acquisition. His 2017 review of neural networks for robot learning and control (75 citations) remains a foundational reference in the field, synthesizing how biologically inspired computational models can govern complex nonlinear robotic systems. His research on teleoperation, teaching-by-demonstration, and deep reinforcement learning for activity recognition (46 citations) reflects a sustained commitment to making robots more intuitive and capable partners in human environments. Notably, his work on gesture prediction in social robots and transfer learning for gesture recognition underscores his focus on bridging perception and interaction. Zhong has also critically examined the gap between domestic robotics aspirations and real-world computational intelligence, demonstrating both technical depth and broader research vision. Collectively, his publications have amassed hundreds of citations, marking him as an influential voice in advancing practical, intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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- 4A Visual-Based Gesture Prediction Framework Applied in Social Robots42 citations · 2021
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- 8Research on Transfer Learning of Vision-based Gesture Recognition25 citations · 2021
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