Papers
79
Total Citations
1,209
H-Index
19
About
Yongsheng Ou is a robotics and intelligent systems researcher whose work spans robot learning and control, human-robot interaction, and autonomous navigation. His research addresses some of the most challenging problems in modern robotics, from taming the complex dynamics of flexible-joint manipulators to enabling robots to learn directly from human demonstrations. Ou's most influential contribution — a neural-learning-based control framework for robotic manipulators with flexible joints (175 citations) — tackled the significant uncertainties inherent in such systems, advancing the maturity of manipulator control technology. His parallel work on learning from demonstration, including dynamical systems modeled via Extreme Learning Machine (71 citations) and automated assembly skill acquisition, provides robots with intuitive, human-inspired programming pathways. In human-robot interaction, his Kinect-based gesture recognition and real-time human imitation systems (collectively exceeding 160 citations) established accessible, non-intrusive frameworks for natural human-robot communication. Ou has also made meaningful contributions to autonomous navigation, developing cost-effective LiDAR-vision fusion SLAM frameworks (72 citations) that make robust robot localization viable for consumer-grade hardware. Across more than a decade of research, his work consistently bridges theoretical rigor with practical implementation, making him a noteworthy contributor to the advancement of intelligent, adaptable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Human gesture recognition through a Kinect sensor81 citations · 2012
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- 5A Real-Time Human Imitation System Using Kinect51 citations · 2015
- 6SLAM of Robot based on the Fusion of Vision and LIDAR47 citations · 2018
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- 9A real-time human imitation system34 citations · 2012
- 10Robot trajectory tracking control using learning from demonstration method32 citations · 2019