Papers
18
Total Citations
299
H-Index
11
About
Pey Yuen Tao is a robotics researcher whose work spans cooperative robotic systems, robot calibration, motion planning, and machine learning applications in robotics. With a career bridging foundational control theory and modern deep learning, Tao has made sustained contributions to both industrial and autonomous robotic systems. His early foundational work on cooperative mobile manipulators introduced centralized robust adaptive control frameworks to manage coupled dynamics under uncertainty and external disturbances, earning 75 citations and establishing him as a authority in multi-robot coordination. He subsequently advanced industrial robot calibration through innovative combinations of Product-of-Exponential models with Gaussian Processes and neural networks, improving positioning accuracy by accounting for both geometric and non-geometric error sources across multiple publications. Tao's research evolved to embrace intelligent automation, contributing to coverage motion planning for 3D shape inspection, sensor-guided path generation for offshore repair tasks, and optimal redundancy resolution for arc welding robots. More recently, he has pioneered deep learning approaches to robotic perception, including a unified convolutional network for scene recognition and object detection, and a semi-supervised domain adaptation framework for grasping detection under limited labelled data. Collectively, his work reflects a researcher committed to bridging theoretical rigor with practical industrial deployment.
Research Focus
Key Achievements
Top Papers
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- 5Autonomous stair climbing for mobile tracked robot20 citations · 2011
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