Dickson Neoh Tze How
Papers
8
Total Citations
106
H-Index
5
About
Dickson Neoh Tze How is a robotics researcher whose work bridges dexterous manipulation, human-robot interaction, and industrial inspection. His primary research areas include flexible deformable object modeling for robotic manipulation, behavior recognition for humanoid robots using deep learning, and the development of specialized inspection robots. His most impactful contribution is a comprehensive review on modeling flexible deformable objects for dexterous robotic manipulation (2019, 29 citations), which synthesizes advances across computer graphics, vision, and robotics. He has also pioneered the application of Long Short-Term Memory (LSTM) networks for behavior recognition in humanoid robots (2016, 28 citations; 2014, 27 citations), enabling robots to learn complex tasks from human demonstration. His work on particle-based garment folding perception (2017, 8 citations) addresses the challenging problem of home service robots recognizing and manipulating clothing. Beyond household robotics, Dickson has contributed to industrial applications, designing robotic systems for visual inspection of boiler tube inner surfaces (2017, 7 citations), demonstrating the practical impact of his research across both domestic and industrial settings. His work continues to advance the frontier of robotic perception and manipulation.
Research Focus
Key Achievements
Top Papers
- 1
- 2Behavior recognition for humanoid robots using long short-term memory28 citations · 2016
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- 6Image Acquisition System for Boiler Header Inspection Robot4 citations · 2014
- 7Robotic Arm Control Based on Human Arm Motion2 citations · 2014
- 8Modeling of flexible deformable object for robotic manipulation1 citations · 2016