Ming‐Shyan Wang
Papers
10
Total Citations
171
H-Index
7
About
Ming-Shyan Wang is a leading researcher in robotics, artificial intelligence, and human-robot interaction, with a focus on developing intelligent systems for society’s most pressing challenges. His major contributions span stereo vision-based object manipulation, where he pioneered the use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for eye-to-hand calibration (36 citations), and the integration of AIoT for automated picking systems in online retail, addressing the demands of Industry 4.0 and Society 5.0 (29 citations). Wang’s work on flexible tactile sensors for robotic grasping control (27 citations) has been instrumental in enabling robots to detect slippage and adjust grip force, a critical advancement for safe object handling. He has also made significant strides in 3D object pose estimation and depth estimation using deep learning, such as deep region-based CNNs, for eye-in-hand manipulators (21 citations each). Notably, his research extends to socially impactful applications, including stair-climbing robots for elderly care and a Chinese chess robotic system designed to support cognitive health in aging populations. With over 170 citations across his most cited works, Wang’s innovations are shaping the future of assistive robotics and automated systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Flexible tactile sensor for the grasping control of robot fingers27 citations · 2013
- 43D object pose estimation using stereo vision for object manipulation system21 citations · 2017
- 5
- 6Design and implementation of a stair-climbing robot14 citations · 2008
- 7
- 8Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning6 citations · 2021
- 9Fuzzy Logic Control Design for a Stair-Climbing Robot3 citations · 2009
- 10A Fuzzy Control Based Stair-Climbing Service Robot2 citations · 2010