Zhengshuai Wang
Xiamen University, Henan University of Science and Technology
Papers
6
Total Citations
77
H-Index
4
About
Zhengshuai Wang is a researcher at the intersection of developmental robotics, human-robot interaction, and intelligent control systems. His work focuses on endowing robots with human-like communication and motor skills, particularly through biologically inspired learning paradigms. Wang’s most influential contribution is a developmental learning approach to robotic pointing, detailed in his 2014 paper (29 citations), which exploits human-robot interaction to teach robots this essential social skill—a method inspired by observing infant development. He has also pioneered novel techniques for robotic Chinese handwriting, introducing a motion-sensing input device for human-robot interaction (25 citations) and a reduced classifier ensemble for gesture-based writing control (15 citations). More recently, Wang has advanced mobile robotics with adaptive model predictive control for Mecanum-wheeled robots, optimized via improved genetic algorithms (2023). His work uniquely integrates constructive neural networks and Q-learning to model infant-like learning, as seen in his 2014 paper on brain-like neural networks for pointing. With a career spanning foundational developmental robotics to modern control theory, Wang’s research demonstrates how interdisciplinary inspiration from neuroscience and psychology can create more intuitive, capable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1A developmental approach to robotic pointing via human–robot interaction29 citations · 2014
- 2Robotic Free Writing of Chinese Characters via Human–Robot Interactions25 citations · 2014
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- 6A human-like learning approach to developmental robotic reaching2 citations · 2013