Yuexi Wang
Papers
6
Total Citations
125
H-Index
4
About
Yuexi Wang is a pioneering researcher in the field of modular robotics and human-robot collaboration (HRC), with a focus on developing intelligent control systems that enable safe and efficient physical interaction between humans and robots. Their major contributions center on solving the fundamental challenge of estimating human motion intention in real-time, allowing modular robot manipulators (MRMs) to adaptively cooperate with human partners. Wang’s most influential work, "Cooperative Game-Based Approximate Optimal Control of Modular Robot Manipulators for Human–Robot Collaboration" (2023), has garnered 104 citations and introduces a novel game-theoretic framework that optimizes both human comfort and task performance during collaborative manipulation. This approach addresses the critical need for decentralized control in modular systems, where individual joint modules must coordinate without centralized oversight. Wang has also advanced finite-time optimal control through adaptive dynamic programming (ADP), as demonstrated in their 2025 paper on force/position control for reconfigurable robots. Their research portfolio, spanning from 2021 to 2025, consistently emphasizes robust, decentralized control methods that leverage harmonic drive compliance models and interaction torque estimation. Wang’s work is particularly notable for bridging theoretical optimal control with practical implementation in physical human-robot interaction (pHRI), making significant strides toward safer and more intuitive collaborative robots.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6