Papers
191
Total Citations
4,685
H-Index
37
About
Hesheng Wang is a prominent robotics researcher whose work spans visual servoing, multi-robot systems, soft robotics, and surgical robotics. His most influential contributions lie in developing adaptive control frameworks for robots operating under uncertainty — particularly when cameras are uncalibrated and environmental parameters are unknown. His 2006 paper introducing a depth-independent interaction matrix for uncalibrated visual servoing (310 citations) fundamentally advanced how robot manipulators can operate without precise camera calibration, a challenge he extended to eye-in-hand configurations and combined point-line feature systems in subsequent work. Wang has also made significant strides in multi-robot coordination, authoring multiple highly cited studies on formation control that eliminate the need for direct position and velocity measurements — a practical breakthrough for real-world deployment. His research extends into soft robotics and medical applications, including adaptive control of compliant manipulators in constrained environments (204 citations) and the development of continuum robotic systems for minimally invasive surgeries such as maxillary sinus and cardiothoracic procedures. More recently, his transformer-based imitative reinforcement learning approach for multi-robot path planning (103 citations) reflects his growing influence in AI-driven robotics. Collectively, Wang's work has accumulated over 1,400 citations across these domains, marking him as a leading figure in intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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- 4Adaptive Vision-Based Leader–Follower Formation Control of Mobile Robots143 citations · 2016
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- 7Dynamic Visual Tracking for Manipulators Using an Uncalibrated Fixed Camera116 citations · 2007
- 8Development of a Novel Continuum Robotic System for Maxillary Sinus Surgery107 citations · 2018
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- 10Transformer-Based Imitative Reinforcement Learning for Multirobot Path Planning103 citations · 2023