Papers
41
Total Citations
1,879
H-Index
20
About
Wen Qi is a prominent researcher at the forefront of human-robot interaction (HRI), teleoperated surgical robotics, and intelligent control systems. His work spans gesture recognition, force estimation, redundancy optimization, and multimodal interaction frameworks, collectively advancing the capabilities of robots operating in medical and collaborative environments. Among his most influential contributions is his development of multi-sensor and deep neural network approaches for human-robot systems. His 2021 paper on multi-sensor guided hand gesture recognition using recurrent neural networks has garnered 245 citations, establishing new benchmarks for touch-free teleoperation in surgical settings. Complementing this, his deep learning methods for robot tool dynamics identification and EMG-based force estimation have significantly improved haptic feedback fidelity in minimally invasive surgery. Qi's 2023 survey on multimodal HRI (155 citations) demonstrates his broader influence in shaping research directions across the field. His explorations of pneumatic soft robots, fuzzy approximation-based task-space control, and reinforcement learning-based gaming frameworks further reflect the remarkable breadth of his expertise. With over 1,000 cumulative citations across his top works, Wen Qi has established himself as a defining voice in next-generation intelligent robotics and human-centered automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Recent advancements in multimodal human–robot interaction155 citations · 2023
- 4
- 5Pneumatic Soft Robots: Challenges and Benefits134 citations · 2022
- 6
- 7
- 8
- 9
- 10