Zhenkun Wen
Papers
3
Total Citations
61
H-Index
3
About
Zhenkun Wen is a leading researcher in soft robotics, with a primary focus on advancing dexterous manipulation and multi-modal perception for bionic soft hands. His work bridges critical gaps in human-robot interaction by integrating machine learning with novel sensor technologies. Wen’s most influential contribution is the development of a machine learning-based multi-modal information perception system for soft robotic hands, published in 2019 and cited 53 times. This work introduced a flexible optical fiber-based curvature sensor that enables precise bending detection, significantly enhancing the sensory capabilities of soft grippers. Building on this, his 2023 paper on a bionic soft hand with dexterity operation and tactile force interaction addresses the longstanding challenge of limited sensing and unfriendly interaction in existing soft hands. Wen also proposed a trigger-based dexterous operation strategy using multimodal sensors, allowing autonomous choice operations. His research is notable for combining optical-based curvature sensors with gas pressure sensing, creating robust systems that improve both manipulation accuracy and user safety. With a growing citation impact, Wen’s work is shaping the next generation of adaptive, sensor-rich soft robotic hands for applications in prosthetics and industrial automation.
Research Focus
Key Achievements
Top Papers
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