Papers
26
Total Citations
519
H-Index
12
About
Zhengkun Yi is a prominent robotics and artificial intelligence researcher whose work sits at the intersection of tactile sensing, robotic perception, and machine learning. His research has made significant contributions to how robots perceive and interact with the physical world, spanning bioinspired tactile sensors, active object exploration, grasp stability recognition, and medical robotics. Yi's early influential work introduced Gaussian process-based active tactile exploration strategies (96 citations), enabling robots to efficiently map object geometries for stable grasping. Complementing this, his bioinspired tactile sensor research (88 citations) drew on biological principles to advance surface roughness discrimination, with a comprehensive review of biomimetic tactile sensors and spike-train signal processing (69 citations) establishing him as a key voice in the field. More recently, Yi has pushed boundaries in intelligent tactile signal processing, developing novel deep learning architectures such as TactONet for hardness classification and graph convolutional networks for grasp stability assessment. His work also extends to robotic palpation for tumor depth recognition and exoskeleton gait control, reflecting a broad commitment to real-world human-robot applications. With over 430 cumulative citations across his top papers, Yi's research continues to shape how robots develop richer, more nuanced senses of touch.
Research Focus
Key Achievements
Top Papers
- 1Active tactile object exploration with Gaussian processes96 citations · 2016
- 2Bioinspired tactile sensor for surface roughness discrimination88 citations · 2017
- 3Biomimetic tactile sensors and signal processing with spike trains: A review69 citations · 2017
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- 10Tactile Grasp Stability Classification Based on Graph Convolutional Networks20 citations · 2021