Papers
9
Total Citations
340
H-Index
6
About
Waner Lin is a leading researcher in flexible tactile sensing and intelligent perception systems, with a focus on bridging the gap between artificial and human-like touch. Their work centers on developing advanced pressure and strain sensors, integrating machine learning for tactile data processing, and enabling robots to perceive complex physical properties such as deformability, surface texture, and liquid dynamics. Lin’s major contributions include the design of a graded nest-like architecture for flexible piezoresistive sensors, achieving wide-range pressure measurements with high sensitivity (164 citations), and the creation of a road-narrow-inspired ionic hydrogel strain sensor with tunable gauge factors (54 citations). Their impact is underscored by over 340 total citations across top publications, with notable work on self-adaptive perception of object deformability using biomimetic mechanoreceptors and a multimodal sole sensor for legged robots navigating complex terrains. Lin also explores neuromorphic spike-based neural coding for efficient tactile perception and augmented gesture estimation for human-robot interaction, demonstrating a commitment to advancing both sensor hardware and intelligent interpretation. Their research is pivotal for applications in health monitoring, prosthetics, and soft robotics.
Research Focus
Key Achievements
Top Papers
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- 7Recent advances in spike-based neural coding for tactile perception6 citations · 2025
- 8Augmented Pointing Gesture Estimation for Human-Robot Interaction6 citations · 2022
- 9Robot Embodied Dynamic Tactile Perception of Liquid in Containers4 citations · 2024