Zixuan Lin

Papers

Total Citations

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About

Zixuan Lin is a researcher at the forefront of human-machine interaction, specializing in tactile feedback systems and machine vision for robotic perception. Their work addresses a critical challenge in intelligent robotics: enabling machines to recognize and convey surface roughness and texture—features essential for realistic haptic interaction but notoriously difficult to capture. In their most-cited study (2024, 4 citations), Lin proposed a novel machine vision-based approach to object surface recognition, demonstrating how tactile feedback can significantly improve a robot’s ability to distinguish object sizes, shapes, and compliance. This contribution lays foundational groundwork for more intuitive and controllable interactive robots, particularly in applications requiring fine-grained haptic feedback. While early in their career, Lin’s research bridges the gap between visual data and tactile sensation, offering promising pathways for assistive technologies, teleoperation, and immersive virtual environments. Their work underscores a growing commitment to making robotic systems not only smarter but also more perceptually aligned with human touch.