Jinlin Gu

University of Chinese Academy of Sciences, Chinese Academy of Sciences

Papers

Total Citations

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About

Jinlin Gu is a robotics researcher whose work focuses on enabling robots to interact safely and effectively with unknown, unstructured environments. His primary research areas include robot-environment contact estimation, uncalibrated visual servoing, and adaptive control for physical human-robot interaction. Gu’s most cited work, “Impedance estimation for robot contact with uncalibrated environments” (2021, 16 citations), introduces a method for robots to estimate the stiffness and damping of surfaces they touch without prior calibration—a critical capability for tasks like assembly or medical robotics. In a 2022 paper (6 citations), he advanced visual servoing by combining homography-based task functions with a neural-network-assisted robust filtering scheme and adaptive servo gain, making vision-guided control more resilient to image defects and system noise. His 2020 work on virtual semi-active damping learning control (4 citations) addresses the challenge of smooth transitions between free motion and contact tasks, proposing a learning-based approach to improve performance without requiring precise environmental models. Gu’s contributions are particularly valuable for industrial and service robotics, where adaptability to unknown surroundings is essential.