Yuming Dong

Shenzhen Institutes of Advanced Technology

Papers

Total Citations

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About

Yuming Dong is a leading researcher in the field of robotic tactile sensing and intelligent manipulation, with a focus on advancing soft sensor technologies and machine learning algorithms for dexterous grasping. Their most cited work, “Optical Soft Tactile Sensor Algorithm Based on Multiscale ResNet” (2023, 14 citations), introduces a novel deep learning approach that significantly enhances the accuracy of tactile recognition in robotic fingers. By integrating a multiscale residual network, Dong’s algorithm enables robots to precisely detect an object’s position and contact force intensity during grasping—a critical capability for successful and adaptive manipulation. This contribution addresses a fundamental challenge in robotics: achieving reliable tactile feedback for complex, real-world interactions. Dong’s research bridges the gap between soft sensor design and computational perception, offering scalable solutions for industrial automation and human-robot collaboration. With a growing citation impact, their work is recognized for its practical implications in enhancing robotic dexterity. Yuming Dong continues to push boundaries in tactile intelligence, making them a notable figure in the advancement of sensor-driven robotic systems.