Zhongjin Xu
Papers
1
Total Citations
9
H-Index
1
About
Zhongjin Xu is a robotics researcher whose work focuses on advancing tactile sensing for dexterous robotic manipulation. His key contributions lie in developing innovative, low-cost sensor designs that integrate imaging and machine learning to extract critical contact information. In his highly cited 2019 paper, "A Simple Robotic Fingertip Sensor Using Imaging and Shallow Neural Networks," Xu introduced a novel approach that enables a robotic fingertip to simultaneously measure geometrical data—such as contact position, normal, and object curvature—alongside physical properties like contact force. This work is foundational for multifingered robot hands, providing the essential sensory feedback needed for precise grasping and manipulation tasks. By leveraging shallow neural networks for data interpretation, Xu’s sensor achieves robust performance without complex hardware, making it accessible for broader research and application. With 9 citations, this paper has influenced subsequent work in tactile sensing and soft robotics. Xu’s research continues to bridge the gap between simple, cost-effective sensor design and the sophisticated perception required for autonomous robotic interaction.
Research Focus
Key Achievements
Top Papers
- 1