Xueting Bi
Papers
1
Total Citations
7
H-Index
1
About
Xueting Bi is a robotics researcher whose work centers on real-time robotic manipulation and grasp detection, with a particular focus on deep learning-driven visual perception. Her most-cited paper, "Attention Grasping Network: A Real-time Approach to Generating Grasp Synthesis" (2019, 7 citations), introduces the Attention Grasping Network (AGN), a fully convolutional neural network that leverages a novel attention mechanism for pixelwise grasp synthesis. This approach enables robots to automatically focus on the most salient visual features, significantly improving the speed and accuracy of grasp detection in dynamic environments. Bi’s contributions advance the field of robotic manipulation by bridging the gap between high-level visual understanding and low-level motor control, making real-time grasping more robust and efficient. Her work has implications for industrial automation, service robotics, and human-robot interaction. With a growing citation impact, Bi is recognized for integrating attention-based architectures into practical robotic systems, and her research continues to inspire new directions in intelligent grasping and autonomous manipulation.
Research Focus
Key Achievements
Top Papers
- 1