Feida Gu
Papers
1
Total Citations
9
H-Index
1
About
Feida Gu is a robotics researcher whose work focuses on the modeling and control of deformable objects, with a particular emphasis on deformable linear objects (DLOs) like cables, ropes, and surgical sutures. His key research areas include robot manipulation, dynamics modeling, and shape control. Gu’s major contribution lies in addressing the critical challenge of precise shape control for DLOs, which has broad applications in manufacturing and medical surgery. His most-cited paper, “Learning Graph Dynamics With Interaction Effects Propagation for Deformable Linear Objects Shape Control” (2025, 9 citations), introduces an innovative approach that leverages graph-based learning to predict deformation dynamics, enabling more accurate and robust robotic manipulation. This work stands out for its novel integration of interaction effects propagation, which captures the complex dependencies between different points along a DLO. Gu’s research has the potential to significantly advance automation in industries requiring delicate handling of flexible materials, and his early citation impact signals growing recognition in the field.
Research Focus
Key Achievements
Top Papers
- 1