Xuefeng Bao

Northeastern University, Northwestern Polytechnical University

Papers

Total Citations

H-Index

About

Xuefeng Bao is a robotics researcher whose work focuses on autonomous manipulation and multi-robot coordination. He is best known for his contributions to grasp estimation, particularly through his work on GraspVDN, a scene-oriented approach that learns vector representations of grasps while accounting for environmental constraints. This method, published in 2021, has garnered 12 citations and addresses a critical challenge in robotic manipulation: generating feasible grasps in cluttered, real-world settings. Bao also developed a multi-robot formation platform using an indoor global positioning system, a low-cost and generalizable solution that has been cited 8 times and facilitates experimental validation in formation control research. Additionally, his work on trajectory tracking for wheeled mobile robots using backstepping control (4 citations) demonstrates his versatility in both theoretical and applied robotics. Bao’s research is notable for bridging the gap between simulation and real-world deployment, offering practical tools and algorithms that advance autonomous systems. His work is particularly valuable for students and researchers interested in grasp planning, multi-robot systems, and mobile robot control.