Papers
2
Total Citations
15
H-Index
2
About
Dr. Xu Shi is at the forefront of dexterous robotic manipulation, specializing in grasp synthesis, motion prediction, and shared control for anthropomorphic robotic hands. Their major contributions include developing a fast, learning-based sampling method for force-closure grasp synthesis, which tackles the high-dimensional configuration space challenge that has long hindered real-time performance in robotic hand planning. This work has garnered 12 citations, reflecting its significance in enabling more responsive and practical robotic grasping. Dr. Shi also advanced shared control systems with a novel approach to hand grasp pose prediction using a motion prior field, achieving 3 citations for its predictive analysis that enhances pre-shape planning for robotic wrists and hands. By integrating learning-based techniques with traditional grasp quality metrics, Dr. Shi’s research bridges the gap between theoretical grasp analysis and real-world robotic applications. Their work is particularly notable for its potential to improve human-robot interaction and assistive technologies, making robotic hands more intuitive and effective in dynamic environments. Dr. Xu Shi’s contributions are shaping the future of autonomous and shared robotic manipulation.
Research Focus
Key Achievements
Top Papers
- 1Fast Force-Closure Grasp Synthesis With Learning-Based Sampling12 citations · 2023
- 2Hand Grasp Pose Prediction Based on Motion Prior Field3 citations · 2023