Papers
8
Total Citations
29
H-Index
3
About
Yunze Shi is a rising researcher at the intersection of medical robotics, human-robot collaboration, and immersive technologies. Their work centers on developing intelligent control systems that enhance the synergy between surgeons, robots, and real-time imaging data. A key contribution is the integration of Mixed Reality (MR) and ultrasound guidance into robotic procedures, as seen in their highly cited 2022 review on MR in robotics (8 citations) and their work on visual optimization of ultrasound-guided robot-assisted tasks using variable impedance control (6 citations). Shi has pioneered methods for dynamic virtual fixture generation from intra-operative 3D images (2024, 4 citations) and virtual potential field-based motion planning for kinesthetically guided teleoperation (2023, 3 citations), directly addressing the challenge of seamless shared control in dynamic surgical environments. Beyond clinical applications, they have advanced robotics education through an experiential active learning framework using immersive technology (2024, 3 citations). Their work on compliant control for pin-based shape displays (2023) further demonstrates a commitment to improving physical human-robot interaction. With a growing portfolio that includes trajectory planning via large-scale digital twins (2025), Shi is establishing a reputation for translating complex control theory into practical, safety-prioritized solutions for next-generation surgical robotics.
Research Focus
Key Achievements
Top Papers
- 1MR Meets Robotics: A Review of Mixed Reality Technology in Robotics8 citations · 2022
- 2
- 3
- 4
- 5
- 6
- 7Classical Control Strategies Used in Recent Surgical Robots2 citations · 2021
- 8