Qinxi Yu

University of Toronto

Papers

4

Total Citations

261

H-Index

3

About

Qinxi Yu is a leading researcher at the intersection of robot learning and surgical robotics, with a focus on creating high-fidelity simulation environments that bridge the gap between virtual training and real-world dexterous manipulation. Their most impactful contribution is the development of **Orbit**, a unified and modular simulation framework powered by NVIDIA Isaac Sim that enables interactive robot learning with photo-realistic scenes and robust rigid/deformable body physics—a work that has garnered over 226 citations since 2023. Building on this foundation, Yu introduced **Orbit-Surgical** (2024), an open-source platform specifically designed for learning surgical augmented dexterity, addressing the longstanding challenge of fast, accurate, and realistic surgical simulation. This framework has already attracted 27 citations and is poised to accelerate progress in autonomous and teleoperated surgery. Yu’s notable achievements include pioneering robot-assisted vascular shunt insertion using the da Vinci Research Kit (dVRK), exploring scenarios from local surgeon assistance to remote teleoperation. With a portfolio that seamlessly integrates simulation infrastructure with clinical applications, Qinxi Yu is shaping the future of how robots learn complex, safety-critical tasks in medicine.

Research Focus

Key Achievements

3
H-Index
4
Papers
261
Total Citations
65
Avg Citations/Paper
🏆 Most Cited Paper
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
226 citations · 2023
📈 Most Prolific Year: 2023 (3 Papers)
🤝 Key Collaborators: 28
🏛 Institutions: University of Toronto

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 12 days ago