Nural Yilmaz
Papers
14
Total Citations
172
H-Index
7
About
Nural Yilmaz is a leading researcher in surgical robotics, with a primary focus on restoring the sense of touch in robotic-assisted minimally invasive surgery. Her work addresses a critical limitation of current systems like the da Vinci robot: the loss of haptic feedback. Yilmaz’s major contributions lie in developing advanced sensorless force estimation algorithms, using neural networks and disturbance observers to infer tool-tissue interaction forces from existing joint torque data, eliminating the need for physical force sensors. Her most cited paper (66 citations) introduces a neural network-based method for inverse dynamics identification and external force estimation on the da Vinci Research Kit. Beyond estimation, she has designed novel hardware, including a dexterous, back-drivable parallel robotic forceps wrist with a large orientation workspace, and a hyper-redundant surgical instrument for 6-axis force/torque sensing. Her work on transparency-optimized haptic teleoperation and learned dynamics transfer across different robots further demonstrates her impact. With over 150 total citations, Yilmaz’s research is foundational for enabling safer, more intuitive robotic surgery, culminating in recent autonomous vision-guided tumor resection—a significant step toward intelligent surgical systems.
Research Focus
Key Achievements
Top Papers
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- 6Robot Force Estimation with Learned Intraoperative Correction11 citations · 2021
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- 10Autonomous Vision-Guided Resection of Central Airway Obstruction5 citations · 2025