Tannaz Torkaman
Papers
5
Total Citations
45
H-Index
3
About
Tannaz Torkaman is pioneering the integration of soft, intelligent sensors into soft robotics—a field where conventional rigid sensors fail due to the extreme deformability of these systems. Her research centers on developing smart polymer-based sensors for force and shape sensing, with a primary application in soft surgical robots for minimally invasive procedures. Torkaman’s major contributions include the design, modeling, and experimental validation of novel gelatin-graphite and polymer sensors that are both compliant and accurate. She has tackled the critical challenge of sensor calibration by introducing learning-based methods—including deep neural networks and convolutional deep learning—that account for nonlinear behavior and rate-dependent effects, dramatically improving feedback control. Her most cited work, "Embedded Six-DoF Force–Torque Sensor for Soft Robots With Learning-Based Calibration" (2023, 18 citations), demonstrates a complete pipeline from prototype to validation. With over 45 total citations across her key papers, Torkaman’s innovations are shaping the future of intraluminal procedures like bronchoscopy and cardiovascular intervention, where precise, adaptable sensing is essential. Her work stands out for bridging materials science, robotics, and machine learning to solve a fundamental bottleneck in soft robot control.
Research Focus
Key Achievements
Top Papers
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