Vignesh Srinivasan
Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute
Papers
3
Total Citations
123
H-Index
3
About
Vignesh Srinivasan is a leading researcher at the intersection of deep learning and robotic surgery, whose work is pioneering sensorless force estimation—a critical challenge in Robot-Assisted Minimally Invasive Surgery (RAMIS). His primary research areas include vision-based force sensing, recurrent and convolutional neural networks, and sensor substitution in surgical robotics. Srinivasan’s most impactful contribution is his 2019 paper, "A recurrent convolutional neural network approach for sensorless force estimation in robotic surgery," which has garnered 93 citations. In this work, he introduced a hybrid R-CNN model that estimates interaction forces directly from visual data, circumventing the need for physical force sensors that are often impractical in constrained surgical environments. He further advanced this field with a semi-supervised deep neural network model (2018, 21 citations), reducing reliance on labeled data. His earlier work on estimating 3D position and velocity from monocular video (2017, 9 citations) laid the groundwork for vision-based sensor substitution, enabling tool-tip tracking without specialized hardware. Srinivasan’s research has profound implications for haptic feedback in surgery, enhancing precision and safety. His innovative use of deep learning to solve real-world surgical constraints marks him as a key figure in the future of autonomous and augmented robotic surgery.
Research Focus
Key Achievements
Top Papers
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