Papers
20
Total Citations
943
H-Index
12
About
Azad Shademan is a pioneering roboticist whose work sits at the intersection of autonomous surgery, computer vision, and human-robot interaction. His landmark 2016 paper, "Supervised autonomous robotic soft tissue surgery," with 600 citations, fundamentally challenged the paradigm of robot-assisted surgery by demonstrating that a robot could autonomously perform soft-tissue suturing with greater precision than a human surgeon—a breakthrough that promises to enhance surgical efficacy and safety. Shademan’s contributions extend to developing robust visual tracking systems for surgical robots, including biocompatible near-infrared fluorescent markers and plenoptic cameras for 3D sensing of deformable tissues, as detailed in his highly cited works on the Smart Tissue Anastomosis Robot (STAR). He has also advanced uncalibrated visual servoing through global visual-motor estimation and reinforcement learning, enabling robots to adapt to uncertain environments. His research on intuitive human-robot interfaces, such as Kinect-based hand tracking and pointing gesture control, has improved teleoperation for surgical tasks. With over 800 total citations across his top papers, Shademan’s work is foundational to the future of autonomous robotic surgery and intelligent human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1Supervised autonomous robotic soft tissue surgery600 citations · 2016
- 2Global visual-motor estimation for uncalibrated visual servoing39 citations · 2007
- 3Biocompatible Near-Infrared Three-Dimensional Tracking System38 citations · 2017
- 4SEPO: Selecting by pointing as an intuitive human-robot command interface38 citations · 2013
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- 6Model-based and model-free reinforcement learning for visual servoing31 citations · 2009
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- 10Feasibility of near-infrared markers for guiding surgical robots17 citations · 2013