Papers
13
Total Citations
337
H-Index
9
About
Andrew Razjigaev is a robotics researcher whose work spans industrial automation, computer vision, and surgical robotics. He first gained international recognition as a key contributor to Cartman, the low-cost Cartesian manipulator that claimed first place at the prestigious Amazon Robotics Challenge in 2017, a achievement documented across multiple highly cited publications accumulating over 150 citations. His contributions to that project encompassed the robot's mechanical design, multi-modal end-effector systems, and semantic segmentation techniques for perception in cluttered environments — work that demonstrated how cost-effective, intelligently designed systems could outperform more complex competitors in real-world pick-and-place tasks. Razjigaev subsequently pivoted toward medical robotics, exploring minimally invasive surgical systems with increasing sophistication. His research on snake-like continuum manipulators, concentric tube robots, and their integration with platforms such as the RAVEN II surgical system reflects a commitment to expanding dexterity and accessibility in keyhole procedures. Notably, his 2022 work on end-to-end bespoke surgical robot design introduced patient-specific anatomical optimization, while his 2024 review of human-robot interaction in autonomous surgery has already attracted 19 citations. Collectively, his portfolio positions him as a versatile roboticist bridging industrial and clinical domains.
Research Focus
Key Achievements
Top Papers
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- 2Semantic Segmentation from Limited Training Data52 citations · 2018
- 3Robotic and Image-Guided Knee Arthroscopy27 citations · 2019
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