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About

Sachin Patil is a robotics researcher whose work sits at the intersection of robot-assisted surgery, motion planning, and autonomous control systems. His research addresses some of the most pressing challenges in minimally invasive surgery, including the precise control of cable-driven surgical robots, haptic sensing, and surgical task automation. Patil's most-cited contribution, "Learning accurate kinematic control of cable-driven surgical robots" (2014, 76 citations), demonstrates his expertise in applying machine learning techniques — specifically Gaussian Process Regression — to overcome the notoriously difficult non-linear kinematics of surgical robotic systems. Complementing this, his work on a low-cost haptic palpation probe (2015, 70 citations) introduced an innovative approach to localizing subcutaneous blood vessels during robot-assisted procedures, combining practical engineering with real clinical need. His earlier work on automated tissue retraction (2010, 48 citations) was among the first to systematically explore surgical task autonomy, laying groundwork for subsequent shared-control architectures that balance surgeon oversight with automated assistance. More recently, Patil has explored high-frequency motion replanning under uncertainty, pushing sampling-based planners toward real-world robustness. Together, his contributions reflect a sustained commitment to making surgical robotics smarter, safer, and more autonomous.