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About

Will Panitch is a robotics researcher at the intersection of surgical automation, teleoperation, and imitation learning. His work addresses critical challenges in robot-assisted surgery, particularly for vascular shunt insertion—a life-saving procedure often performed in trauma settings. Panitch has developed frameworks for automating this delicate task using the da Vinci Research Kit (dVRK), exploring scenarios where a supervising surgeon is local, remote, or entirely unavailable. His most cited paper introduces a digital twin framework for telesurgery, mitigating network quality-of-service issues to enable reliable long-distance surgical collaboration. Beyond surgery, Panitch’s research extends to deformable object manipulation, as seen in his work on automating gasket assembly. A notable contribution is the In-Context Robot Transformer (ICRT), which leverages next-token prediction for in-context imitation learning, allowing robots to perform novel tasks from demonstration examples. With over 30 citations across his publications, Panitch’s work is shaping the future of autonomous and teleoperated robotic systems, with direct implications for healthcare and manufacturing. His achievements include advancing surgical robotics toward greater autonomy and resilience in real-world, high-stakes environments.