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About

Connor Watson is pioneering the next generation of soft, continuum robots, with a focus on growing robots and concentric tube robots for medical applications. His work addresses fundamental challenges in controlling these inherently compliant systems, which navigate safely through constrained environments like the human body. Watson’s most influential paper, "Permanent Magnet-Based Localization for Growing Robots in Medical Applications" (2020, 55 citations), tackles the critical issue of sensing and control for robots that extend from their tip. He has further advanced the field by developing closed-loop position control via online Jacobian corrections (2021, 20 citations) and model-free disturbance rejection strategies using neural networks (2024, 9 citations). His research on shape control of concentric tube robots (2024, 5 citations) and tactile perception for growing robots (2022, 3 citations) demonstrates a comprehensive approach to making these flexible machines safer and more precise. By enabling these robots to sense their environment and correct their motion in real-time, Watson is laying the groundwork for transformative tools in minimally invasive surgery and exploration of confined spaces.