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About

Preston Fairchild is a rising leader in soft robotics, with research that bridges modeling, control, and real-world deployment of compliant manipulators. His work centers on three interconnected challenges: enabling soft robots to operate safely alongside humans, giving them the ability to sense and adapt their own stiffness, and planning their motion through cluttered environments. Fairchild’s most cited paper, “IMU-assisted robotic structured light sensing with featureless registration under uncertainties for pipeline inspection” (2023, 15 citations), tackles a critical industrial need—autonomous inspection of confined, unstructured spaces. He further advances the field through “Efficient Path Planning of Soft Robotic Arms in the Presence of Obstacles” (2021, 11 citations), which leverages the inherent redundancy of continuum robots for obstacle avoidance. His contributions to stiffness tuning, detailed in “Semi-Physical Modeling of Soft Pneumatic Actuators With Stiffness Tuning” (2023, 8 citations) and “Physics-Informed Online Estimation of Stiffness and Shape of Soft Robotic Manipulators” (2023, 3 citations), provide foundational frameworks for real-time control and adaptation. By integrating physics-informed learning with practical sensing, Fairchild is shaping a future where soft robots are not just safe, but also precise and reliable in real-world tasks.