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Endotracheal intubation is a critical medical procedure for protecting a patient’s airway. Current intubation technology requires extensive anatomical knowledge, training, technical skill, and a clear view of the glottic opening. However, all of these may be limited during emergency care for trauma and cardiac arrest outside the hospital, where first-pass failure is nearly 35%. To address this challenge, we designed a soft robotic device to autonomously guide a breathing tube into the trachea with the goal of allowing rapid, repeatable, and safe intubation without the need for extensive training, skill, anatomical knowledge, or a glottic view. During initial device testing with highly trained users in a mannequin and a cadaver, we found a 100% success rate and an average intubation duration of under 8 s. We then conducted a preliminary study comparing the device with video laryngoscopy, in which prehospital medical providers with 5 min of device training intubated cadavers. When using the device, users achieved an 87% first-pass success rate and a 96% overall success rate, requiring an average of 1.1 attempts and 21 s for successful intubation, significantly (P = 0.008) faster than with video laryngoscopy. When using video laryngoscopy, the users achieved a 63% first-pass success rate and a 92% overall success rate, requiring an average of 1.6 attempts and 44 s for successful intubation. This preliminary study offers directions for future clinical studies, the next step in testing a device that could address the critical needs of emergency airway management and help democratize intubation. 

Soft robots promise improved safety and capability over rigid robots when deployed near humans or in complex, delicate, and dynamic environments. However, infinite degrees of freedom and the potential for highly nonlinear dynamics severely complicate their modeling and control. Analytical and machine learning methodologies have been applied to model soft robots but with constraints: quasi-static motions, quasi-linear deflections, or both. Here, we advance the modeling and control of soft robots into the inertial, nonlinear regime. We controlled motions of a soft, continuum arm with velocities 10 times larger and accelerations 40 times larger than those of previous work and did so for high-deflection shapes with more than 110° of curvature. We leveraged a data-driven learning approach for modeling, based on Koopman operator theory, and we introduce the concept of the static Koopman operator as a pregain term in optimal control. Our approach is rapid, requiring less than 5 min of training; is computationally low cost, requiring as little as 0.5 s to build the model; and is design agnostic, learning and accurately controlling two morphologically different soft robots. This work advances rapid modeling and control for soft robots from the realm of quasi-static to inertial, laying the groundwork for the next generation of compliant and highly dynamic robots.