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About

Nathaniel Hanson is a robotics researcher whose work sits at the intersection of multimodal sensing, robot perception, and soft robotics. His research focuses on equipping robots with richer environmental understanding through hyperspectral imaging, spectroscopy, and novel sensor integration — enabling machines to perceive material properties, classify terrain, and navigate complex, unstructured environments far more effectively than traditional vision-based systems allow. Hanson's most-cited contribution, "VAST" (2022, 20 citations), tackles the difficult problem of terrain classification under real-world conditions, combining visual and spectral data to handle multi-class, poorly lit, and ambiguous environments. His hyperspectral work extends across multiple papers, including the "Hyper-Drive" dataset (2023) and the "HyperBot" benchmarking testbed (2022), establishing foundational infrastructure for the broader research community. His spectroscopic sensing work — spanning liquid identification, in-hand object recognition with soft grippers, and pre-grasp material classification — demonstrates a consistent vision of robots that understand *what* they touch and handle, not just *where* objects are. Beyond sensing, Hanson has contributed to soft robotics through origami-inspired pneumatic actuators with proprioceptive feedback and field-deployable vine robots for urban search and rescue. With over 95 cumulative citations across just a few years of publication, his work is making a tangible mark on the future of intelligent, perceptually rich robotic systems.