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About

Paul Yanik is a robotics and human-computer interaction researcher whose work spans continuum robotics, gesture-based interfaces, assistive technology, and the emerging field of architectural robotics. His most-cited contribution, "Robot Tendrils" (2017, 52 citations), introduced long, thin continuum robots capable of navigating constrained environments unsuitable for traditional rigid-link systems, with applications in space inspection operations. Yanik has made significant strides in intuitive human-machine interfaces, developing gesture recognition systems leveraging Kinect depth data and growing neural gas algorithms to enable natural robot control — work that has collectively garnered over 60 citations. His research consistently emphasizes accessibility, exploring gaze estimation and gesture learning to extend robotic assistance to individuals with limited mobility. Yanik has also applied deep learning to practical societal challenges, with his CNN-based trash and recycling identification system (2020, 35 citations) contributing to smart city infrastructure development. Perhaps most distinctively, he has championed the interdisciplinary concept of "architectural robotics" — rethinking built environments as intelligent, robotic ecosystems that support healthcare, aging-in-place, and human well-being — bridging engineering, design, and social science in ways that have inspired cross-disciplinary graduate education.