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About

Aaron Staranowicz is a researcher whose work sits at the intersection of robotics, computer vision, and assistive healthcare technology. His primary contributions lie in sensor calibration and simulation, with a particular focus on RGB-D cameras—the affordable depth-sensing devices that have revolutionized robotic perception. Staranowicz’s most cited work, a comprehensive survey of robotic simulators (100 citations), established a critical resource for researchers evaluating tools for safe human-robot interaction. He advanced the field of sensor accuracy through a practical method for calibrating RGB-D cameras using spheres (50 citations), a technique that improved the reliability of depth data in assistive environments. His translational impact is evident in his work on gait monitoring (37 citations), where he evaluated a mobile Kinect-based system for fall prediction in older adults—a pressing clinical challenge. By systematically comparing calibration methods for Kinect-style cameras (14 citations) and ego-motion algorithms for teleoperated robotic endoscopes (8 citations), Staranowicz has helped bridge the gap between low-cost sensors and high-stakes applications in medicine and elder care. His research continues to enable safer, more accurate robotic systems that work alongside humans.