Ben Selby

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About

Ben Selby is a robotics researcher specializing in biologically inspired vision systems, with a focus on developing hardware that mimics the extraordinary capabilities of the primate visual system. His work bridges the gap between artificial and biological vision, addressing the computational constraints that limit mobile robots. Selby’s major contributions center on the design and implementation of saccading and accommodating robotic vision systems—technologies that replicate the rapid eye movements (saccades) and lens focusing (accommodation) that allow primates to efficiently process visual information using a fovea. His 2018 paper, “OREO: An Open-Hardware Robotic Head That Supports Practical Saccades and Accommodation,” with 7 citations, stands as his most influential work, offering an open-source platform for researchers to explore these dynamic vision capabilities. Earlier, his 2016 paper on saccading and accommodating robot vision (6 citations) laid the groundwork for this approach, while his 2014 modeling of the shape hierarchy for visually guided grasping (4 citations) connected neural encoding in the monkey anterior intraparietal area to robotic grasping. Selby’s work is notable for its practical, open-hardware ethos, making advanced vision systems accessible to the broader robotics community and pushing toward more agile, human-like robotic perception.