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About
Jerome Susgin is a researcher focused on bridging the sim-to-real gap in robotic manipulation, with a particular emphasis on perception challenges involving transparent objects. His work addresses a critical bottleneck in robotics: enabling machines to accurately interpret and interact with everyday items like glass and plastic bottles. Susgin’s most cited paper, "A Pipeline for Transparency Estimation of Glass and Plastic Bottle Images for Neural Scanning" (2025), introduces a novel method for estimating the alpha value of transparent pixels. This contribution is essential for rendering realistic novel views of common objects, allowing neural scanning systems to better simulate real-world conditions. By tackling the opacity of transparent materials, his research directly improves the fidelity of synthetic training data, which is vital for reinforcement learning and robot grasping tasks. Though early in his career, Susgin’s work has already garnered attention for its practical approach to a notoriously difficult problem. His pipeline promises to accelerate progress in robotic perception, making it easier for machines to handle the complex, reflective, and translucent objects that populate human environments.
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