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About

Samuel Adebayo is a rising researcher at the forefront of human-robot interaction (HRI), specializing in the critical challenge of enabling machines to understand human intent. His work sits at the intersection of computer vision, gaze estimation, and collaborative robotics. Adebayo’s most significant contribution is his pioneering approach to intention inference, where he develops systems that allow robots to predict human actions during collaborative tasks, making interactions more natural and safe. His 2022 paper on "Hand-Eye-Object Tracking for Human Intention Inference" (11 citations) lays the groundwork for this, while his latest work, "SLYKLatent" (2025, 5 citations), introduces a novel self-supervised learning framework that dramatically improves gaze estimation accuracy—a key component for reading intent. To accelerate research in this domain, Adebayo created the QUB-PHEO dataset (2024, 4 citations), a rich, dyadic multi-view resource designed specifically for studying intention inference in assembly operations. Though early in his career, Adebayo’s focused contributions are establishing him as a key voice in building the perceptive, collaborative robots of the future.