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About

Guillermo Larregay is a robotics researcher whose work sits at the intersection of computer vision, industrial automation, and intelligent manipulation. His primary research areas include autonomous robotic systems, grasp detection, and machine learning for manufacturing. Larregay’s major contributions center on enhancing the ability of industrial robot manipulators to perceive and interact with their environment. Notably, his most-cited work (14 citations) details the design of an autonomous chess-playing robot, integrating computer vision, an industrial-grade manipulator, and an open-source chess engine—a compelling demonstration of end-to-end mechatronic system design. He has further advanced the field by applying Convolutional Neural Networks (ConvNets) to robotic grasping, developing methods that enable robots to pick and reorient objects from assembly lines, a critical step toward flexible automation. His comparative study of classification algorithms for chess piece detection (2 citations) provides foundational insights for vision-based object recognition in constrained environments. Through these projects, Larregay has shown a consistent focus on bridging perception and action in robotics, with his work collectively cited over 26 times, laying groundwork for more adaptive and autonomous industrial robots.