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About

Robert B. Kelley is a pioneering robotics researcher whose career has been defined by solving one of industrial automation's most persistent challenges: enabling robots to intelligently perceive, locate, and handle unstructured workpieces. His foundational work in bin-picking — developing robot systems capable of acquiring randomly oriented cylindrical and general workpieces from bins — established early proof-of-concept demonstrations that shaped an entire generation of industrial robotics research. Beginning in the early 1980s, Kelley advanced vision-based pose estimation techniques, including the influential "feature points method," and explored matched filters and multiple vision algorithms for workpiece recognition, collectively accumulating hundreds of citations across the field. His contributions extend into 3D spatial reasoning, where his spherical-object representation framework and successive spherical approximation scheme provided computationally efficient solutions for collision detection and distance computation — work that remains relevant to modern motion planning. Kelley further enhanced robotic dexterity through research on general-purpose gripper design and infrared proximity sensor feedback, improving real-world precision and adaptability. Spanning over two decades of consistent innovation, his body of work represents a comprehensive, practical foundation for intelligent robotic manipulation in unstructured industrial environments.