Jack Rebman

Lord Corporation (United States), Duke University

Papers

Total Citations

H-Index

About

Jack Rebman is a pioneer in tactile sensing for robotics, with a career focused on enabling machines to perceive and manipulate objects through touch. His research spans tactile image processing, pattern recognition, and grasp stability. Rebman’s most influential work, “Edge detection in tactile images” (2005, 40 citations), adapts classic computer vision edge-detection algorithms to noisy tactile sensor data, employing 2D median filtering to clean tactile images. This foundational contribution helps robots identify object boundaries through contact, a critical step toward dexterous manipulation. Earlier, he explored trainable tactile pattern classifiers using back-error propagation networks (1992, 6 citations) and developed rapid transformation methods for low-resolution tactile data (1984, 7 citations). Rebman also investigated precursors of slip in robotic grasps (1987, 5 citations), conducting experiments with multi-modal contact sensing to detect early slip events—work that informs safer, more reliable grasping. Though his citation counts are modest, Rebman’s research laid essential groundwork for tactile sensing in robotics, bridging computer vision and haptics at a time when the field was nascent. His contributions remain relevant for researchers building touch-enabled robotic systems.