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About

Hooman Lee is a leading researcher in robot teaching and human-robot interaction, with a focus on dual-arm manipulators and intuitive programming methods. His work addresses critical challenges in industrial robotics, particularly the difficulty of teaching synchronized motions for multi-arm systems. Lee’s major contributions include developing a comprehensive categorization framework for robot teaching methods—presented in his highly cited 2013 survey (30 citations)—which provides a structured taxonomy that researchers continue to use as a foundational reference. He pioneered master-slave teleoperation systems using exoskeletal devices, enabling operators to naturally demonstrate complex dual-arm tasks. His 2014 paper on this topic (21 citations) and subsequent work (2017, 17 citations) demonstrate how exoskeleton-based teaching can replace cumbersome teach pendants. Lee also contributed to active sensing strategies for contact localization without tactile sensors (2013, 14 citations), expanding robot perception capabilities. His research has been recognized for bridging the gap between human demonstration and industrial robot programming, with cumulative citations exceeding 87. Lee’s work is particularly notable for its practical impact on manufacturing, where dual-arm robots are increasingly essential for assembly tasks requiring coordination and dexterity.