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About

Sang-Duck Lee is a leading researcher in human-robot collaboration, with a focus on making industrial robots safer, more affordable, and easier to teach. His primary research areas include sensorless collision detection, direct robot teaching, and the mechanical design of collaborative robot arms. Lee’s most influential work, a 2015 paper on sensorless collision detection for safe human-robot collaboration (64 citations), introduced a method to detect collisions without expensive skin or torque sensors, significantly lowering the barrier to safe automation. He further advanced intuitive human-robot interaction with a 2016 study on torque control-based sensorless hand guiding for direct robot teaching (38 citations), enabling operators to guide robots by hand without costly sensor arrays. In mechanical design, Lee’s 2017 paper on a 6-DOF collaborative robot arm with counterbalance mechanisms (21 citations) proposed spring-based systems to reduce motor power needs, cutting costs while maintaining performance and safety. His work on collision detection indices for redundant manipulators (2013, 14 citations) and handling model uncertainties in humanoid arms (2015, 7 citations) rounds out a career dedicated to practical, sensor-free solutions that advance the field of collaborative robotics.