Mark D. Wheeler

Papers

Total Citations

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About

Mark D. Wheeler is a pioneering researcher in human-robot interaction, with a focused expertise in hand action perception and automatic robot programming. His work centers on developing vision-based systems that allow robots to learn tasks through natural human demonstration, eliminating the need for specialized equipment like data gloves or markers. Wheeler’s major contribution lies in creating robust algorithms that interpret depth image sequences to recognize and replicate complex hand movements, enabling intuitive robot instruction. His seminal 2002 paper, "Hand action perception for robot programming," which has garnered 6 citations, presents a general approach where a human instructor simply demonstrates an assembly task in front of a vision system, and the robot autonomously learns the procedure. This work builds on his earlier 1999 study, "Hand Action Perception and Robot Instruction" (3 citations), which established the foundational concept of teaching robots through demonstration, akin to a teacher instructing a student. Wheeler’s research has significantly advanced the field of programming by demonstration, making robot training more accessible and efficient. His achievements underscore a vision where robots can seamlessly learn from human teachers, paving the way for more intuitive and collaborative human-robot environments in manufacturing, education, and beyond.