Papers
11
Total Citations
192
H-Index
5
About
Omey M. Manyar is a robotics and manufacturing automation researcher whose work sits at the intersection of intelligent robot control, human-robot collaboration, and advanced manufacturing processes. His most recognized contributions focus on applying machine learning and adaptive control to industrial robotic tasks, including surface finishing, assembly, and manipulation. His 2019 work on virtual verification of weld seam removal using deep learning — garnering 97 citations — demonstrated how computer vision could enable in-process quality assurance in robotic grinding, a significant step toward autonomous manufacturing. His 2020 framework for impedance-controlled robotic polishing (54 citations) further showcased his ability to bridge control theory with real-world industrial demands. More recently, Manyar has expanded into learning-from-demonstration methodologies, developing inverse reinforcement learning frameworks that transfer human task-sequencing expertise to robots in high-mix manufacturing environments. His research on human-robot collaboration, grasp planning for composite layup, screw-driving under uncertainty, and in-space robotic assembly reflects a broad and forward-looking research agenda. With over 190 citations accumulated across a relatively compact body of work, Manyar is establishing himself as an impactful voice in intelligent industrial robotics and flexible automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2An adaptive framework for robotic polishing based on impedance control54 citations · 2020
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- 7Autonomous Execution of Insertion Operations in Space Assembly Tasks3 citations · 2025
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- 9Toolpath Generation for Robot Filleting3 citations · 2019
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