Papers
15
Total Citations
132
H-Index
7
About
Qilong Yuan is a robotics researcher whose work spans human-robot collaboration, robot programming, pose estimation, and teleoperation, with a particular focus on automating complex industrial manufacturing tasks. He is best known for pioneering robotic masking and taping systems — a notoriously tedious yet precision-demanding aerospace surface treatment process — developing automated path planning, tool design, and manipulation strategies that reduce reliance on manual labor in high-mix, low-volume production environments. His most cited work (36 citations) introduced a telemanipulation-based human-robot collaboration framework enabling operators to intuitively teach robots complex masking skills, addressing fundamental limitations of traditional offline programming. This was complemented by subsequent research on flexible, geometry-adaptive taping systems and wearable IMU-based teleoperation interfaces, demonstrating a consistent commitment to making robot teaching more accessible and responsive. Yuan also contributed to 6D object pose estimation from RGB-D imagery using Hough Forest methods, advancing robotic grasping in cluttered, occluded environments. His quantitative frameworks for assessing robotic task performance further reflect a rigorous, systems-level approach to robotics research. Collectively, his body of work — accumulating nearly 100 citations — offers meaningful advances at the intersection of industrial automation, human-robot interaction, and intelligent manipulation.
Research Focus
Key Achievements
Top Papers
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- 4Automatic robot taping: system integration10 citations · 2015
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- 6Strategy for robot motion and path planning in robot taping7 citations · 2016
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- 8Automatic robot taping: Auto-path planning and manipulation6 citations · 2015
- 9Task-orientated robot teleoperation using wearable IMUs5 citations · 2017
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