Papers
15
Total Citations
180
H-Index
8
About
Qujiang Lei is a robotics researcher whose work spans two interconnected domains: robotic grasping of unknown objects and human-robot collaboration through gesture-based interaction. His most influential contribution, a 2019 survey on vision-based hand gesture recognition for human-robot collaboration (43 citations), has become a key reference for researchers navigating the growing field of intuitive robot control in shared manufacturing environments. Complementing this, his extensive work on unknown object grasping — including algorithms leveraging force balance optimization, principal component analysis, and C-shape grasping strategies — addresses one of robotics' fundamental challenges: enabling robots to reliably manipulate objects without prior models or appearance data. With multiple papers on this topic accumulating over 60 combined citations, Lei has systematically advanced both the speed and reliability of robotic grasping pipelines. His 2020 work on robot programming by demonstration further reflects his broader interest in making robots more accessible and intuitive to human operators. Across his body of work, Lei consistently bridges perception, manipulation, and human-machine interaction, offering practical, deployable solutions that position him as a thoughtful contributor to the advancement of collaborative and autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Fast grasping of unknown objects using force balance optimization21 citations · 2014
- 4Fast grasping of unknown objects using principal component analysis18 citations · 2017
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- 8Fast C-shape grasping for unknown objects9 citations · 2017
- 9
- 10Applications of hand gestures recognition in industrial robots: a review7 citations · 2019