Leixian Qiao
Papers
1
Total Citations
2
H-Index
1
About
Leixian Qiao is a researcher whose work lies at the intersection of computer vision, robotics, and human-robot interaction, with a particular focus on RGB-D object recognition and interactive learning. Qiao’s major contribution addresses a critical gap in traditional object recognition: the neglect of human involvement in the learning process. In their 2016 paper, "RGB-D Object Recognition from Hand-Held Object Teaching," Qiao proposed a novel framework that leverages human interaction for object segmentation and concept learning, enabling robots to acquire knowledge directly from hand-held object demonstrations. This approach not only improves recognition accuracy but also facilitates the transfer of learned knowledge to general indoor scenes, making it highly practical for real-world robotic applications. While the paper has garnered 2 citations, its conceptual foundation is significant for advancing interactive machine learning paradigms. Qiao’s work underscores the importance of integrating human guidance into autonomous systems, paving the way for more intuitive and adaptable robots. This research is particularly valuable for students and researchers exploring human-centered AI, embodied cognition, and the future of collaborative robotics.
Research Focus
Key Achievements
Top Papers
- 1RGB-D Object Recognition from Hand-Held Object Teaching2 citations · 2016