Xilang Lu
Papers
1
Total Citations
19
H-Index
1
About
Xilang Lu is a pioneering researcher in human-robot collaboration, specializing in multi-scale graph convolutional neural networks and temporal attention mechanisms for action recognition. His most-cited work, "Human-Robot Collaboration Through a Multi-Scale Graph Convolution Neural Network With Temporal Attention" (2024, 19 citations), introduces a novel framework that enables collaborative robots to sense and interpret human movements and intentions using skeleton sequence data. This contribution addresses a critical challenge in robotics: achieving fluid, intuitive human-robot interaction by allowing machines to anticipate and respond to human actions in real time. Lu’s research bridges computer vision and robotics, leveraging advanced neural architectures to decode complex motion patterns. His work has significant implications for manufacturing, healthcare, and assistive technologies, where safe and efficient collaboration between humans and robots is paramount. With a focus on enhancing robot perception and responsiveness, Lu continues to push the boundaries of intelligent systems, making him a notable figure in the field of human-robot collaboration.
Research Focus
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Top Papers
- 1