Xueliang Sheng

University of Science and Technology of China

Papers

Total Citations

H-Index

About

Xueliang Sheng is a researcher focused on advancing intelligent robotics through deep learning, particularly in the domains of target recognition and localization. Their most-cited work, "Target Recognition and Location Based on Deep Learning" (2020), addresses a critical bottleneck in robotics: bridging the gap between human-level cognitive perception and machine capability in dynamic environments. By applying deep neural networks to enhance how robots identify and spatially locate objects, Sheng’s research contributes to smarter automation across smart homes, agriculture, industry, and office settings. While their citation count is still growing—reflecting an emerging career—this foundational paper has laid important groundwork for integrating artificial intelligence with real-world robotic systems. Sheng’s work underscores a commitment to solving practical challenges in human-robot interaction, aiming to elevate machines from simple tool-users to context-aware agents. As the field of embodied AI accelerates, Sheng’s contributions offer a stepping stone toward more autonomous, perceptive robots capable of navigating complex, unstructured environments.