Jie Sheng

Papers

Total Citations

H-Index

About

Jie Sheng is a researcher whose work sits at the intersection of networked control systems, robotics, and artificial intelligence. Their most notable contribution lies in the development of Internet-based teleoperation frameworks, particularly a remote control scheme that leverages Adaptive Linear (Adaline) neural networks to enable robust human-robot interaction over the web. This work addressed a fundamental challenge in networked robotics: the unpredictable and variable nature of Internet communication delays. By employing Adaline neural networks to estimate concurrent roundtrip delays in real time, Sheng's approach enabled intelligent, dynamic task allocation between human operators and robotic systems, optimizing performance despite latency uncertainties. This contribution was notably ahead of its time, anticipating the growing importance of cloud robotics and remote operation that would become central themes in the field years later. Published in 2003, the work reflects an early and forward-thinking integration of machine learning techniques into teleoperation infrastructure. While the citation count remains modest at 2, the conceptual significance of bridging adaptive neural computation with Internet-based control systems marks Sheng as a pioneering contributor to what has since evolved into a critical area of modern robotics research.