Papers
1
Total Citations
2
H-Index
1
About
H. Zeng’s research centers on the intersection of robotics, machine learning, and intelligent systems, with a particular focus on performance evaluation and automation. Their most notable contribution is a pioneering study on cleaning robots, where they developed a machine learning-based evaluation method to assess cleaning performance. By analyzing factors such as stain area, color intensity, and moisture levels, Zeng built a stain recognition model that achieved a precision rate of 95.7% and a recall rate of 85.9%. This work, published in 2022, has garnered 2 citations and represents a significant step toward more autonomous and efficient household robotics. Zeng’s approach combines practical engineering challenges with advanced data-driven techniques, offering a framework that could be extended to other robotic applications. Their research not only advances the field of service robotics but also provides a replicable methodology for performance evaluation in automated systems. With a focus on real-world impact, Zeng continues to explore how machine learning can enhance the reliability and intelligence of robotic platforms.
Research Focus
Key Achievements
Top Papers
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