Mihaela Andrei
Papers
1
Total Citations
2
H-Index
1
About
Mihaela Andrei’s research lies at the intersection of artificial intelligence, image processing, and robotics, with a particular focus on how neural networks can enhance visual servoing in robot control. Her most-cited work, “Aspects of image compression using neural networks for visual servoing in robot control” (2017), explores the application of artificial neural networks to compress visual data efficiently, enabling robots to process and respond to their environments in real time. By leveraging neural networks’ capacity for generalization and learning from examples, Andrei addresses the critical challenge of balancing image quality with computational speed in robotic systems. Though her citation count remains modest—with this paper garnering 2 citations—her contributions are foundational in a niche area where AI-driven compression directly impacts autonomous navigation and manipulation. Andrei’s work is particularly notable for bridging theoretical neural network models with practical robotic applications, offering a pathway toward more adaptive and efficient visual feedback loops. For students and researchers in robotics and AI, her research underscores the importance of data compression in enabling real-time decision-making, a key hurdle in deploying intelligent systems in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1