Mai-Ngoc Dau
Papers
1
Total Citations
7
H-Index
1
About
Mai-Ngoc Dau is a researcher specializing in the intersection of deep learning, control systems, and teleoperation, with a focus on enhancing remote robotic manipulation. Her most-cited work, "Deep learning-based smith predictor design for a remote grasping control system" (2022, 7 citations), addresses critical challenges in time-delayed teleoperation by integrating neural network approaches with classical Smith predictor architectures. This contribution improves stability and precision in remote grasping tasks, which is vital for applications in hazardous environments, telesurgery, and space robotics. Dau’s research demonstrates a practical fusion of artificial intelligence and control theory, offering robust solutions for real-world latency issues. While her citation count is still growing, her work has been recognized for its innovative approach to bridging deep learning with traditional control methods, laying groundwork for more adaptive and intelligent teleoperation systems. Her achievements highlight a promising trajectory in advancing human-robot interaction and autonomous control.
Research Focus
Key Achievements
Top Papers
- 1