Mostafa Othman
Papers
1
Total Citations
4
H-Index
1
About
Mostafa Othman is a researcher advancing the frontier of intelligent robotic manipulation, with a primary focus on integrating deep learning with sensor-based control systems. His most cited work, "Robotic Pick and Assembly Using Deep Learning and Hybrid Vision/Force Control" (2021, 4 citations), introduces a novel approach to handling featureless cylindrical objects in cluttered environments. By combining YOLO-based object detection with hybrid visual and force feedback, Othman enables robots to reliably identify and grasp objects even amidst visual noise, then execute precise assembly tasks. This contribution addresses a critical bottleneck in industrial automation: the need for robots to adapt to unstructured, real-world conditions rather than controlled settings. His work demonstrates how deep learning can bridge the gap between perception and physical interaction, making robotic systems more robust and versatile. Though early in his citation impact, Othman’s research holds significant promise for manufacturing, logistics, and collaborative robotics, where reliable pick-and-place operations are essential. His integration of vision and force control represents a practical step toward more autonomous, human-like robotic dexterity.
Research Focus
Key Achievements
Top Papers
- 1