Loubna Ourabah
Papers
2
Total Citations
21
H-Index
2
About
Loubna Ourabah is a rising researcher at the intersection of robotics, artificial intelligence, and algorithmic optimization. Her primary research areas include robotic manipulation, reinforcement learning, and path planning, where she focuses on bridging the gap between theoretical machine learning models and practical robotic applications. Ourabah’s most impactful contribution is her comprehensive 2023 review paper on reinforcement learning for robotic grasping, which has already garnered 13 citations. This work critically analyzes over 100 studies on Deep Neural Networks and RL techniques, offering actionable recommendations for improving robotic dexterity—a foundational challenge in automation and manufacturing. In 2024, she extended her expertise to algorithmic efficiency with a comparative study of DFS, BFS, and A* search algorithms for maze navigation, earning 8 citations for its clear evaluation of path cost and computational complexity. By synthesizing complex AI methods with real-world robotic tasks, Ourabah’s work provides valuable guidance for students and engineers seeking to develop more intelligent, adaptive robotic systems. Her growing citation record underscores her emerging influence in the field.
Research Focus
Key Achievements
Top Papers
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