Hiba Sekkat
Papers
4
Total Citations
92
H-Index
4
About
Hiba Sekkat is a rising researcher at the forefront of intelligent robotics, specializing in the integration of reinforcement learning, computer vision, and autonomous manipulation. Her most impactful work, a 2021 study on vision-based robotic arm control using deep reinforcement learning (66 citations), pioneered a deep deterministic policy gradient algorithm for autonomous object grasping, enabling robots to work more intuitively alongside humans. She further established herself as a thought leader with a comprehensive 2023 review of over 100 papers on reinforcement learning for robotic grasping, offering critical analysis and recommendations that have become a key reference in the field (13 citations). Beyond manipulation, Sekkat has advanced path planning through a comparative study of DFS, BFS, and A* search algorithms (2024, 8 citations), and is pushing the boundaries of simulation-to-reality transfer with her development of an open-source digital twin for the Pepper humanoid robot using ROS 2 (2024, 5 citations). Her work consistently bridges the gap between theoretical machine learning and practical robotic deployment, making her a notable voice in next-generation autonomous systems.
Research Focus
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Top Papers
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