Papers
2
Total Citations
5
H-Index
2
About
Dr. Abdelhak Ouanane is a robotics researcher whose work focuses on the intersection of neural network control and legged locomotion. His primary research areas include visual servoing, deep learning for system identification, and force sensing for quadruped robots. In his most cited work, "LSTM-Based Visual Control for Complex Robot Interactions" (2023, 3 citations), Dr. Ouanane pioneers the use of Long Short-Term Memory (LSTM) recurrent neural networks to replace computationally intensive interaction matrix calculations in visual control laws, offering a more efficient approach for complex robotic tasks. His earlier contribution, "Measuring ground reaction forces of quadruped robot" (2022, 2 citations), addresses a fundamental challenge in legged robotics by developing a novel sensing system using two parallelepiped elastic elements to measure orthogonal ground reaction force components. This work is critical for enabling better environmental interaction and stability in quadruped locomotion. Though early in his career, Dr. Ouanane’s research demonstrates a clear trajectory toward integrating machine learning with practical robotic control challenges, promising significant advances in autonomous, adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1LSTM-Based Visual Control for Complex Robot Interactions3 citations · 2023
- 2Measuring ground reaction forces of quadruped robot2 citations · 2022