Mohamad Saada
Papers
3
Total Citations
37
H-Index
2
About
Mohamad Saada is a robotics researcher whose work sits at the intersection of legged locomotion, embedded systems, and machine learning. His primary research areas include bipedal robot balance control, real-time road surface detection, and the application of deep reinforcement learning to physical systems. Saada’s most significant contribution is his development of a hybrid autonomous controller that combines deep reinforcement learning with pattern generators to help bipedal robots recover from abrupt pushes—a critical capability for human-robot collaboration in real-world environments. This work, published in 2021, has already garnered 18 citations, reflecting its relevance to the growing field of dynamic locomotion. In parallel, Saada has advanced practical sensing solutions, designing a Raspberry Pi-based system using recurrent neural networks to classify six types of road surfaces in real time, a contribution cited 17 times for its low-cost, deployable approach. His earlier research extended the Normalized Advantage Function algorithm to work with recurrent neural networks, enabling more efficient balance control with minimal power consumption. Saada’s work bridges the gap between theoretical reinforcement learning and tangible robotic hardware, making him a notable figure in the pursuit of more resilient and autonomous bipedal systems.
Research Focus
Key Achievements
Top Papers
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