Papers
9
Total Citations
93
H-Index
5
About
Walid Gomaa is a leading researcher in robotics and artificial intelligence, with a focus on humanoid robots, motion planning, and autonomous navigation. His work bridges the gap between human-robot interaction and intelligent decision-making, particularly through real-time human motion imitation using Kinect sensors—a contribution that has garnered 25 citations. Gomaa has made significant strides in vision-based SLAM (Simultaneous Localization and Mapping) for humanoid robots, authoring a highly cited survey that guides the design of VSLAM systems for mapping unknown environments. His research extends to WiFi-based localization for mobile robots, employing random forests and Gaussian process latent variable models to enhance indoor positioning accuracy. Gomaa has also pioneered complex motion planning for NAO humanoid robots, enabling whole-body motions like climbing stairs and stepping over obstacles using only onboard sensing. His work on aggregate reinforcement learning for multi-agent territory division, as demonstrated in the Hide-and-Seek game, showcases his expertise in adaptive AI. More recently, he has explored meta-reinforcement learning for robust robotic assembly tasks and applied Fast Fourier Transform methods to accident detection. With over 90 citations across his top papers, Gomaa’s contributions are shaping the future of autonomous, human-like robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Vision based SLAM for humanoid robots: A survey24 citations · 2013
- 3WiFi Localization for Mobile Robots Based on Random Forests and GPLVM12 citations · 2014
- 4
- 5Complex Motion Planning for NAO Humanoid Robot9 citations · 2014
- 6
- 7NAO humanoid robot motion planning based on its own kinematics3 citations · 2014
- 8
- 9Fast Fourier Transform based Method for Accident Detection2 citations · 2022