Oualid Doukhi
Papers
6
Total Citations
107
H-Index
5
About
Oualid Doukhi is an emerging researcher specializing in autonomous robotics, deep reinforcement learning, and intelligent navigation systems for aerial and ground-based platforms. His work centers on enabling robots to operate safely and efficiently in complex, unstructured environments without relying on pre-built maps or explicit programming. Doukhi's most significant contributions lie in applying deep reinforcement learning to autonomous drone navigation. His 2021 paper on end-to-end local motion planning for aerial robots in unknown outdoor environments garnered 31 citations, demonstrating real-world viability of learned navigation policies. A closely related 2022 study on map-less flying robot navigation has accumulated 28 citations, further solidifying his expertise in sensor-driven, learning-based flight control. His earlier 2017 work on voice-enabled drone control, with 25 citations, highlights a broader interest in intuitive human-robot interaction. Beyond aerial systems, Doukhi has extended these principles to ground robotics, including LiDAR-equipped autonomous cars and last-mile delivery robots incorporating deep learning-based predictive control. His most recent work ventures into zero-shot action generation using vision-language models, signaling an exciting pivot toward foundation model integration in robotics. Across his portfolio, Doukhi consistently bridges theoretical machine learning advances with practical robotic deployment.
Research Focus
Key Achievements
Top Papers
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- 3Voice enabled smart drone control25 citations · 2017
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