Mohamad Chehadeh

About

Mohamad Chehadeh is a researcher at the intersection of robotics, reinforcement learning, and aerial systems, with a focus on bridging the gap between simulation and real-world deployment. His most cited work, "Learning to Navigate Through Reinforcement Across the Sim2Real Gap" (2022), addresses a critical challenge in autonomous UAV navigation—training drones to operate safely and efficiently in populated environments by transferring policies learned in simulation to physical hardware. This contribution is pivotal for advancing the deployment of unmanned aerial vehicles in complex, human-centric settings. Chehadeh also explores applied aerial robotics in his work on "Aerial Firefighting System for Suppression of Incipient Cladding Fires" (2021), proposing a semi-autonomous UAV-based solution for tackling hard-to-reach high-rise building fires, a novel approach that could revolutionize emergency response. With over 5 citations across his key papers, Chehadeh’s research demonstrates tangible impact in both foundational machine learning and practical safety applications. His work is particularly notable for its potential to enhance the safety and autonomy of UAVs in critical infrastructure and public spaces.

Research Focus

Key Achievements

Top Papers

1. 1
   Learning to Navigate Through Reinforcement Across the Sim2Real Gap

   3 citations · 2022
2. 2
   Aerial Firefighting System for Suppression of Incipient Cladding Fires

   2 citations · 2021
3. 3
   Learning to Navigate Through Reinforcement Across the Sim2Real Gap

   2 citations · 2022

Key Collaborators

Contact & Links