Oussama Abdulhay

About

Oussama Abdulhay is a robotics researcher focused on bridging the critical gap between simulation and real-world deployment for autonomous systems, particularly unmanned aerial vehicles (UAVs). His primary research areas include reinforcement learning, Sim2Real transfer, and autonomous navigation in complex, populated environments. Abdulhay’s most cited work, "Learning to Navigate Through Reinforcement Across the Sim2Real Gap" (2022), tackles the fundamental challenge of training UAVs to operate safely and efficiently in real-world settings where they must interact with humans. By developing reinforcement learning frameworks that account for the discrepancies between simulated training environments and physical reality, he addresses stringent safety and security requirements for drones in civilian airspace. His contributions are vital as UAVs become increasingly integrated into daily life for delivery, surveillance, and emergency response. With accumulating citations from the robotics community, Abdulhay’s research is recognized for its practical impact on autonomous navigation, offering scalable solutions that reduce the need for costly real-world data collection while ensuring robust performance. His work stands at the intersection of machine learning and robotics, paving the way for safer, more reliable autonomous systems.

Research Focus

Key Achievements

Top Papers

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

   3 citations · 2022
2. 2
   Learning to Navigate Through Reinforcement Across the Sim2Real Gap

   2 citations · 2022

Key Collaborators

Contact & Links