Ömer Kurkutlu 🤖

Ömer Kurkutlu

PhD Candidate in Electrical and Computer Engineering

University of Illinois Chicago

Professional Summary

I am a Ph.D. candidate in Electrical and Computer Engineering at the University of Illinois Chicago, advised by Dr. Arman Roohi. I specialize in robot learning, Physical AI, and autonomous systems. My research focuses on deep reinforcement learning, vision-language-action models, vision-based navigation, sim-to-real, and resource-constrained robotics, with hands-on experience in NVIDIA Isaac Sim/Isaac Lab, ROS/ROS2, and embedded AI.
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Education

PhD in Electrical and Computer Engineering

2024-08-01

University of Illinois Chicago

PhD in Electrical Engineering

2023-08-01
2024-08-01

University of Notre Dame

Bachelor of Engineering in Computer Engineering

2015-08-01
2021-05-01

University of Pune

Interests

Robot Learning & Physical AI Deep Reinforcement Learning Vision-Language-Action Models Vision-Based Navigation Sim-to-Real Transfer Tiny Drones & Micro Robots TinyML & Edge AI
📚 My research

I work on robot learning and Physical AI for autonomous systems. My research focuses on deep reinforcement learning, vision-language-action (VLA) models, vision-based navigation, and sim-to-real transfer for resource-constrained robots — from fully onboard navigation on palm-sized Crazyflie drones to multimodal policies for embodied robotics.

I build simulation-to-deployment pipelines with NVIDIA Isaac Sim/Isaac Lab, ROS/ROS2, and embedded AI, and I am the developer of the open-source Crazyflie Isaac Framework (CIF).

🎤 Research presentations
  • IROS 2026 Late-Breaking Results — Lightweight 3D-Aware Navigation for Tiny Drones in Dynamic Mapless Environments, poster presentation, Sep. 2026.
  • DAC 2026 Young Fellows Program — Tiny Brain, Big World: Autonomous MCU-Friendly Navigation for Mapless Dynamic Environments, poster presentation, Design Automation Conference, 2026.
  • IROS 2023 Late-Breaking Results — Investigating the Impact of Spinal Joint Dynamics on a Sprawling Robot Using Deep Reinforcement Learning, poster presentation, 2023.