Makram Chahine
Papers
6
Total Citations
210
H-Index
4
About
Makram Chahine is a robotics researcher whose work lies at the intersection of safe learning, autonomous navigation, and multi-agent coordination. He is best known for developing BarrierNet, a method that integrates differentiable control barrier functions into neural network training, enabling end-to-end learning with formal safety guarantees for robotic control—a contribution that has already garnered 99 citations. Chahine has also advanced the robustness of visual navigation with liquid neural networks, demonstrating that autonomous agents can generalize out-of-distribution to unseen environments (79 citations). His practical impact extends to real-time object tracking and following with the open-source “Follow Anything” system, which enables robots to detect, track, and follow any object in real-time, with applications spanning logistics, healthcare, and security. In multi-agent motion planning, Chahine has introduced game-theoretic approaches that model interactive behavior without requiring a priori knowledge of other agents’ objectives, improving scalability and realism. With a growing citation record and contributions to both theoretical safety and deployed robotic systems, Chahine is a rising figure in safe, generalizable, and interactive robot autonomy.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robust flight navigation out of distribution with liquid neural networks79 citations · 2023
- 3Follow Anything: Open-Set Detection, Tracking, and Following in Real-Time20 citations · 2024
- 4
- 5
- 6