Rooholla Khorrambakht
Robotics Research (United States), K.N.Toosi University of Technology, New York University
Papers
8
Total Citations
83
H-Index
4
About
Rooholla Khorrambakht is a rising star in robotics and control theory, whose work centers on safety-critical navigation and perception-driven autonomy. His primary research areas include control barrier functions (CBFs), differentiable optimization, and visual servoing, with a focus on enabling robots to operate safely in dynamic, unstructured environments. Khorrambakht’s major contributions lie in developing novel CBF frameworks that leverage differentiable optimization to handle complex collision avoidance—for static and dynamic obstacles alike—while ensuring real-time feasibility. His 2023 paper on safe navigation using differentiable optimization-based CBFs has garnered 37 citations, establishing a foundation for subsequent advances. Notably, his 2024 work on occlusion-free visual servoing (21 citations) addresses the critical challenge of maintaining visibility of moving targets, bridging control and perception. He has also pioneered point cloud-based CBFs for unstructured settings and tackled underactuated cable-driven robot localization. With over 80 total citations and a string of recent publications (2023–2025), Khorrambakht is shaping the next generation of safety-assured robotic systems, making his research essential for students and engineers pursuing robust autonomy.
Research Focus
Key Achievements
Top Papers
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- 5Preintegrated IMU Features For Efficient Deep Inertial Odometry.3 citations · 2020
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