Hamed Habibi
Papers
5
Total Citations
43
H-Index
4
About
Hamed Habibi is a rising researcher in robotics and control systems, whose work bridges the gap between theoretical guarantees and practical deployment. His primary research areas include adaptive and robust control, prescribed performance control, and learning from demonstration (LfD) for industrial manipulators. Habibi’s major contributions lie in developing control frameworks that ensure both transient and steady-state trajectory tracking under uncertainties—such as unknown control gains and parametric variations—using techniques like finite-time sliding mode and barrier Lyapunov functions. Notably, his 2023 paper on prescribed performance control with assigned settling time has garnered 12 citations, while his 2024 roadmap for LfD in manufacturing (10 citations) provides a structured, practitioner-focused guide for integrating human demonstration into robotic tasks, addressing the shift from mass production to mass customization. He has also explored deep reinforcement learning for motion control of multi-rotor aerial robots in additive manufacturing, showcasing versatility across ground and aerial platforms. With over 40 total citations and a clear trajectory toward impactful, application-driven research, Habibi is establishing himself as a key voice in making advanced robotics accessible and reliable for real-world manufacturing.
Research Focus
Key Achievements
Top Papers
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