Shamil Mamedov
Papers
14
Total Citations
103
H-Index
4
About
Shamil Mamedov is a robotics researcher whose work bridges the critical gap between theoretical control and practical industrial application. His primary research areas include physical human-robot interaction, collision detection, compliance error compensation, and the challenging domain of deformable object manipulation. Mamedov’s most influential contribution, "Practical Aspects of Model-Based Collision Detection" (2020, 46 citations), provides essential guidance for implementing safe human-robot collaboration using proprioceptive sensors—a cornerstone for modern manufacturing. He has also advanced the control of robots with double encoders, enabling more precise external force detection and classification. In the realm of manufacturing precision, Mamedov developed reduced elastostatic models to compensate for compliance errors, directly improving the machining accuracy of industrial manipulators. His recent work pushes into frontier territory: learning interpretable dynamics of deformable linear objects from single trajectories and applying pseudo-rigid body networks. Notably, his 2024 paper on safe imitation learning of nonlinear model predictive control for flexible robots addresses the complex oscillatory dynamics that have long hindered flexible robot adoption. Through these contributions, Mamedov is shaping a future where robots are not only safer and more precise but also capable of handling the soft, flexible materials that dominate modern industry.
Research Focus
Key Achievements
Top Papers
- 1Practical Aspects of Model-Based Collision Detection46 citations · 2020
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- 3Compliance Error Compensation based on Reduced Model for Industrial Robots10 citations · 2018
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- 6Learning Deformable Linear Object Dynamics From a Single Trajectory3 citations · 2025
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