Doganay Sirintuna
Italian Institute of Technology, Koç University, University of Genoa
Papers
10
Total Citations
185
H-Index
8
About
Doganay Sirintuna is an emerging robotics researcher whose work sits at the intersection of physical human-robot interaction (pHRI), collaborative manipulation, and adaptive control. His research primarily advances how humans and robots can work together seamlessly in manufacturing, assistive, and everyday environments, with a particular focus on making such collaboration intuitive, safe, and robust. Sirintuna's most-cited contribution — an object deformation-agnostic framework for human-robot collaborative transportation (2023, 53 citations) — elegantly addresses the real-world challenge of co-carrying objects with unpredictable physical properties, fusing haptic and motion capture signals to enable fluid human-robot teaming. His early work on admittance and variable fractional-order controllers for cobots (2020, 30 and 22 citations respectively) established strong theoretical foundations for compliant robot behavior during shared tasks. Beyond manipulation, Sirintuna has expanded into assistive robotics, developing navigation frameworks for visually impaired individuals and reactive pushing strategies for mobile robots operating in uncertain environments. His use of EMG-based neural networks to decode human motion intention (21 citations) reflects a broader commitment to anticipatory, human-centered robot design. Collectively, his publications — accumulating over 185 citations — demonstrate a researcher consistently translating sophisticated control theory into practical, people-first robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Towards collaborative drilling with a cobot using admittance controller30 citations · 2020
- 3A Variable-Fractional Order Admittance Controller for pHRI22 citations · 2020
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