Amir Haddadi
Papers
7
Total Citations
409
H-Index
7
About
Amir Haddadi is a leading researcher in human-robot collaboration and robotic contact dynamics, whose work bridges intuitive communication and physical interaction for industrial applications. His major contributions center on developing natural gestural communication lexicons for human-robot teamwork in manufacturing, derived from observing human workers—a paradigm that enhances safety and efficiency without requiring programming expertise. His highly cited 2013 paper on this topic (106 citations) established foundational methodology for intuitive human-robot turn-taking using nonverbal cues. In parallel, Haddadi pioneered real-time estimation of Hunt-Crossley dynamic contact models, advancing robotic manipulation in unknown environments. His 2012 paper (95 citations) demonstrated superior physical consistency over classical linear models, while his 2008 work (49 citations) introduced novel online parameter estimation methods. His research on contact impedance identification (37 citations) further optimized algorithms for convergence speed and noise resilience. Haddadi’s work has profound implications for Industry 4.0, enabling robots that both understand human gestures and adapt to physical contact with unprecedented accuracy—a dual contribution that positions him at the forefront of intelligent, collaborative manufacturing systems.
Research Focus
Key Achievements
Top Papers
- 1Gestures for industry Intuitive human-robot communication from human observation106 citations · 2013
- 2
- 3
- 4
- 5Online Contact Impedance Identification for Robotic Systems37 citations · 2008
- 6Identifying nonverbal cues for automated human-robot turn-taking27 citations · 2012
- 7Analysis of task-based gestures in human-robot interaction13 citations · 2013