Iman Salehi
Papers
8
Total Citations
109
H-Index
6
About
Iman Salehi is a robotics and control systems researcher whose work sits at the intersection of human-robot collaboration, machine learning, and safety-constrained control. With a cumulative citation count exceeding 100 across his published works, Salehi has established himself as a thoughtful contributor to the challenges of deploying robots safely alongside humans in real-world manufacturing environments. His most influential contribution, "Human-in-the-Loop Robot Control for Human-Robot Collaboration" (2020, 48 citations), addresses the critical challenge of estimating human intention in real time to enable safe and efficient collaborative robotics. This work, complemented by his research on fusing pupil and hand motion data for intention inference, demonstrates a holistic approach to understanding human behavior in shared workspaces. Salehi has also made notable advances in visual servoing, particularly through barrier function-based constrained image control and the integration of dynamic movement primitives with visual feedback. A recurring theme across his research is the use of Extreme Learning Machines and barrier certificates to guarantee the safety and stability of learned dynamical systems — bridging data-driven modeling with formal control-theoretic guarantees. His body of work offers both theoretical rigor and practical relevance for the next generation of intelligent, human-aware robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Constrained Image-Based Visual Servoing using Barrier Functions16 citations · 2021
- 3Stitching Dynamic Movement Primitives and Image-Based Visual Servo Control13 citations · 2022
- 4
- 5Human Intention Estimation using Fusion of Pupil and Hand Motion7 citations · 2020
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- 8