Shirine El Zaatari
Papers
6
Total Citations
541
H-Index
5
About
Shirine El Zaatari is a leading researcher in collaborative robotics, specializing in human-robot interaction and adaptive manufacturing systems. Her work centers on making industrial robots—or “cobots”—safer, smarter, and more intuitive for dynamic factory environments. El Zaatari’s most influential contribution is her comprehensive 2019 overview of cobot programming for collaborative tasks, which has accumulated 447 citations and serves as a foundational reference for the field. She has advanced Task-Parameterized Learning from Demonstrations (TP-LfD), developing methods like iTP-LfD and improved TP-LfD that enable cobots to adaptively generate paths for tasks such as pick-and-place in changing workspaces. Her 2021 work on ring Gaussian mixture modeling further refined cobot trajectory generation. El Zaatari also explores reinforcement learning to optimize cobot path planning, pushing toward fully autonomous collaboration. With over 540 total citations across her most-cited papers, her research directly addresses the core challenge of making industrial robots flexible enough for small-batch, high-variety manufacturing. Her achievements have positioned her as a key voice in the next generation of human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1Cobot programming for collaborative industrial tasks: An overview447 citations · 2019
- 2Optimised Learning from Demonstrations for Collaborative Robots33 citations · 2021
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- 5Ring Gaussian Mixture Modelling and Regression for collaborative robots12 citations · 2021
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