Papers
7
Total Citations
28
H-Index
4
About
Khansa Rekik is a leading researcher at the forefront of human-robot collaboration (HRC), with a focus on making industrial robotics more intuitive, adaptive, and intelligent. Her work spans key areas including deep learning for intention recognition, augmented and virtual reality interfaces, digital twins, and multimodal interaction. Rekik’s major contributions include developing a multi-perspective augmented video interface that allows operators to command robots through visual feeds, and creating communication-free collaboration approaches for complex tasks like aircraft riveting using AI probabilistic planning. She has also pioneered predictive intention recognition models that overcome classical limitations by anticipating human actions through spatio-temporal deep learning, and has introduced LLM-enhanced multimodal frameworks for assembly tasks. Her research on bin-picking combines 3D object recognition with CAD models to solve fundamental industrial challenges. With papers accumulating citations across top venues, Rekik’s work is shaping the future of distributed, collaborative manufacturing—enabling seamless human-robot teamwork in shared production environments through digital twins and remote VR control. Her innovative approaches are directly addressing the pressing need for flexible, safe, and efficient automation in large-scale industries.
Research Focus
Key Achievements
Top Papers
- 1
- 2Human intention and workspace recognition for collaborative assembly6 citations · 2022
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- 6Digital Twins for Distributed Collaborative Work in Shared Production3 citations · 2022
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