Olfa Nasraoui
Papers
6
Total Citations
43
H-Index
3
About
Olfa Nasraoui is a leading researcher at the intersection of explainable artificial intelligence (XAI) and robotic autonomy, with a focused expertise in robotic grasp failure prediction. Her work addresses a critical bottleneck in human-robot collaboration: the lack of transparency in machine learning models used for real-time robotic control. Nasraoui’s major contributions include pioneering the use of machine learning—from traditional models to advanced deep learning sequence models—to predict whether a robot’s grasp will fail *before* it occurs, thereby enabling proactive error recovery and saving significant operational time. She has been instrumental in advancing beyond standard post-hoc explanations, developing innovative *pre-hoc* and local explainability frameworks that embed transparency directly into the model’s learning process. Her most cited paper (2020, 26 citations) established the foundational approach for robot failure mode prediction, while her subsequent work (2024–2025) has systematically optimized and compared explainability methods, achieving instance-level transparency. Nasraoui’s research is vital for building trust in autonomous systems, making her a key figure in the drive toward safe, interpretable, and reliable robotic intelligence.
Research Focus
Key Achievements
Top Papers
- 1Robot Failure Mode Prediction with Explainable Machine Learning26 citations · 2020
- 2Robot failure mode prediction with deep learning sequence models5 citations · 2024
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- 5Optimizing Local Explainability in Robotic Grasp Failure Prediction3 citations · 2025
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