Tinne De Laet
Papers
27
Total Citations
619
H-Index
10
About
Tinne De Laet is a leading researcher in robotics and artificial intelligence, whose work bridges the gap between complex sensor-based robot systems and intelligent, autonomous behavior. Her primary research areas include constraint-based task specification, multi-sensor integration, and probabilistic modeling for robotic manipulation and fault detection. De Laet is best known for pioneering the iTASC framework, a systematic constraint-based approach that unifies task specification and geometric uncertainty estimation for sensor-based robots—a contribution that has garnered over 263 citations and remains foundational in the field. She has also made significant advances in Bayesian nonparametric time-series models for continuous fault detection in industrial robotic tasks, enabling robots to learn sensor signatures and recognize anomalies during assembly operations. Her work extends into probabilistic logic programming for hybrid relational domains, with applications in state estimation for dynamic environments. With over 500 total citations across her most influential papers, De Laet’s research has had a lasting impact on robot programming, making it more systematic, reusable, and accessible. Her achievements include developing domain-specific languages for rapid application development in robotics and standardizing geometric relations between rigid bodies, solidifying her reputation as a key innovator in autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3iTASC: A Tool for Multi-Sensor Integration in Robot Manipulation46 citations · 2009
- 4Probabilistic logic programming for hybrid relational domains38 citations · 2016
- 5iTASC: a tool for multi-sensor integration in robot manipulation35 citations · 2008
- 6A particle filter for hybrid relational domains34 citations · 2013
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