Tine Lefebvre
Papers
12
Total Citations
342
H-Index
8
About
Tine Lefebvre is a leading researcher in autonomous compliant motion and sensor-based robotics, whose work has fundamentally advanced how robots handle physical contact tasks under uncertainty. Her research focuses on force-controlled manipulation, Bayesian state estimation, and active sensing for robotic assembly. Lefebvre’s most influential contribution is her comprehensive survey on active compliant motion (86 citations), which established a foundational framework for robots to safely and stably perform contact tasks like polishing, assembly, and door opening. She pioneered the application of nonlinear Kalman filtering and Bayesian hybrid model-state estimation to force-controlled robot tasks, enabling simultaneous contact formation recognition and geometrical parameter estimation during compliant motion (60 citations). Her work on decision-making criteria for active robotic sensing (47 citations) and online statistical model recognition has been instrumental in developing autonomous systems that can cope with partially unknown environments and measurement noise. Lefebvre also contributed to unified constraint-based task specification for complex sensor-based systems and robot programming by human demonstration. Her research has laid critical groundwork for the next generation of robots that must operate robustly in uncertain, contact-rich environments.
Research Focus
Key Achievements
Top Papers
- 1Active compliant motion: a survey86 citations · 2005
- 2Nonlinear Kalman Filtering for Force-Controlled Robot Tasks64 citations · 2005
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- 6A roadmap for autonomous robotic assembly20 citations · 2002
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