Aude Billard
École Polytechnique Fédérale de Lausanne, École Normale Supérieure - PSL, University of Southern California, Centre for Artificial Intelligence and Robotics, University of Edinburgh, Computer Algorithms for Medicine, University of Washington, École Polytechnique, Laboratoire d'Informatique Fondamentale de Lille, Liaocheng University
Papers
268
Total Citations
17,636
H-Index
67
About
Aude Billard is a pioneering roboticist whose work sits at the intersection of machine learning, human-robot interaction, and robot manipulation. Based at EPFL's Learning Algorithms and Systems Laboratory, she has fundamentally shaped how robots learn from human demonstrations — a paradigm known as learning from demonstration (LfD) — making it one of the most influential approaches in modern robotics. Billard's most celebrated contributions include developing programming-by-demonstration frameworks that allow robots to generalize tasks across contexts (1,086 citations), and her foundational work on learning stable nonlinear dynamical systems using Gaussian Mixture Models (764 citations), which gave robots mathematically guaranteed, reliable motion reproduction. Her 2019 review of robot manipulation trends (935 citations) has become essential reading for the field, reflecting her broad command of dexterous robotics challenges. Beyond technical methods, Billard has pursued deeply human applications, including groundbreaking research on humanoid robots supporting children with autism (686 citations), demonstrating her commitment to socially meaningful robotics. Her surveys on tactile human-robot interaction and variable impedance control further illustrate her range. With multiple papers exceeding hundreds of citations, Billard stands as one of the most impactful voices shaping how robots learn, move, and meaningfully collaborate with humans.
Research Focus
Key Achievements
Top Papers
- 1On Learning, Representing, and Generalizing a Task in a Humanoid Robot1,086 citations · 2007
- 2Robot Programming by Demonstration984 citations · 2008
- 3Trends and challenges in robot manipulation935 citations · 2019
- 4Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models764 citations · 2011
- 5Recent Advances in Robot Learning from Demonstration716 citations · 2019
- 6
- 7Learning and Reproduction of Gestures by Imitation455 citations · 2010
- 8A survey of Tactile Human–Robot Interactions349 citations · 2010
- 9Incremental learning of gestures by imitation in a humanoid robot333 citations · 2007
- 10Stability Considerations for Variable Impedance Control270 citations · 2016