Papers

13

Total Citations

1,922

H-Index

12

About

F. Guenter is a robotics researcher whose work sits at the intersection of imitation learning, programming by demonstration, and humanoid robot design. Best known for the landmark 2007 paper "On Learning, Representing, and Generalizing a Task in a Humanoid Robot" — now cited over 1,086 times — Guenter developed foundational frameworks that allow robots to extract meaningful task features from human demonstrations and generalize that knowledge across varying contexts. This contribution helped shape the modern field of robot learning from demonstration, offering practical pathways for making robots programmable by non-experts. Guenter's research extended into dynamical systems-based skill acquisition, showing how robots can reproduce goal-directed gestures robustly under perturbation (237 citations), and into reinforcement learning as a mechanism for refining and adapting imitated behaviors (145 citations). Alongside this algorithmic work, Guenter contributed to the physical design of the humanoid robot Robota, developing biomimetic spine and upper-body architectures intended to enhance human-robot interaction, particularly in therapeutic settings with disabled children. Across a remarkably focused body of work produced largely between 2006 and 2008, Guenter established core principles in imitation learning and robot skill transfer that continue to influence robotics research today.

Research Focus

Key Achievements

12
H-Index
13
Papers
1,922
Total Citations
148
Avg Citations/Paper
🏆 Most Cited Paper
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
1,086 citations · 2007
📈 Most Prolific Year: 2006 (6 Papers)
🤝 Key Collaborators: 13
🏛 Institutions: École Polytechnique Fédérale de Lausanne, École Normale Supérieure - PSL

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago