Sylvain Calinon
EPFL > VPA-AVP-DLE > AVP-DLE-EDOC > EDEE-ENS
EPFL STI IEM LIDIAP
ELD 241 (Bâtiment ELD)
Station 11
1015 Lausanne
Web site: Site web: https://idiap.epfl.ch/
Domaines de compétences
Programmation de robots par démonstration, Apprentissage par imitation, Collaboration homme-robot, Contrôle optimal
Biographie
I am a Senior Research Scientist at the Idiap Research Institute and a Lecturer at the Ecole Polytechnique Fédérale de Lausanne (EPFL), with research interests covering robot learning, human-robot collaboration, optimal control and model-based optimization.From 2009 to 2014, I was a Team Leader at the Department of Advanced Robotics, Italian Institute of Technology (IIT). From 2007 to 2009, I was a Postdoc at the Learning Algorithms and Systems Laboratory, EPFL. I hold a PhD from EPFL (2007), awarded by the Robotdalen Scientific Award, ABB Award and EPFL-Press Distinction. Other recognitions include Best Paper Award in the journal of Intelligent Service Robotics (ISR, 2017), Best Paper Award at IEEE Ro-Man'2007, Best Poster Award at ICRA'2021 Workshop, Best Presentation Award at CoRL'2019, and Best Paper Award Finalist at IEEE-RAS Humanoids'2009, IEEE/RSJ IROS'2013, ICIRA'2015 and IEEE ICRA'2016. I served as Associate Editor in IEEE Transactions on Robotics (T-RO) and IEEE Robotics and Automation Letters (RA-L), and I currently serve as TC Chair on Model-based optimization for robotics for the IEEE Robotics and Automation Society (RAS).
PUBLICATIONS
La liste complète de mes publications se trouve ici: https://calinon.ch/publications.htmEnseignement & Phd
Enseignement
Microtechnique
Doctorants
Bilaloglu Cem, Li Yiming, Löw Tobias, Maric Ante, Razmjoo Fard Amirreza, Schonger Martin, Xue Teng, Zhang Yan,A dirigé les thèses EPFL de
Girgin Hakan , Jankowski Julius Maximilian , Jaquier Noémie Laure Gwendoline , Kulak Thibaut Antoine , Lembono Teguh Santoso , Pignat Emmanuel , Shetty Suhan Narayana , Tanwani Ajay Kumar ,Cours
Machine Learning for Engineers
* Fundamentals 4h
- Notion of learning (classification vs. regression vs. density modeling vs. reinforcement learning)
- Probability theory (formalization, densities, density models)
- Standard statistical tools
- Cross validation and performance evaluation
- Signal processing (Fourier, edges, etc.)
- Optimization (gradient, newton, stochastic gra
- Notion of learning (classification vs. regression vs. density modeling vs. reinforcement learning)
- Probability theory (formalization, densities, density models)
- Standard statistical tools
- Cross validation and performance evaluation
- Signal processing (Fourier, edges, etc.)
- Optimization (gradient, newton, stochastic gra