Sylvain CALINON

Idiap Research Institute
Centre du Parc
Rue Marconi 19
PO Box 592
CH - 1920 Martigny
Switzerland

T +41 27 721 77 61
F +41 27 721 77 12

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I am a permanent researcher at Idiap since May 2014, heading the Robot Learning & Interaction Group. I am also a lecturer at the Ecole Polytechnique Fédérale de Lausanne (EPFL), and an external collaborator at the Department of Advanced Robotics (ADVR), Italian Institute of Technology (IIT).

From 2009 to 2014, I was a Team Leader at ADVR-IIT. From 2007 to 2009, I was a Postdoc at the Learning Algorithms and Systems Laboratory (LASA), EPFL. I hold a PhD from EPFL (2007), awarded by the Robotdalen Scientific Award, ABB Award and EPFL-Press Distinction. I co-authored about 90 publications in the field of robot learning, with recognition including Best Paper Award at IEEE Ro-Man'2007 and Best Paper Award Finalist at IEEE-RAS Humanoids'2009, IEEE/RSJ IROS'2013, ICIRA'2015 and IEEE ICRA'2016. I currently serve as Associate Editor in IEEE Robotics and Automation Letters, Springer Intelligent Service Robotics, Frontiers in Robotics and AI, and the International Journal of Advanced Robotic Systems. Collaborative projects I am or have been involved in include DexROV, I-DRESS, TACT-HAND, PLATFORM-MMD, STIFF-FLOP, PANDORA, SMART-E, SAPHARI, AMARSI, ROBOT@CWE, FEELIX GROWING and COGNIRON.

My work focuses on human-centric robotic applications in which the robots can learn new skills by interacting with the end-users. From a machine learning perspective, the challenge is to acquire skills from only few demonstrations and interactions, with strong generalization requirements. It requires: 1) the development of intuitive active learning interfaces to acquire meaningful demonstrations; 2) the development of models that can exploit the structure and geometry of the acquired data in an efficient way; 3) the development of adaptive control techniques that can exploit the learned task variations and coordination patterns.

The developed models must serve several purposes (recognition, prediction, online synthesis), and be compatible with different learning strategies (imitation, emulation, incremental refinement or exploration). For the reproduction of skills, the models need to be enriched with force and impedance information to enable human-robot collaboration and to generate safe and natural movements.

These models and algorithms can be applied to a wide range of robotic applications, with robots that are either close to us (assistive robots in I-DRESS), parts of us (prosthetic hands in TACT-HAND), or far away from us (manipulation skills in deep water with DexROV).

Personal website: http://calinon.ch

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