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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 Federale 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 70 publications and a book in the field of robot learning by imitation and exploration, with recognition including IEEE Ro-Man'2007 Best Paper Award, IEEE-RAS Humanoids'2009 Best Paper Award Finalist, IEEE/RSJ IROS'2013 Cognitive Robotics Best Paper Award Finalist and ICIRA'2015 Best Paper Award Finalist. European Projects I am or have been involved in include DexROV, I-DRESS, TACT-HAND, STIFF-FLOP, PANDORA, SMART-E, SAPHARI, AMARSI, ROBOT@CWE, FEELIX GROWING and COGNIRON.

My work focuses on human-centric robot applications, with the objective of developing probabilistic approaches to encode movements and behaviors in robots evolving in unconstrained human environments. In these applications, the models serve several purposes (recognition, prediction, online synthesis), and are shared by different learning strategies (imitation, emulation, incremental refinement or exploration). The aim is to facilitate the transfer of skills from end-users to robots, or in-between robots, by exploiting multimodal sensory information and by developing intuitive teaching interfaces. I take the perspective that the robots actuation capability must be enriched with force and impedance information to anticipate the users' behaviors and generate safe and natural movements.

The developed learning 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).

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