I am a permanent researcher at the Idiap Research Institute since May 2014, with research interests covering robot learning and human-robot interaction. 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). Collaborative projects I am or have been involved in include MEMMO, ROSALIS, DexROV, I-DRESS, TACT-HAND, PLATFORM-MMD, STIFF-FLOP, PANDORA, SMART-E, SAPHARI, AMARSI, ROBOT@CWE, FEELIX GROWING and COGNIRON, supported by the European Commission and by the Swiss National Science Foundation.
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 demands. 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, these 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).