Speakers > Jean-Baptiste Mouret

Title: Micro-data: the challenge of robot learning

Summary: A large part of the impressive results achieved with modern machine learning (in particular, by deep learning) are made possible by the use of very large datasets. However, robots have to face the real world, in which trying something might take seconds, hours, or days. And seeing the consequence of this trial might take much more. In spite of these constraints, robots are expected to adapt like humans or animals, that is, in only a handful of trials: we refer to this challenge as "micro-data learning". In this talk, I will describe our ongoing efforts to design micro-data learning algorithms that allow robots to discover new behaviors by trial-and-error in a few minutes (a dozen of trials), and I will highlight how such algorithms make it possible for robots to recover from unforeseen damage (e.g., learning to walk with a broken leg) without requiring a diagnosis. Overall, this talk will give an overview  of what kind of prior knowledge and machine learning models can be leveraged for micro-data learning in robotics.

Bio: Dr. Jean-Baptiste Mouret is a senior researcher ("Directeur de recherche") at Inria, the French research institute dedicated to computer science and mathematics. He is currently the principal investigator of an ERC grant (ResiBots – Robots with animal-like resilience, 2015-2020). From 2009 to 2015, he was an assistant professor ("maître de conférences") at the Pierre and Marie Curie University (Paris, France).  Overall, J.-B. Mouret conducts researches that intertwine machine learning and evolutionary computation to make robots that can adapt in a few minutes. His work was recently featured on the cover of Nature ("Robots that adapt like animals", Cully et al., 2015) and it received several national and international scientific awards, including the "Prix La Recherche 2016" and the "Distinguished Young Investigator in Artificial Life 2017".