Dexterous manipulation of objects is robotics’ 21st century primary goal. It envisions robots capable of sorting objects and packaging them, of chopping vegetables and folding clothes, and this, at high speed. To manipulate these objects cannot be done with traditional control approaches, for lack of accurate models of objects and contact dynamics. Robotics leverages, hence, the immense progress in machine learning to encapsulate models of uncertainty and to support further advances on adaptive and robust control.
I will present applications of machine learning for controlling robots to: a) learn non-linear control laws in closed-form, which enables fast retrieval and adaptation at run time – and have robots catch flying objects; b) model complex deformations of objects – to peel and grate vegetables; c) learn manifolds, as embedding of feasible solutions and extract latent spaces in which stability of control laws can be more easily ensured.
Aude Billard (École polytechnique fédérale de Lausanne)
Aude Billard is full professor and head of the LASA laboratory at the School of Engineering at the Swiss Institute of Technology Lausanne (EPFL). She was a faculty member at the University of Southern California, prior to joining EPFL in 2003. She holds a B.Sc and M.Sc. in Physics from EPFL (1995) and a Ph.D. in Artificial Intelligence (1998) from the University of Edinburgh. She was the recipient of the Intel Corporation Teaching award, the Swiss National Science Foundation career award in 2002, the Outstanding Young Person in Science and Innovation from the Swiss Chamber of Commerce and the IEEE-RAS Best Reviewer Award. Her research spans the fields of machine learning and robotics with a particular emphasis on learning from sparse data and performing fast and robust retrieval. Her work finds application to robotics, human-robot / human-computer interaction and computational neuroscience. This research received best paper awards from IEEE T-RO, RSS, ICRA, IROS, Humanoids and ROMAN and was featured in premier venues (BBC, IEEE Spectrum, Wired).