Machine Learning for Estimating Robust Control Laws

Duke Computer Science Colloquium
Speaker Name
Aude Billard
Date and Time
Fitzpatrick Schiciano Side B 1466
Lunch served at 11:45 am

This talk will provide an overview of techniques developed in my group to enable robots to react rapidly in the face of changes in the environment when manipulating objects. Learning is guided by observing humans’ elaborate manipulatory skills. I will stress how important it is to model the various ways with which humans perform the same task. This multiplicity of solutions is the key to generate robust and flexible robotic controllers capable of adapting their strategies in the face of unexpected changes in the environment. I will review methods we have developed to allow instantaneous reactions to perturbation. These methods are based on autonomous dynamical systems as core controller. Machine learning techniques are developed to identify the control law while ensuring stability of the learned dynamics. I will present applications of these learned control laws for compliant control during human-robot collaborative tasks and for performing sport tasks, such as when playing golf with moving targets. The talk will conclude with examples in which robots achieve super-human capabilities for catching fast moving objects with a dexterity that exceeds that displayed by human beings.

Short Biography

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 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).

Kris Hauser