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Machine Learning for Robots to Think Fast in the Face of Unexpected Events

IET Public lecture

Professor Aude Billard will present techniques from machine learning to allow robots to learn strategies to enable them to react rapidly and efficiently to changes in the environment. Learning the set of feasible solutions will be preferred over learning optimal controllers. She will review methods we have developed to allow instantaneous reactions to perturbation, leveraging on the multiplicity of feasible solutions. Professor Billard will present applications of these methods for compliant control during human-robot collaborative tasks and for performing fast motion, such as catching flying objects.

 

The next generation of robots will soon get out of the secure and predictable environment of factories and will face the complexity and unpredictability of our daily environments. To avoid that robots fail lamely at the task they are programmed to do, robots will need to adapt on the go. I will present techniques from machine learning to allow robots to learn strategies to enable them to react rapidly and efficiently to changes in the environment. Learning the set of feasible solutions will be preferred over learning optimal controllers. I will review methods we have developed to allow instantaneous reactions to perturbation, leveraging on the multiplicity of feasible solutions. I will present applications of these methods for compliant control during human-robot collaborative tasks and for performing fast motion, such as catching flying objects.

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Continuing Professional Development

This event can contribute towards your Continuing Professional Development (CPD) hours as part of the IET's CPD monitoring scheme.

Time

03 Jul 2019

6:00pm - 7:00pm

Calendar
Add to Calendar 07/03/2019 18:00 07/03/2019 19:00 Machine Learning for Robots to Think Fast in the Face of Unexpected Events The next generation of robots will soon get out of the secure and predictable environment of factories and will face the complexity and unpredictability of our daily environments. To avoid that robots fail lamely at the task they are programmed to do, robots will need to adapt on the go. Graduate Centre, Hendon, London NW4 4JS, UK

Organiser

Robotics and Mechatronics TPN

Registration Information

Register online

Speakers

Professor Aude Billard

Professor Aude Billard

Professor of Robotics - Swiss Institute of Technology Lausanne (EPFL)

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).

Location

Graduate Centre of Mile End campus

Mile End Road
London
E1 4NS
United Kingdom