Time: 3:00 - 4:00pm
Venue: People's Palace, PP2, Mile End campus
This seminar is co-organised with the QMUL Institute of Applied Data Science. All are invited. Coffee/tea reception after the seminar.
Speaker: Dr Antoine Cully (Lecturer, Department of Computing, Imperial College London, UK)
Abstract. The optimisation of functions to find the best solution according to one or several objectives has a central role in many engineering and research fields. Recently, a new family of optimisation algorithms, named Quality-Diversity optimisation, has been introduced, and contrasts with classic algorithms. Instead of searching for a single solution, Quality-Diversity algorithms are searching for a large collection of both diverse and high-performing solutions. The role of this collection is to cover the range of possible solution types as much as possible, and to contain the best solution for each type. During this seminar, I will show how these algorithms can enable robots to learn large behavioural repertoires and to adapt to unforeseen situations, like mechanical damages.
Bio. Antoine Cully is Lecturer at Imperial College London (United Kingdom). His research is at the intersection between artificial intelligence and robotics. He applies machine learning approaches, like deep learning and evolutionary algorithms, on robots to increase their versatility and their adaptation capabilities. He received the M.Sc. and the Ph.D. degrees in robotics and artificial intelligence from the Sorbonne University of Paris (previously called UPMC), France, in 2012 and 2015, respectively, and the engineer degree from the School of Engineering Polytech’Sorbonne, in 2012. His Ph.D. dissertation has received three Best-Thesis awards. He has published several journal papers in prestigious journals including Nature, IEEE Transaction in Evolutionary Computation, and the International Journal of Robotics Research. His work was featured on the cover of Nature (Cully et al., 2015) and received the "Outstanding Paper of 2015" award from the Society for Artificial Life (2016), and the French "La Recherche" award (2016).