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Seminar Talk


22 Feb 2019

Internal Event

 

Speaker: Dr. Georg Martius, Group Leader, Max Planck Institute for Intelligent Systems, Tübingen

Title: Self-organisation of behaviour in autonomous robot development

Date: Friday 22nd February, 2019

Time: 2pm – 3pm

Location: IF 4.31/4.33

Abstract
Dr. Martius is studying the question: how robots can autonomously develop skills.  Considering children, it seems natural that they have their own agenda. They explore their environment in a playful way, without the necessity for somebody to tell them what to do next. With robots the situation is different. There are many methods to let robots learn to do something, but it is always about learning to do a specific task from a supervision signal.  Unfortunately, these methods do not scale well to systems with many degrees of freedom, except a good  pre-structuring is available.  The hypothesis is that if the robots first learn to use their bodies and interact with the environment in a playful way they can acquire many small skills with which they can later solve complicated tasks much quicker. In the talk, he will present his steps into this direction. Starting from some general information theoretic consideration we provide robots with an own drive to do something and explore their behavioural capabilities.  Technically, this is achieved by considering the sensorimotor loop as a dynamical system, whose parameters are adapted online according to a gradient ascent on an approximated information quantity.  He will show examples of simulated and real robots behaving in a self-determined way and present future directions.

Bio

Georg Martius is leading a research group on Autonomous Learning at the Max Planck Institute for Intelligent Systems in Tübingen, Germany.  He was a postdoc fellow at the IST Austria in the groups of Christoph Lampert and Gašper Tkačik after being a postdoc at the Max Planck Institute for Mathematics in the Sciences in Leipzig.  He pursues research in autonomous learning, that is how an embodied agent can determine what to learn, how to learn, and how to judge the learning success.  He is using information theory and dynamical systems theory to formulate generic intrinsic motivations that lead to coherent behaviour exploration – much like playful behaviour.  He is also working on machine learning methods particularly suitable for internal models and hierarchical reinforcement learning.  More details can be found on http://al.is.mpg.de.