

Preprint 15/2013
Information driven self-organization of complex robotic behaviors
Georg Martius, Ralf Der, and Nihat Ay
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Submission date: 31. Jan. 2013
Pages: 32
published in: PLOS ONE, 8 (2013) 5, art-no. e63400
DOI number (of the published article): 10.1371/journal.pone.0063400
Bibtex
MSC-Numbers: 94A15, 68T05, 68T40, 37N35
PACS-Numbers: 89.70.-a, 87.19.lu, 87.85.St, 05.45.-a, 89.75.Fb
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Abstract:
Information theory is a powerful tool to express principles to drive
autonomous systems because it is domain invariant and allows for an
intuitive interpretation. This paper studies the use of the
predictive information (PI), also called excess entropy or effective
measure complexity, of the sensorimotor process as a driving force to
generate behavior. We study nonlinear and nonstationary systems and
introduce the time-local predicting information (TiPI) which allows us
to derive exact results together with explicit update rules for the
parameters of the controller in the dynamical systems framework. In
this way the information principle, formulated at the level of
behavior, is translated to the dynamics of the synapses. We underpin
our results with a number of case studies with high-dimensional
robotic systems. We show the spontaneous cooperativity in a complex
physical system with decentralized control. Moreover, a jointly
controlled humanoid robot develops a high behavioral variety depending
on its physics and the environment it is dynamically embedded into.
The behavior can be decomposed into a succession of low-dimensional
modes that increasingly explore the behavior space. This is a
promising way to avoid the curse of dimensionality which hinders
learning systems to scale well.