Open-Ended Evolutionary Robotics: an Information Theoretic Approach

Pierre Delarboulas 1 Marc Schoenauer 1 Michèle Sebag 2
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : This paper is concerned with designing self-driven fitness functions for Embedded Evolutionary Robotics. The proposed approach considers the entropy of the sensori-motor stream generated by the robot controller. This entropy is computed using unsupervised learning; its maximization, achieved by an on-board evolutionary algorithm, implements a ``curiosity instinct'', favouring controllers visiting many diverse sensori-motor states (sms). Further, the set of sms discovered by an individual can be transmitted to its offspring, making a cultural evolution mode possible. Cumulative entropy (computed from ancestors and current individual visits to the sms) defines another self-driven fitness; its optimization implements a "discovery instinct'', as it favours controllers visiting new or rare sensori-motor states. Empirical results on the benchmark problems proposed by Lehman and Stanley (2008) comparatively demonstrate the merits of the approach.
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  • ARXIV : 1006.4959

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Pierre Delarboulas, Marc Schoenauer, Michèle Sebag. Open-Ended Evolutionary Robotics: an Information Theoretic Approach. PPSN XI, Sep 2010, Krakow, Poland. pp.334-343. ⟨inria-00494237⟩

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