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arXiv · 1006.4959

Open-Ended Evolutionary Robotics: an Information Theoretic Approach

Pierre Delarboulas·Marc Schoenauer·Michèle Sebag
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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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Pierre Delarboulas, Marc Schoenauer, Michèle Sebag. 2010-06-25. Open-Ended Evolutionary Robotics: an Information Theoretic Approach. https://arxiv.org/abs/1006.4959

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