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Simultaneous versus incremental learning of multiple skills by modular robots

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Abstract

This paper is concerned with the problem of learning multiple skills by modular robots. The main question we address is whether it is better to learn multiple skills simultaneously (all-at-once) or incrementally (one-by-one). We conduct an experimental study with modular robots of various morphologies that need to acquire three different but correlated skills, efficient locomotion, navigation towards a target point, and obstacle avoidance, using a real-time, on-board evolution as the learning method. The results indicate that the one-by-one strategy is more efficient and more stable than the all-at-once strategy.

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Notes

  1. Webots system by Cyberbotics, www.cyberbotics.com.

  2. Early work on such learning strategy for the problem at hand has shown that an individual mutation rate for each gene does not provide an advantage, w.r.t having a common mutation rate for all genes, see [28].

  3. Recall that the Infancy stage is part of a cycle—the Triangle of Life—where individuals mate sto generate new offspring.

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Authors and Affiliations

  1. Centre for Automation and Robotics UPM-CSIC, Madrid, Spain

    C. Rossi

  2. VU University Amsterdam, Amsterdam, The Netherlands

    A. E. Eiben

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  1. C. Rossi
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  2. A. E. Eiben
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Correspondence to C. Rossi.

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Rossi, C., Eiben, A.E. Simultaneous versus incremental learning of multiple skills by modular robots. Evol. Intel. 7, 119–131 (2014). https://doi.org/10.1007/s12065-014-0109-3

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