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Software . 2022
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Code and Data for "Morphological Wobbling Can Help Robots Learn"

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 03 May 2022 English Publisher:Zenodo
Authors: Benureau, Fabien; Tani, Jun;

doi: 10.5281/zenodo.6513360 , 10.5281/zenodo.6513359

Code and Data for "Morphological Wobbling Can Help Robots Learn"

Abstract

# Code and Data for "Morphological Wobbling Can Help Robots Learn" This code and data are for reproducing the figure and results of the article "Morphological Wobbling Can Help Robots Learn", by Fabien C.Y. Benureau and Jun Tani. There are three files here: wobbling2022.tar.xz: the code, figures, post-processed data (i.e. data displayed in the figures). The readme file (wobbling2022/code/readme.md) contains instruction on how to use the code and regenerate the results and figures. movie S1.mp4: supplementary movie for the article. results_archives.tar: the complete log files of the simulations ran for the article. Needed if one want to regenerate post-processed files. If you have issues using this code and data, contact Fabien C.Y. Benureau: fabien@benureau.com

We are grateful for the help and support provided by the Scientific Computing and Data Analysis section of Research Support Division at OIST.

Keywords

developmental robotics, soft robots, morphological wobbling, morphological development

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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