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Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor


Covered by SCOPUS


CiteScore 2018: 2.17

SCImago Journal Rank (SJR) 2018: 0.336
Source Normalized Impact per Paper (SNIP) 2018: 1.707

ICV 2017: 99.90

Open Access
Online
ISSN
2081-4836
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Is it useful for a robot to visit a museum?

The impact of cumulative learning on a robot population

Aliaa Moualla / Sofiane Boucenna / Ali Karaouzene / Denis Vidal / Philippe Gaussier
Published Online: 2018-12-13 | DOI: https://doi.org/10.1515/pjbr-2018-0025

Abstract

In this work, we study how learning in a special environment such as a museum can influence the behavior of robots. More specifically, we show that online learning based on interaction with people at a museum leads the robots to develop individual preferences. We first developed a humanoid robot (Berenson) that has the ability to head toward its preferred object and to make a facial expression that corresponds to its attitude toward said object. The robot is programmed with a biologically-inspired neural network sensory-motor architecture. This architecture allows Berenson to learn and to evaluate objects. During experiments, museum visitors’ emotional responses to artworks were recorded and used to build a database for training. A similar database was created in the laboratory with laboratory objects. We use those databases to train two simulated populations of robots. Each simulated robot emulates the Berenson sensory-motor architecture. Firstly, the results show the good performance of our architecture in artwork recognition in the museum. Secondly, they demonstrate the effect of training variability on preference diversity. The response of the two populations in a new unknown environment is different; the museum population of robots shows a greater variance in preferences than the population of robots that have been trained only on laboratory objects. The obtained diversity increases the chances of success in an unknown environment and could favor an accidental discovery.

Keywords: neural networks; aesthetic preferences; pattern recognition; online learning; robot’s population; chance discovery

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About the article

Received: 2018-02-13

Accepted: 2018-10-09

Published Online: 2018-12-13

Published in Print: 2018-12-01


Citation Information: Paladyn, Journal of Behavioral Robotics, Volume 9, Issue 1, Pages 374–390, ISSN (Online) 2081-4836, DOI: https://doi.org/10.1515/pjbr-2018-0025.

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© by Aliaa Moualla, et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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