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21.01.2021 | Technical Contribution

Robot in the Mirror: Toward an Embodied Computational Model of Mirror Self-Recognition

verfasst von: Matej Hoffmann, Shengzhi Wang, Vojtech Outrata, Elisabet Alzueta, Pablo Lanillos

Erschienen in: KI - Künstliche Intelligenz | Ausgabe 1/2021

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Abstract

Self-recognition or self-awareness is a capacity attributed typically only to humans and few other species. The definitions of these concepts vary and little is known about the mechanisms behind them. However, there is a Turing test-like benchmark: the mirror self-recognition, which consists in covertly putting a mark on the face of the tested subject, placing her in front of a mirror, and observing the reactions. In this work, first, we provide a mechanistic decomposition, or process model, of what components are required to pass this test. Based on these, we provide suggestions for empirical research. In particular, in our view, the way the infants or animals reach for the mark should be studied in detail. Second, we develop a model to enable the humanoid robot Nao to pass the test. The core of our technical contribution is learning the appearance representation and visual novelty detection by means of learning the generative model of the face with deep auto-encoders and exploiting the prediction error. The mark is identified as a salient region on the face and reaching action is triggered, relying on a previously learned mapping to arm joint angles. The architecture is tested on two robots with completely different face.

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1
Mitchell [46] discusses the “chicken-and-egg problem” of acquiring this self-image—prior recognition in the mirror may be necessary to learn it—and concludes that there are “three possibilities: (1) a visually based, incomplete self-image of the part of the organism it can see, (2) a non-visual self-image, (3) or a mixture of these images.”
 
2
Elbow pitch and wrist rotation were fixed.
 
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Metadaten
Titel
Robot in the Mirror: Toward an Embodied Computational Model of Mirror Self-Recognition
verfasst von
Matej Hoffmann
Shengzhi Wang
Vojtech Outrata
Elisabet Alzueta
Pablo Lanillos
Publikationsdatum
21.01.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
KI - Künstliche Intelligenz / Ausgabe 1/2021
Print ISSN: 0933-1875
Elektronische ISSN: 1610-1987
DOI
https://doi.org/10.1007/s13218-020-00701-7

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