Learning adjectives and nouns from affordances on the iCub humanoid robot
Author
Yürüten O.
Uyanık, Fırat
Çalişkan, Y.
Bozcuoǧlu, A.K.
Şahin, E.
Kalkan, Sinan
Date
2012Source Title
From Animals to Animats 12
Print ISSN
0302-9743
Publisher
Springer, Berlin, Heidelberg
Volume
7426
Pages
330 - 340
Language
English
Type
Conference PaperItem Usage Stats
156
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146
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Abstract
This article studies how a robot can learn nouns and adjectives in language. Towards this end, we extended a framework that enabled robots to learn affordances from its sensorimotor interactions, to learn nouns and adjectives using labeling from humans. Specifically, an iCub humanoid robot interacted with a set of objects (each labeled with a set of adjectives and a noun) and learned to predict the effects (as labeled with a set of verbs) it can generate on them with its behaviors. Different from appearance-based studies that directly link the appearances of objects to nouns and adjectives, we first predict the affordances of an object through a set of Support Vector Machine classifiers which provided a functional view of the object. Then, we learned the mapping between these predicted affordance values and nouns and adjectives. We evaluated and compared a number of different approaches towards the learning of nouns and adjectives on a small set of novel objects. The results show that the proposed method provides better generalization than the appearance-based approaches towards learning adjectives whereas, for nouns, the reverse is the case. We conclude that affordances of objects can be more informative for (a subset of) adjectives describing objects in language. © 2012 Springer-Verlag.
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http://hdl.handle.net/11693/28167Published Version (Please cite this version)
http://dx.doi.org/10.1007/978-3-642-33093-3_33https://doi.org/10.1007/978-3-642-33093-3