How are you? How a Robot can Learn to Express its own Roboceptions | Zendy

Irene Trifirò | Zendy; Agnese Augello | Zendy; Umberto Maniscalco | Zendy; Giovanni Pilato | Zendy; Filippo Vella | Zendy; Rosa Meo | Zendy

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How are you? How a Robot can Learn to Express its own Roboceptions

Author(s) -

Irene Trifirò,

Agnese Augello,

Umberto Maniscalco,

Giovanni Pilato,

Filippo Vella,

Rosa Meo

Publication year - 2020

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2020.08.050

Subject(s) - computer science , realization (probability) , robot , meaning (existential) , association (psychology) , dual (grammatical number) , process (computing) , artificial intelligence , natural language , natural language processing , human–computer interaction , perception , linguistics , programming language , psychology , philosophy , statistics , mathematics , epistemology , neuroscience , psychotherapist , biology

This work is framed on investigating how a robot can learn associations between linguistic elements, such as words or sentences, and its bodily perceptions, that we named “roboceptions”. We discuss the possibility of defining such a process of an association through the interaction with human beings. By interacting with a user, the robot can learn to ascribe a meaning to its roboceptions to express them in natural language. Such a process could then be used by the robot in a verbal interaction to detect some words recalling the previously experimented roboceptions. In this paper, we discuss a Dual-NMT approach to realize such an association. However, it requires adequate training corpus. For this reason, we consider two different phases towards the realization of the system, and we show the results of the first phase, comparing two approaches: one based on the Latent Semantic Analysis paradigm and one based on the Random Indexing methodology.

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