A Probabilistic Approach to Learning a Visually Grounded Language Model through Human-Robot Interaction

Risultato della ricerca: Otherpeer review

11 Citazioni (Scopus)

Abstract

A Language is among the most fascinating and complex cognitive activities that develops rapidly since the early months of infants' life. The aim of the present work is to provide a humanoid robot with cognitive, perceptual and motor skills fundamental for the acquisition of a rudimentary form of language. We present a novel probabilistic model, inspired by the findings in cognitive sciences, able to associate spoken words with their perceptually grounded meanings. The main focus is set on acquiring the meaning of various perceptual categories (e. g. red, blue, circle, above, etc.), rather than specific world entities (e. g. an apple, a toy, etc.). Our probabilistic model is based on a variant of multi-instance learning technique, and it enables a robotic platform to learn grounded meanings of adjective/noun terms. The systems could be used to understand and generate appropriate natural language descriptions of real objects in a scene, and it has been successfully tested on the NAO humanoid robotic platform.
Lingua originaleEnglish
Numero di pagine7
Stato di pubblicazionePublished - 2010

All Science Journal Classification (ASJC) codes

  • ???subjectarea.asjc.1700.1702???
  • ???subjectarea.asjc.1700.1709???
  • ???subjectarea.asjc.2200.2207???

Fingerprint

Entra nei temi di ricerca di 'A Probabilistic Approach to Learning a Visually Grounded Language Model through Human-Robot Interaction'. Insieme formano una fingerprint unica.

Cita questo