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.

Dindo, H., Zambuto, D. (2010). A Probabilistic Approach to Learning a Visually Grounded Language Model through Human-Robot Interaction. In Proc. of the IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010) [10.1109/IROS.2010.5654298].

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

DINDO, Haris;
2010-01-01

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.
2010
2010 IEEE/RSJ International Conference on Intelligent RObots and Systems (IROS)
Taipei, Taiwan
October 18-22
2010
7
Dindo, H., Zambuto, D. (2010). A Probabilistic Approach to Learning a Visually Grounded Language Model through Human-Robot Interaction. In Proc. of the IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010) [10.1109/IROS.2010.5654298].
Proceedings (atti dei congressi)
Dindo, H.; Zambuto, D.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/57670
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