Sensorimotor input as a language generalisation tool: a neurorobotics model for generation and generalisation of noun-verb combinations with sensorimotor inputs

Junpei Zhong, Martin Peniak, Jun Tani, Tetsuya Ogata, Angelo Cangelosi

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The paper presents a neurorobotics cognitive model explaining the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot. The dataset used for training was obtained from object manipulation tasks with a humanoid robot platform; it includes 9 motor actions and 9 objects placing placed in 6 different locations), which enables the robot to learn to handle real-world objects and actions. Based on the multiple time-scale recurrent neural networks, this study demonstrates its generalisation capability using a large data-set, with which the robot was able to generalise semantic representation of novel combinations of noun-verb sentences, and therefore produce the corresponding motor behaviours. This generalisation process is done via the grounding process: different objects are being interacted, and associated, with different motor behaviours, following a learning approach inspired by developmental language acquisition in infants. Further analyses of the learned network dynamics and representations also demonstrate how the generalisation is possible via the exploitation of this functional hierarchical recurrent network.

Original languageEnglish
Pages (from-to)1271-1290
Number of pages20
JournalAutonomous Robots
Volume43
Issue number5
DOIs
Publication statusPublished - 2019 Jun 15

Keywords

  • Developmental robotics
  • Language learning
  • Multiple time-scale recurrent neural network
  • Neurorobotics
  • Recurrent artificial neural networks

ASJC Scopus subject areas

  • Artificial Intelligence

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