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

Zhong, J. ORCID: 0000-0001-7642-2961, Peniak, M., Tani, J., Ogata, T. and Cangelosi, A., 2019. Sensorimotor input as a language generalisation tool: a neurorobotics model for generation and generalisation of noun-verb combinations with sensorimotor inputs. Autonomous Robots, 43 (5), pp. 1271-1290. ISSN 0929-5593

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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.

Item Type: Journal article
Publication Title: Autonomous Robots
Creators: Zhong, J., Peniak, M., Tani, J., Ogata, T. and Cangelosi, A.
Publisher: Springer New York
Date: June 2019
Volume: 43
Number: 5
ISSN: 0929-5593
Identifiers:
NumberType
10.1007/s10514-018-9793-7DOI
9793Publisher Item Identifier
Rights: © The Author(s) 2018. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Divisions: Schools > School of Science and Technology
Depositing User: Jonathan Gallacher
Date Added: 12 Sep 2019 13:13
Last Modified: 04 Oct 2019 10:11
URI: http://irep.ntu.ac.uk/id/eprint/37646

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