Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction
To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot beh...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2016
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4946379/ https://www.ncbi.nlm.nih.gov/pubmed/27471463 http://dx.doi.org/10.3389/fnbot.2016.00005 |
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author | Yamada, Tatsuro Murata, Shingo Arie, Hiroaki Ogata, Tetsuya |
author_facet | Yamada, Tatsuro Murata, Shingo Arie, Hiroaki Ogata, Tetsuya |
author_sort | Yamada, Tatsuro |
collection | PubMed |
description | To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language–behavior relationships and the temporal patterns of interaction. Here, “internal dynamics” refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human’s linguistic instruction. After learning, the network actually formed the attractor structure representing both language–behavior relationships and the task’s temporal pattern in its internal dynamics. In the dynamics, language–behavior mapping was achieved by the branching structure. Repetition of human’s instruction and robot’s behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases. |
format | Online Article Text |
id | pubmed-4946379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-49463792016-07-28 Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction Yamada, Tatsuro Murata, Shingo Arie, Hiroaki Ogata, Tetsuya Front Neurorobot Neuroscience To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language–behavior relationships and the temporal patterns of interaction. Here, “internal dynamics” refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human’s linguistic instruction. After learning, the network actually formed the attractor structure representing both language–behavior relationships and the task’s temporal pattern in its internal dynamics. In the dynamics, language–behavior mapping was achieved by the branching structure. Repetition of human’s instruction and robot’s behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases. Frontiers Media S.A. 2016-07-15 /pmc/articles/PMC4946379/ /pubmed/27471463 http://dx.doi.org/10.3389/fnbot.2016.00005 Text en Copyright © 2016 Yamada, Murata, Arie and Ogata. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Yamada, Tatsuro Murata, Shingo Arie, Hiroaki Ogata, Tetsuya Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction |
title | Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction |
title_full | Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction |
title_fullStr | Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction |
title_full_unstemmed | Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction |
title_short | Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction |
title_sort | dynamical integration of language and behavior in a recurrent neural network for human–robot interaction |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4946379/ https://www.ncbi.nlm.nih.gov/pubmed/27471463 http://dx.doi.org/10.3389/fnbot.2016.00005 |
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