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Multiple sequence behavior recognition on humanoid robot using long short-term memory (LSTM)

Anthropomorphic robots; Behavioral research; Cells; Cytology; Manufacture; Network architecture; Robotics; Robots; Speech recognition; Behavior recognition; Long short term memory; Multiple sequences; Protein structure prediction; Recurrent networks; Recurrent neural network (RNN); Time-series data;...

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Main Authors: How D.N.T., Sahari K.S.M., Yuhuang H., Kiong L.C.
Other Authors: 56942483000
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-222342023-05-29T13:59:45Z Multiple sequence behavior recognition on humanoid robot using long short-term memory (LSTM) How D.N.T. Sahari K.S.M. Yuhuang H. Kiong L.C. 56942483000 57218170038 56096604000 57193642839 Anthropomorphic robots; Behavioral research; Cells; Cytology; Manufacture; Network architecture; Robotics; Robots; Speech recognition; Behavior recognition; Long short term memory; Multiple sequences; Protein structure prediction; Recurrent networks; Recurrent neural network (RNN); Time-series data; Vanishing gradient; Recurrent neural networks Recurrent neural networks (RNN) are powerful sequence learners. However, RNN suffers from the problem of vanishing gradient point. This fact makes learning sequential task more than 10 time steps harder for RNN. Recurrent network with LSTM cells as hidden layers (LSTM-RNN) is a deep learning recurrent network architecture designed to address the vanishing gradient problem by incorporating memory cells (LSTM cells) in the hidden layer(s). This advantage puts it at one of the best sequence learners for time-series data such as cursive hand writings, protein structure prediction, speech recognition and many more task that require learning through long time lags [2][3][4], In this paper, we applied the concept of using recurrent networks with LSTM cells as hidden layer to learn the behaviours of a humanoid robot based on multiple sequences of joint data from 10 joints on the NAO robot. We show that the LSTM network is able to learn the patterns in the data and effectively classify the sequences into 6 different trained behaviors. � 2014 IEEE. Final 2023-05-29T05:59:45Z 2023-05-29T05:59:45Z 2015 Conference Paper 10.1109/ROMA.2014.7295871 2-s2.0-84959472628 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959472628&doi=10.1109%2fROMA.2014.7295871&partnerID=40&md5=79d35e7f59a4468740faa3c104ae63ea https://irepository.uniten.edu.my/handle/123456789/22234 7295871 109 114 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Anthropomorphic robots; Behavioral research; Cells; Cytology; Manufacture; Network architecture; Robotics; Robots; Speech recognition; Behavior recognition; Long short term memory; Multiple sequences; Protein structure prediction; Recurrent networks; Recurrent neural network (RNN); Time-series data; Vanishing gradient; Recurrent neural networks
author2 56942483000
author_facet 56942483000
How D.N.T.
Sahari K.S.M.
Yuhuang H.
Kiong L.C.
format Conference Paper
author How D.N.T.
Sahari K.S.M.
Yuhuang H.
Kiong L.C.
spellingShingle How D.N.T.
Sahari K.S.M.
Yuhuang H.
Kiong L.C.
Multiple sequence behavior recognition on humanoid robot using long short-term memory (LSTM)
author_sort How D.N.T.
title Multiple sequence behavior recognition on humanoid robot using long short-term memory (LSTM)
title_short Multiple sequence behavior recognition on humanoid robot using long short-term memory (LSTM)
title_full Multiple sequence behavior recognition on humanoid robot using long short-term memory (LSTM)
title_fullStr Multiple sequence behavior recognition on humanoid robot using long short-term memory (LSTM)
title_full_unstemmed Multiple sequence behavior recognition on humanoid robot using long short-term memory (LSTM)
title_sort multiple sequence behavior recognition on humanoid robot using long short-term memory (lstm)
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806424221362946048
score 13.222552

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