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

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dc.citedby21
dc.contributor.authorHow D.N.T.en_US
dc.contributor.authorSahari K.S.M.en_US
dc.contributor.authorYuhuang H.en_US
dc.contributor.authorKiong L.C.en_US
dc.contributor.authorid56942483000en_US
dc.contributor.authorid57218170038en_US
dc.contributor.authorid56096604000en_US
dc.contributor.authorid57193642839en_US
dc.date.accessioned2023-05-29T05:59:45Z
dc.date.available2023-05-29T05:59:45Z
dc.date.issued2015
dc.descriptionAnthropomorphic 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 networksen_US
dc.description.abstractRecurrent 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7295871
dc.identifier.doi10.1109/ROMA.2014.7295871
dc.identifier.epage114
dc.identifier.scopus2-s2.0-84959472628
dc.identifier.spage109
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84959472628&doi=10.1109%2fROMA.2014.7295871&partnerID=40&md5=79d35e7f59a4468740faa3c104ae63ea
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22234
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2014 IEEE International Symposium on Robotics and Manufacturing Automation, IEEE-ROMA2014
dc.titleMultiple sequence behavior recognition on humanoid robot using long short-term memory (LSTM)en_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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