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

Content Provider	IEEE Xplore Digital Library
Author	How, D.N.T. Sahari, K.S.M. Hu Yuhuang Loo Chu Kiong
Copyright Year	2014
Description	Author affiliation: Dept. of Mech. Eng., Univ. Tenaga Nasional, Kajang, Malaysia (How, D.N.T.; Sahari, K.S.M.) \|\| Dept. of Artificial Intell., Univ. Malaya, Kuala Lumpur, Malaysia (Hu Yuhuang; Loo Chu Kiong)
Abstract	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.
Starting Page	109
Ending Page	114
File Size	2987547
Page Count	6
File Format	PDF
e-ISBN	9781479957651
DOI	10.1109/ROMA.2014.7295871
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-15
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Robots
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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