Local Online Support Vector Regression for Learning Control
Support vector regression (SVR) is a class of machine learning technique that has been successfully applied to low-level learning control in robotics. Because of the large amount of computation required by SVR, however, most studies have used a batch mode. Although a recently developed online form o...
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creator | Younggeun Choi Shin-Young Cheong Schweighofer, N. |
description | Support vector regression (SVR) is a class of machine learning technique that has been successfully applied to low-level learning control in robotics. Because of the large amount of computation required by SVR, however, most studies have used a batch mode. Although a recently developed online form of SVR shows faster learning performance than batch SVR, the amount of computation required by online SVR prevent its use in real-time robot learning control, which requires short sampling time. Here, we present a novel method, Local online SVR for Learning control, or LoSVR, that extends online SVR with a windowing method. We demonstrate the performance of LoSVR in learning the inverse dynamics of both a simulated two-joint robot and a real one-link robot arm. Our results show that, in both cases, LoSVR can learn the inverse dynamics on-line faster and with a better accuracy than batch SVR. |
doi_str_mv | 10.1109/CIRA.2007.382883 |
format | Conference Proceeding |
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Our results show that, in both cases, LoSVR can learn the inverse dynamics on-line faster and with a better accuracy than batch SVR.</description><identifier>ISBN: 1424407893</identifier><identifier>ISBN: 9781424407897</identifier><identifier>EISBN: 1424407907</identifier><identifier>EISBN: 9781424407903</identifier><identifier>DOI: 10.1109/CIRA.2007.382883</identifier><language>eng ; jpn</language><publisher>IEEE</publisher><subject>Computational intelligence ; Degradation ; Machine learning ; Neural networks ; Robot control ; Robotics and automation ; Sampling methods ; Support vector machine classification ; Support vector machines ; USA Councils</subject><ispartof>2007 International Symposium on Computational Intelligence in Robotics and Automation, 2007, p.13-18</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4269883$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4269883$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Younggeun Choi</creatorcontrib><creatorcontrib>Shin-Young Cheong</creatorcontrib><creatorcontrib>Schweighofer, N.</creatorcontrib><title>Local Online Support Vector Regression for Learning Control</title><title>2007 International Symposium on Computational Intelligence in Robotics and Automation</title><addtitle>CIRA</addtitle><description>Support vector regression (SVR) is a class of machine learning technique that has been successfully applied to low-level learning control in robotics. 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Our results show that, in both cases, LoSVR can learn the inverse dynamics on-line faster and with a better accuracy than batch SVR.</description><subject>Computational intelligence</subject><subject>Degradation</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Robot control</subject><subject>Robotics and automation</subject><subject>Sampling methods</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><subject>USA Councils</subject><isbn>1424407893</isbn><isbn>9781424407897</isbn><isbn>1424407907</isbn><isbn>9781424407903</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9j0tLxDAURiMiqOPsBTf5A61JbpoHroai40BhYHxshzud2yFSk5LWhf_eguLZfJzNB4exWylKKYW_rze7VamEsCU45RycsWupldbCemHP_8V5uGTLcfwQM-DBq-qKPTSpxZ5vYx8i8ZevYUh54u_UTinzHZ0yjWNIkXezNoQ5hnjidYpTTv0Nu-iwH2n5twv29vT4Wj8XzXa9qVdNEWRlpkJp9KCNpaNw6KBziAhSI5I27nCQxglzRGiVBetbEEqCrKi1HlTlpXCwYHe_v4GI9kMOn5i_91oZP8fCD-OsRos</recordid><startdate>200706</startdate><enddate>200706</enddate><creator>Younggeun Choi</creator><creator>Shin-Young Cheong</creator><creator>Schweighofer, N.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200706</creationdate><title>Local Online Support Vector Regression for Learning Control</title><author>Younggeun Choi ; Shin-Young Cheong ; Schweighofer, N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i156t-24a93467ed08a83f8aaa314aae468bb16806da3c27379c3021315ec7932591083</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng ; jpn</language><creationdate>2007</creationdate><topic>Computational intelligence</topic><topic>Degradation</topic><topic>Machine learning</topic><topic>Neural networks</topic><topic>Robot control</topic><topic>Robotics and automation</topic><topic>Sampling methods</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><topic>USA Councils</topic><toplevel>online_resources</toplevel><creatorcontrib>Younggeun Choi</creatorcontrib><creatorcontrib>Shin-Young Cheong</creatorcontrib><creatorcontrib>Schweighofer, N.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Younggeun Choi</au><au>Shin-Young Cheong</au><au>Schweighofer, N.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Local Online Support Vector Regression for Learning Control</atitle><btitle>2007 International Symposium on Computational Intelligence in Robotics and Automation</btitle><stitle>CIRA</stitle><date>2007-06</date><risdate>2007</risdate><spage>13</spage><epage>18</epage><pages>13-18</pages><isbn>1424407893</isbn><isbn>9781424407897</isbn><eisbn>1424407907</eisbn><eisbn>9781424407903</eisbn><abstract>Support vector regression (SVR) is a class of machine learning technique that has been successfully applied to low-level learning control in robotics. Because of the large amount of computation required by SVR, however, most studies have used a batch mode. Although a recently developed online form of SVR shows faster learning performance than batch SVR, the amount of computation required by online SVR prevent its use in real-time robot learning control, which requires short sampling time. Here, we present a novel method, Local online SVR for Learning control, or LoSVR, that extends online SVR with a windowing method. We demonstrate the performance of LoSVR in learning the inverse dynamics of both a simulated two-joint robot and a real one-link robot arm. Our results show that, in both cases, LoSVR can learn the inverse dynamics on-line faster and with a better accuracy than batch SVR.</abstract><pub>IEEE</pub><doi>10.1109/CIRA.2007.382883</doi><tpages>6</tpages></addata></record> |
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subjects | Computational intelligence Degradation Machine learning Neural networks Robot control Robotics and automation Sampling methods Support vector machine classification Support vector machines USA Councils |
title | Local Online Support Vector Regression for Learning Control |
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