Skill acquisition from human demonstration using a hidden Markov model

A new approach to skill acquisition in assembly is proposed. An assembly skill is represented by a hybrid dynamic system where a discrete event controller models the skill at the task level. The output of the discrete event controller provides the reference commands for the underlying robot controll...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Hovland, G.E., Sikka, P., McCarragher, B.J.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 2711 vol.3
container_issue
container_start_page 2706
container_title
container_volume 3
creator Hovland, G.E.
Sikka, P.
McCarragher, B.J.
description A new approach to skill acquisition in assembly is proposed. An assembly skill is represented by a hybrid dynamic system where a discrete event controller models the skill at the task level. The output of the discrete event controller provides the reference commands for the underlying robot controller. This structure is naturally encoded by a hidden Markov model (HMM). The HMM parameters are obtained by training on sensory data from human demonstrations of the skill. Currently, assembly tasks have to be performed by human operators or by robots using expensive fixtures. Our approach transfers the assembly skill from an expert human operator to the robot, thus making it possible for a robot to perform assembly tasks without the use of expensive fixtures.
doi_str_mv 10.1109/ROBOT.1996.506571
format Conference Proceeding
fullrecord <record><control><sourceid>proquest_6IE</sourceid><recordid>TN_cdi_ieee_primary_506571</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>506571</ieee_id><sourcerecordid>15727903</sourcerecordid><originalsourceid>FETCH-LOGICAL-i269t-b0e301444d2ccd53b9f5e8aabade0641096756641c5448e8f0c6b001015c53fc3</originalsourceid><addsrcrecordid>eNotkE1LAzEYhIMfYFv9AXrKydvWN5vvo5ZWhUpBK3hbsknWxu5u2s2u4L-3WE8zMMPAMwhdE5gSAvrudfWwWk-J1mLKQXBJTtAo51JmoOTHKRqDVEBzrRScoREBDhmTub5A45S-AIBSIUZo8bYNdY2N3Q8hhT7EFlddbPBmaEyLnW9im_rO_AVDCu0nNngTnPMtfjHdNn7jJjpfX6LzytTJX_3rBL0v5uvZU7ZcPT7P7pdZyIXusxI8BcIYc7m1jtNSV9wrY0rjPAh2oBKSi4OxnDHlVQVWlAAECLecVpZO0O1xd9fF_eBTXzQhWV_XpvVxSAXhMpf6wDZBN8di8N4Xuy40pvspjj_RXzV2Wm4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>15727903</pqid></control><display><type>conference_proceeding</type><title>Skill acquisition from human demonstration using a hidden Markov model</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Hovland, G.E. ; Sikka, P. ; McCarragher, B.J.</creator><creatorcontrib>Hovland, G.E. ; Sikka, P. ; McCarragher, B.J.</creatorcontrib><description>A new approach to skill acquisition in assembly is proposed. An assembly skill is represented by a hybrid dynamic system where a discrete event controller models the skill at the task level. The output of the discrete event controller provides the reference commands for the underlying robot controller. This structure is naturally encoded by a hidden Markov model (HMM). The HMM parameters are obtained by training on sensory data from human demonstrations of the skill. Currently, assembly tasks have to be performed by human operators or by robots using expensive fixtures. Our approach transfers the assembly skill from an expert human operator to the robot, thus making it possible for a robot to perform assembly tasks without the use of expensive fixtures.</description><identifier>ISSN: 1050-4729</identifier><identifier>ISBN: 0780329880</identifier><identifier>ISBN: 9780780329881</identifier><identifier>EISSN: 2577-087X</identifier><identifier>DOI: 10.1109/ROBOT.1996.506571</identifier><language>eng</language><publisher>IEEE</publisher><subject>Assembly systems ; Fixtures ; Hidden Markov models ; Humans ; Monitoring ; Process control ; Robot control ; Robot sensing systems ; Robotic assembly ; Uncertainty</subject><ispartof>Proceedings - IEEE International Conference on Robotics and Automation, 1996, Vol.3, p.2706-2711 vol.3</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/506571$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,4050,4051,25140,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/506571$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Hovland, G.E.</creatorcontrib><creatorcontrib>Sikka, P.</creatorcontrib><creatorcontrib>McCarragher, B.J.</creatorcontrib><title>Skill acquisition from human demonstration using a hidden Markov model</title><title>Proceedings - IEEE International Conference on Robotics and Automation</title><addtitle>ROBOT</addtitle><description>A new approach to skill acquisition in assembly is proposed. An assembly skill is represented by a hybrid dynamic system where a discrete event controller models the skill at the task level. The output of the discrete event controller provides the reference commands for the underlying robot controller. This structure is naturally encoded by a hidden Markov model (HMM). The HMM parameters are obtained by training on sensory data from human demonstrations of the skill. Currently, assembly tasks have to be performed by human operators or by robots using expensive fixtures. Our approach transfers the assembly skill from an expert human operator to the robot, thus making it possible for a robot to perform assembly tasks without the use of expensive fixtures.</description><subject>Assembly systems</subject><subject>Fixtures</subject><subject>Hidden Markov models</subject><subject>Humans</subject><subject>Monitoring</subject><subject>Process control</subject><subject>Robot control</subject><subject>Robot sensing systems</subject><subject>Robotic assembly</subject><subject>Uncertainty</subject><issn>1050-4729</issn><issn>2577-087X</issn><isbn>0780329880</isbn><isbn>9780780329881</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1996</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkE1LAzEYhIMfYFv9AXrKydvWN5vvo5ZWhUpBK3hbsknWxu5u2s2u4L-3WE8zMMPAMwhdE5gSAvrudfWwWk-J1mLKQXBJTtAo51JmoOTHKRqDVEBzrRScoREBDhmTub5A45S-AIBSIUZo8bYNdY2N3Q8hhT7EFlddbPBmaEyLnW9im_rO_AVDCu0nNngTnPMtfjHdNn7jJjpfX6LzytTJX_3rBL0v5uvZU7ZcPT7P7pdZyIXusxI8BcIYc7m1jtNSV9wrY0rjPAh2oBKSi4OxnDHlVQVWlAAECLecVpZO0O1xd9fF_eBTXzQhWV_XpvVxSAXhMpf6wDZBN8di8N4Xuy40pvspjj_RXzV2Wm4</recordid><startdate>1996</startdate><enddate>1996</enddate><creator>Hovland, G.E.</creator><creator>Sikka, P.</creator><creator>McCarragher, B.J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>1996</creationdate><title>Skill acquisition from human demonstration using a hidden Markov model</title><author>Hovland, G.E. ; Sikka, P. ; McCarragher, B.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i269t-b0e301444d2ccd53b9f5e8aabade0641096756641c5448e8f0c6b001015c53fc3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Assembly systems</topic><topic>Fixtures</topic><topic>Hidden Markov models</topic><topic>Humans</topic><topic>Monitoring</topic><topic>Process control</topic><topic>Robot control</topic><topic>Robot sensing systems</topic><topic>Robotic assembly</topic><topic>Uncertainty</topic><toplevel>online_resources</toplevel><creatorcontrib>Hovland, G.E.</creatorcontrib><creatorcontrib>Sikka, P.</creatorcontrib><creatorcontrib>McCarragher, B.J.</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><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hovland, G.E.</au><au>Sikka, P.</au><au>McCarragher, B.J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Skill acquisition from human demonstration using a hidden Markov model</atitle><btitle>Proceedings - IEEE International Conference on Robotics and Automation</btitle><stitle>ROBOT</stitle><date>1996</date><risdate>1996</risdate><volume>3</volume><spage>2706</spage><epage>2711 vol.3</epage><pages>2706-2711 vol.3</pages><issn>1050-4729</issn><eissn>2577-087X</eissn><isbn>0780329880</isbn><isbn>9780780329881</isbn><abstract>A new approach to skill acquisition in assembly is proposed. An assembly skill is represented by a hybrid dynamic system where a discrete event controller models the skill at the task level. The output of the discrete event controller provides the reference commands for the underlying robot controller. This structure is naturally encoded by a hidden Markov model (HMM). The HMM parameters are obtained by training on sensory data from human demonstrations of the skill. Currently, assembly tasks have to be performed by human operators or by robots using expensive fixtures. Our approach transfers the assembly skill from an expert human operator to the robot, thus making it possible for a robot to perform assembly tasks without the use of expensive fixtures.</abstract><pub>IEEE</pub><doi>10.1109/ROBOT.1996.506571</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 1050-4729
ispartof Proceedings - IEEE International Conference on Robotics and Automation, 1996, Vol.3, p.2706-2711 vol.3
issn 1050-4729
2577-087X
language eng
recordid cdi_ieee_primary_506571
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Assembly systems
Fixtures
Hidden Markov models
Humans
Monitoring
Process control
Robot control
Robot sensing systems
Robotic assembly
Uncertainty
title Skill acquisition from human demonstration using a hidden Markov model
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T19%3A42%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Skill%20acquisition%20from%20human%20demonstration%20using%20a%20hidden%20Markov%20model&rft.btitle=Proceedings%20-%20IEEE%20International%20Conference%20on%20Robotics%20and%20Automation&rft.au=Hovland,%20G.E.&rft.date=1996&rft.volume=3&rft.spage=2706&rft.epage=2711%20vol.3&rft.pages=2706-2711%20vol.3&rft.issn=1050-4729&rft.eissn=2577-087X&rft.isbn=0780329880&rft.isbn_list=9780780329881&rft_id=info:doi/10.1109/ROBOT.1996.506571&rft_dat=%3Cproquest_6IE%3E15727903%3C/proquest_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=15727903&rft_id=info:pmid/&rft_ieee_id=506571&rfr_iscdi=true