Learning probabilistic models for optimal visual servo control of dynamic manipulation
We present an experiment in sequential visual servo control of a dynamic manipulation task with unknown equations of motion and feedback from an uncalibrated camera. Our algorithm constructs a model of a Markov decision process (MDP) by means of grounding states in observed trajectories, and uses th...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1073 vol.1 |
|---|---|
| container_issue | |
| container_start_page | 1068 |
| container_title | |
| container_volume | 1 |
| creator | Nikovski, D. Nourbakhsh, I. |
| description | We present an experiment in sequential visual servo control of a dynamic manipulation task with unknown equations of motion and feedback from an uncalibrated camera. Our algorithm constructs a model of a Markov decision process (MDP) by means of grounding states in observed trajectories, and uses the model to find a control policy based on visual input, which maximizes a prespecified optimal control criterion balancing performance and control effort. |
| doi_str_mv | 10.1109/IRDS.2002.1041533 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>pascalfrancis_6IE</sourceid><recordid>TN_cdi_ieee_primary_1041533</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1041533</ieee_id><sourcerecordid>15672236</sourcerecordid><originalsourceid>FETCH-LOGICAL-i178t-4cc417c0baf7f068cff9c77fa72f3f8887f1f2986d3cc42919b31b3be560371e3</originalsourceid><addsrcrecordid>eNpFkEtLxDAUhQMiqOP8AHGTjcvWPNomWcr4GigIvrZDmuZKJE1K0hmYf2-lgmdzFue7l3sPQleUlJQSdbt9vX8rGSGspKSiNecn6IIISbjgSooztM75m8yqqpordY4-W6tTcOELjyl2unPe5ckZPMTe-owhJhzHyQ3a44PL-9myTYeITQxTih5HwP0x6OF3RAc37r2eXAyX6BS0z3b95yv08fjwvnku2pen7eauLRwVcioqYyoqDOk0CCCNNADKCAFaMOAgpRRAgSnZ9HwmmaKq47Tjna2b-SNq-QrdLHtHnY32kHQwLu_GNF-cjjtaN4Ix3szc9cI5a-1_vFTEfwA4lF3H</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Learning probabilistic models for optimal visual servo control of dynamic manipulation</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Nikovski, D. ; Nourbakhsh, I.</creator><creatorcontrib>Nikovski, D. ; Nourbakhsh, I.</creatorcontrib><description>We present an experiment in sequential visual servo control of a dynamic manipulation task with unknown equations of motion and feedback from an uncalibrated camera. Our algorithm constructs a model of a Markov decision process (MDP) by means of grounding states in observed trajectories, and uses the model to find a control policy based on visual input, which maximizes a prespecified optimal control criterion balancing performance and control effort.</description><identifier>ISBN: 0780373987</identifier><identifier>ISBN: 9780780373983</identifier><identifier>DOI: 10.1109/IRDS.2002.1041533</identifier><language>eng</language><publisher>Piscataway NJ: IEEE</publisher><subject>Adaptive control ; Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Control systems ; Control theory. Systems ; Cost function ; Exact sciences and technology ; Kinematics ; Manipulator dynamics ; Motion control ; Optimal control ; Pattern recognition. Digital image processing. Computational geometry ; Robot sensing systems ; Robotics ; Servomechanisms ; Servosystems</subject><ispartof>IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002, Vol.1, p.1068-1073 vol.1</ispartof><rights>2004 INIST-CNRS</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1041533$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,4050,4051,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1041533$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15672236$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Nikovski, D.</creatorcontrib><creatorcontrib>Nourbakhsh, I.</creatorcontrib><title>Learning probabilistic models for optimal visual servo control of dynamic manipulation</title><title>IEEE/RSJ International Conference on Intelligent Robots and Systems</title><addtitle>IRDS</addtitle><description>We present an experiment in sequential visual servo control of a dynamic manipulation task with unknown equations of motion and feedback from an uncalibrated camera. Our algorithm constructs a model of a Markov decision process (MDP) by means of grounding states in observed trajectories, and uses the model to find a control policy based on visual input, which maximizes a prespecified optimal control criterion balancing performance and control effort.</description><subject>Adaptive control</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Control systems</subject><subject>Control theory. Systems</subject><subject>Cost function</subject><subject>Exact sciences and technology</subject><subject>Kinematics</subject><subject>Manipulator dynamics</subject><subject>Motion control</subject><subject>Optimal control</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Robot sensing systems</subject><subject>Robotics</subject><subject>Servomechanisms</subject><subject>Servosystems</subject><isbn>0780373987</isbn><isbn>9780780373983</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2002</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkEtLxDAUhQMiqOP8AHGTjcvWPNomWcr4GigIvrZDmuZKJE1K0hmYf2-lgmdzFue7l3sPQleUlJQSdbt9vX8rGSGspKSiNecn6IIISbjgSooztM75m8yqqpordY4-W6tTcOELjyl2unPe5ckZPMTe-owhJhzHyQ3a44PL-9myTYeITQxTih5HwP0x6OF3RAc37r2eXAyX6BS0z3b95yv08fjwvnku2pen7eauLRwVcioqYyoqDOk0CCCNNADKCAFaMOAgpRRAgSnZ9HwmmaKq47Tjna2b-SNq-QrdLHtHnY32kHQwLu_GNF-cjjtaN4Ix3szc9cI5a-1_vFTEfwA4lF3H</recordid><startdate>2002</startdate><enddate>2002</enddate><creator>Nikovski, D.</creator><creator>Nourbakhsh, I.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>IQODW</scope></search><sort><creationdate>2002</creationdate><title>Learning probabilistic models for optimal visual servo control of dynamic manipulation</title><author>Nikovski, D. ; Nourbakhsh, I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i178t-4cc417c0baf7f068cff9c77fa72f3f8887f1f2986d3cc42919b31b3be560371e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Adaptive control</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Control systems</topic><topic>Control theory. Systems</topic><topic>Cost function</topic><topic>Exact sciences and technology</topic><topic>Kinematics</topic><topic>Manipulator dynamics</topic><topic>Motion control</topic><topic>Optimal control</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Robot sensing systems</topic><topic>Robotics</topic><topic>Servomechanisms</topic><topic>Servosystems</topic><toplevel>online_resources</toplevel><creatorcontrib>Nikovski, D.</creatorcontrib><creatorcontrib>Nourbakhsh, I.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Nikovski, D.</au><au>Nourbakhsh, I.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Learning probabilistic models for optimal visual servo control of dynamic manipulation</atitle><btitle>IEEE/RSJ International Conference on Intelligent Robots and Systems</btitle><stitle>IRDS</stitle><date>2002</date><risdate>2002</risdate><volume>1</volume><spage>1068</spage><epage>1073 vol.1</epage><pages>1068-1073 vol.1</pages><isbn>0780373987</isbn><isbn>9780780373983</isbn><abstract>We present an experiment in sequential visual servo control of a dynamic manipulation task with unknown equations of motion and feedback from an uncalibrated camera. Our algorithm constructs a model of a Markov decision process (MDP) by means of grounding states in observed trajectories, and uses the model to find a control policy based on visual input, which maximizes a prespecified optimal control criterion balancing performance and control effort.</abstract><cop>Piscataway NJ</cop><pub>IEEE</pub><doi>10.1109/IRDS.2002.1041533</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 0780373987 |
| ispartof | IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002, Vol.1, p.1068-1073 vol.1 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_1041533 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Adaptive control Applied sciences Artificial intelligence Computer science control theory systems Control systems Control theory. Systems Cost function Exact sciences and technology Kinematics Manipulator dynamics Motion control Optimal control Pattern recognition. Digital image processing. Computational geometry Robot sensing systems Robotics Servomechanisms Servosystems |
| title | Learning probabilistic models for optimal visual servo control of dynamic manipulation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T04%3A03%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pascalfrancis_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Learning%20probabilistic%20models%20for%20optimal%20visual%20servo%20control%20of%20dynamic%20manipulation&rft.btitle=IEEE/RSJ%20International%20Conference%20on%20Intelligent%20Robots%20and%20Systems&rft.au=Nikovski,%20D.&rft.date=2002&rft.volume=1&rft.spage=1068&rft.epage=1073%20vol.1&rft.pages=1068-1073%20vol.1&rft.isbn=0780373987&rft.isbn_list=9780780373983&rft_id=info:doi/10.1109/IRDS.2002.1041533&rft_dat=%3Cpascalfrancis_6IE%3E15672236%3C/pascalfrancis_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=1041533&rfr_iscdi=true |