{"id":"https://openalex.org/W4312342945","doi":"https://doi.org/10.1109/iros47612.2022.9981610","title":"Tactile-Sensitive NewtonianVAE for High-Accuracy Industrial Connector Insertion","display_name":"Tactile-Sensitive NewtonianVAE for High-Accuracy Industrial Connector Insertion","publication_year":2022,"publication_date":"2022-10-23","ids":{"openalex":"https://openalex.org/W4312342945","doi":"https://doi.org/10.1109/iros47612.2022.9981610"},"language":"en","primary_location":{"id":"doi:10.1109/iros47612.2022.9981610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros47612.2022.9981610","pdf_url":null,"source":{"id":"https://openalex.org/S4363607704","display_name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109594509","display_name":"Ryo Okumura","orcid":null},"institutions":[{"id":"https://openalex.org/I1283155146","display_name":"Panasonic (Japan)","ror":"https://ror.org/011tm7n37","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283155146"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ryo Okumura","raw_affiliation_strings":["Digital&AI Technology Center, Panasonic Holdings Corporation,Technology Division,Japan"],"affiliations":[{"raw_affiliation_string":"Digital&AI Technology Center, Panasonic Holdings Corporation,Technology Division,Japan","institution_ids":["https://openalex.org/I1283155146"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010288134","display_name":"Nobuki Nishio","orcid":null},"institutions":[{"id":"https://openalex.org/I1283155146","display_name":"Panasonic (Japan)","ror":"https://ror.org/011tm7n37","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283155146"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Nobuki Nishio","raw_affiliation_strings":["Panasonic Connect Co., Ltd.,R&D Division,Japan"],"affiliations":[{"raw_affiliation_string":"Panasonic Connect Co., Ltd.,R&D Division,Japan","institution_ids":["https://openalex.org/I1283155146"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023160093","display_name":"Tadahiro Taniguchi","orcid":"https://orcid.org/0000-0002-5682-2076"},"institutions":[{"id":"https://openalex.org/I1283155146","display_name":"Panasonic (Japan)","ror":"https://ror.org/011tm7n37","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283155146"]},{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tadahiro Taniguchi","raw_affiliation_strings":["Panasonic Connect Co., Ltd.,R&D Division,Japan","Ritsumeikan University, College of Information Science and Engineering, Japan"],"affiliations":[{"raw_affiliation_string":"Panasonic Connect Co., Ltd.,R&D Division,Japan","institution_ids":["https://openalex.org/I1283155146"]},{"raw_affiliation_string":"Ritsumeikan University, College of Information Science and Engineering, Japan","institution_ids":["https://openalex.org/I135768898"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5109594509"],"corresponding_institution_ids":["https://openalex.org/I1283155146"],"apc_list":null,"apc_paid":null,"fwci":4.9547,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.97350859,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4625","last_page":"4631"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10914","display_name":"Tactile and Sensory Interactions","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6507670879364014},{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.5489611029624939},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5349684357643127},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.49994993209838867},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.45503732562065125},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.42619985342025757},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.3370268642902374}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6507670879364014},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.5489611029624939},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5349684357643127},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.49994993209838867},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45503732562065125},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.42619985342025757},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3370268642902374},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros47612.2022.9981610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros47612.2022.9981610","pdf_url":null,"source":{"id":"https://openalex.org/S4363607704","display_name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W130216483","https://openalex.org/W834081922","https://openalex.org/W1522301498","https://openalex.org/W2073787051","https://openalex.org/W2108598243","https://openalex.org/W2121103318","https://openalex.org/W2126909264","https://openalex.org/W2136719407","https://openalex.org/W2158782408","https://openalex.org/W2169364846","https://openalex.org/W2194775991","https://openalex.org/W2421020186","https://openalex.org/W2771494584","https://openalex.org/W2799151646","https://openalex.org/W2842511635","https://openalex.org/W2890208753","https://openalex.org/W2966271811","https://openalex.org/W2967936796","https://openalex.org/W2995298643","https://openalex.org/W3012514211","https://openalex.org/W3033324992","https://openalex.org/W3036642894","https://openalex.org/W3130717831","https://openalex.org/W3132285497","https://openalex.org/W3132689960","https://openalex.org/W3173693019","https://openalex.org/W3177145475","https://openalex.org/W3206042616","https://openalex.org/W3207359859","https://openalex.org/W4288294128","https://openalex.org/W4289294484","https://openalex.org/W4297808394","https://openalex.org/W4312299945","https://openalex.org/W4387525134","https://openalex.org/W6623316541","https://openalex.org/W6631190155","https://openalex.org/W6634817459","https://openalex.org/W6678367057","https://openalex.org/W6750186571","https://openalex.org/W6754184789","https://openalex.org/W6756256016","https://openalex.org/W6765407481","https://openalex.org/W6771217966","https://openalex.org/W6774733536","https://openalex.org/W6779265984","https://openalex.org/W6787971688","https://openalex.org/W6829554866"],"related_works":["https://openalex.org/W2163296013","https://openalex.org/W2743859443","https://openalex.org/W2326995835","https://openalex.org/W165915117","https://openalex.org/W2059402478","https://openalex.org/W2123347777","https://openalex.org/W3021551196","https://openalex.org/W2736638679","https://openalex.org/W4313046826","https://openalex.org/W1968716783"],"abstract_inverted_index":{"An":[0],"industrial":[1,96],"connector":[2,134,245],"insertion":[3,135,153,246],"task":[4,247],"requires":[5],"submillimeter":[6],"positioning":[7,240],"and":[8,26,53,128,150,173,177,202,221,237],"grasp":[9,113,137,158,222,261],"pose":[10,22,114,138,159,223,258,262],"compensation":[11,115,263],"for":[12,30,39],"a":[13,24,63,132,146,233],"plug.":[14,162],"Thus,":[15],"highly":[16],"accurate":[17],"estimation":[18],"of":[19,65,89,160],"the":[20,32,61,66,86,106,112,117,141,152,157,161,166,187,195,243,249],"relative":[21],"between":[23],"plug":[25],"socket":[27],"is":[28,101,184],"fundamental":[29],"achieving":[31],"task.":[33],"World":[34],"models":[35],"are":[36,179],"promising":[37],"technologies":[38],"visuomotor":[40],"control":[41,81,183],"because":[42],"they":[43],"obtain":[44],"appropriate":[45],"state":[46],"representation":[47],"to":[48,73,77,94,131],"jointly":[49],"optimize":[50],"feature":[51],"extraction":[52],"latent":[54,70,87,119,167,189],"dynamics":[55],"model.":[56],"Recent":[57],"studies":[58],"show":[59],"that":[60,194,204,228],"Newto-nianVAE,":[62],"type":[64],"world":[67],"model,":[68],"acquires":[69],"space":[71,88,168],"equivalent":[72],"mapping":[74],"from":[75],"images":[76],"physical":[78,99,142,250],"coordinates.":[79],"Proportional":[80],"can":[82,214],"be":[83,215],"achieved":[84,232],"in":[85,98,116,140,169,186,199,242,248],"NewtonianVAE.":[90],"However,":[91],"applying":[92],"NewtonianVAE":[93,197],"high-accuracy":[95],"tasks":[97],"environments":[100],"an":[102,170],"open":[103],"problem.":[104],"Moreover,":[105,191],"existing":[107],"framework":[108],"does":[109],"not":[110],"consider":[111],"obtained":[118,188,217],"space.":[120,190],"In":[121],"this":[122],"work,":[123],"we":[124,192],"proposed":[125,230],"tactile-sensitive":[126],"Newtonian-VAE":[127],"applied":[129],"it":[130],"USB":[133,244],"with":[136,260],"variation":[139],"environments.":[143],"We":[144,226],"adopted":[145],"GelSight-type":[147],"tactile":[148],"sensor":[149],"estimated":[151],"position":[154],"compensated":[155],"by":[156],"Our":[163],"method":[164,231],"trains":[165],"end-to-end":[171],"manner,":[172],"no":[174],"additional":[175],"engineering":[176],"annotation":[178],"required.":[180],"Simple":[181],"proportional":[182],"available":[185],"showed":[193],"original":[196],"fails":[198],"some":[200],"situations,":[201],"demonstrated":[203,227],"domain":[205,212],"knowledge":[206,213],"induction":[207],"improves":[208],"model":[209],"accuracy.":[210],"This":[211],"easily":[216],"using":[218,264],"robot":[219],"specification":[220],"error":[224],"measurement.":[225],"our":[229],"100%":[234],"success":[235],"rate":[236],"0.3":[238],"mm":[239],"accuracy":[241],"environment.":[251],"It":[252],"outperformed":[253],"SOTA":[254],"CNN-based":[255],"two-stage":[256],"goal":[257],"regression":[259],"coordinate":[265],"transformation.":[266]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-02-22T13:39:03.778224","created_date":"2025-10-10T00:00:00"}