{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T05:03:15Z","timestamp":1779685395711,"version":"3.53.1"},"reference-count":33,"publisher":"Informa UK Limited","issue":"8","license":[{"start":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:00:00Z","timestamp":1776470400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"JSPS","doi-asserted-by":"publisher","award":["JP25K01191"],"award-info":[{"award-number":["JP25K01191"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001863","name":"New Energy and Industrial Technology Development Organization","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001863","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Advanced Robotics"],"published-print":{"date-parts":[[2026,4,18]]},"DOI":"10.1080\/01691864.2026.2651286","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T04:35:04Z","timestamp":1777437304000},"page":"383-396","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised motion primitive identification under translational physical constraints in contact-rich tasks"],"prefix":"10.1080","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2297-819X","authenticated-orcid":false,"given":"Ryoga","family":"Oishi","sequence":"first","affiliation":[{"name":"Saitama University","place":["Saitama, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5182-5649","authenticated-orcid":false,"given":"Sho","family":"Sakaino","sequence":"additional","affiliation":[{"name":"University of Tsukuba","place":["Tsukuba, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4532-4514","authenticated-orcid":false,"given":"Toshiaki","family":"Tsuji","sequence":"additional","affiliation":[{"name":"Saitama University","place":["Saitama, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"301","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/02783649261417694"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3185651"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364917713116"},{"key":"e_1_3_2_5_1","doi-asserted-by":"crossref","unstructured":"Komatsu T Ohmura Y Kuniyoshi Y. Unsupervised temporal segmentation using models that discriminate between demonstrations and unintentional actions. In: Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems. Prague: Czech Republic; 2021. p. 8951\u20138956.","DOI":"10.1109\/IROS51168.2021.9636688"},{"key":"e_1_3_2_6_1","doi-asserted-by":"crossref","unstructured":"Yu T Abbeel P Levine S et al. One-shot composition of vision-based skills from demonstration. In: Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems. Macau China; 2019. p. 2643\u20132650.","DOI":"10.1109\/IROS40897.2019.8967745"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3225914"},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.1163\/156855307782148578"},{"key":"e_1_3_2_9_1","doi-asserted-by":"crossref","unstructured":"Nasiriany S Liu H Zhu Y. Augmenting reinforcement learning with behavior primitives for diverse manipulation tasks. In: Proceedings of the IEEE International Conference on Robotics and Automation. Philadelphia PA; 2022. p. 7477\u20137484.","DOI":"10.1109\/ICRA46639.2022.9812140"},{"key":"e_1_3_2_10_1","doi-asserted-by":"crossref","unstructured":"Zhang X Jin S Wang C et al. Learning insertion primitives with discrete-continuous hybrid action space for robotic assembly tasks. In: Proceedings of the IEEE International Conference on Robotics and Automation. Philadelphia PA; 2022. p. 9881\u20139887.","DOI":"10.1109\/ICRA46639.2022.9811973"},{"key":"e_1_3_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3443610"},{"key":"e_1_3_2_12_1","doi-asserted-by":"crossref","unstructured":"Niekum S Osentoski S Konidaris G et al. Learning and generalization of complex tasks from unstructured demonstrations. In: Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems. Vilamoura-Algarve Portugal; 2012. p. 5239\u20135246.","DOI":"10.1109\/IROS.2012.6386006"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3264213"},{"key":"e_1_3_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2024.3353075"},{"issue":"5","key":"e_1_3_2_15_1","first-page":"1000","article-title":"Motion generation based on contact state estimation using two-stage clustering","volume":"12","author":"Takeuchi K","year":"2023","unstructured":"Takeuchi K, Sakaino S, Tsuji T. Motion generation based on contact state estimation using two-stage clustering. IEEJ J Ind Appl. 2023;12(5):1000\u20131007.","journal-title":"IEEJ J Ind Appl"},{"key":"e_1_3_2_16_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013254724861"},{"key":"e_1_3_2_17_1","doi-asserted-by":"crossref","unstructured":"Senger L Schr\u00f6er M Metzen JH et al. Velocity-based multiple change-point inference for unsupervised segmentation of human movement behavior. In: Proceedings of the International Conference on Pattern Recognition. Stockholm Sweden; 2014. p. 4564\u20134569.","DOI":"10.1109\/ICPR.2014.781"},{"key":"e_1_3_2_18_1","unstructured":"Barbi\u010d J Safonova A Pan J et al. Segmenting motion capture data into distinct behaviors. In: Proceedings of Graphics Interface. London; 2004. p. 185\u2013194."},{"key":"e_1_3_2_19_1","doi-asserted-by":"crossref","unstructured":"Herrero EG Ho J Khatib O. Understanding and segmenting human demonstrations into reusable compliant primitives. In: Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems. Prague Czech Republic; 2021. p. 9437\u20139444.","DOI":"10.1109\/IROS51168.2021.9636523"},{"key":"e_1_3_2_20_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2017.00067"},{"key":"e_1_3_2_21_1","doi-asserted-by":"crossref","unstructured":"Kroemer O van Hoof H Neumann G et al. Learning to predict phases of manipulation tasks as hidden states. In: Proceedings of the IEEE International Conference on Robotics and Automation: Hong Kong; 2014. p. 4009\u20134014.","DOI":"10.1109\/ICRA.2014.6907441"},{"key":"e_1_3_2_22_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364911426178"},{"key":"e_1_3_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3303828"},{"key":"e_1_3_2_24_1","doi-asserted-by":"crossref","unstructured":"Su Z Kroemer O Loeb GE et al. Learning manipulation graphs from demonstrations using multimodal sensory signals. In: Proceedings of the IEEE International Conference on Robotics and Automation. Brisbane; 2018. p. 2758\u20132765.","DOI":"10.1109\/ICRA.2018.8461121"},{"key":"e_1_3_2_25_1","doi-asserted-by":"crossref","unstructured":"Kober J Gienger M Steil JJ. Learning movement primitives for force interaction tasks. In: Proceedings of the IEEE International Conference on Robotics and Automation. Seattle WA; 2015. p. 3192\u20133199.","DOI":"10.1109\/ICRA.2015.7139639"},{"key":"e_1_3_2_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02527-8"},{"key":"e_1_3_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2795648"},{"key":"e_1_3_2_28_1","doi-asserted-by":"crossref","unstructured":"S\u00f8rensen SLB Savarimuthu TR Iturrate I. Robot task primitive segmentation from demonstrations using only built-in kinematic state and force-torque sensor data. In: Proceedings of the IEEE International Conference on Automation Science and Engineering. Auckland; 2023. p. 1\u20137.","DOI":"10.1109\/CASE56687.2023.10260448"},{"issue":"5","key":"e_1_3_2_29_1","first-page":"489","article-title":"Contact state estimation for peg-in-hole task based on coordinate transformation of forces using principal component analysis","volume":"42","author":"Oishi R","year":"2024","unstructured":"Oishi R, Tsuji T. Contact state estimation for peg-in-hole task based on coordinate transformation of forces using principal component analysis. J Rob Soc Jpn. 2024;42(5):489\u2013492.","journal-title":"J Rob Soc Jpn"},{"key":"e_1_3_2_30_1","doi-asserted-by":"crossref","unstructured":"Tang T Lin HC Zhao Y et al. Teach industrial robots peg-hole-insertion by human demonstration. In: Proceedings of the IEEE International Conference on Advanced Intelligent Mechatronics. Banff AB; 2016. p. 488\u2013494.","DOI":"10.1109\/AIM.2016.7576815"},{"key":"e_1_3_2_31_1","doi-asserted-by":"publisher","DOI":"10.1108\/IR-07-2014-0363"},{"key":"e_1_3_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3335768"},{"key":"e_1_3_2_33_1","unstructured":"Adams RP MacKay DJC. Bayesian online changepoint detection. Preprint; 2007. Available from: arXiv:0710.3742"},{"key":"e_1_3_2_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/nav.v2:1\/2"}],"container-title":["Advanced Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/01691864.2026.2651286","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T04:28:01Z","timestamp":1779683281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/01691864.2026.2651286"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,18]]},"references-count":33,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,4,18]]}},"alternative-id":["10.1080\/01691864.2026.2651286"],"URL":"https:\/\/doi.org\/10.1080\/01691864.2026.2651286","relation":{},"ISSN":["0169-1864","1568-5535"],"issn-type":[{"value":"0169-1864","type":"print"},{"value":"1568-5535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,18]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tadr20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tadr20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2025-07-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-29","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}