{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T02:37:09Z","timestamp":1770777429709,"version":"3.50.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T00:00:00Z","timestamp":1633651200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T00:00:00Z","timestamp":1633651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["410916101"],"award-info":[{"award-number":["410916101"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In shared-control teleoperation, rather than directly executing a user\u2019s input, a robot system assists the user via part of autonomy to reduce user\u2019s workload and improve efficiency. Effective assistance is challenging task as it requires correctly inferring the user intent, including predicting the user goal from all possible candidates as well as inferring the user preferred movement in the next step. In this paper, we present a probabilistic formulation for inferring the user intent by taking consideration of user behavior. In our approach, the user behavior is learned from demonstrations, which is then incorporated in goal prediction and path planning. Using maximum entropy principle, two goal prediction methods are tailored according to the similarity metrics between user\u2019s short-term movements and the learned user behavior. We have validated the proposed approaches with a user study\u2014examining the performance of our goal prediction methods in approaching tasks in multiple goals scenario. The results show that our approaches perform well in user goal prediction and are able to respond quickly to dynamic changing of the user\u2019s goals. Comparison analysis shows that the proposed approaches outperform the existing methods especially in scenarios with goal ambiguity.<\/jats:p>","DOI":"10.1007\/s40747-021-00533-4","type":"journal-article","created":{"date-parts":[[2021,10,9]],"date-time":"2021-10-09T05:13:35Z","timestamp":1633756415000},"page":"2971-2981","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Intent inference in shared-control teleoperation system in consideration of user behavior"],"prefix":"10.1007","volume":"8","author":[{"given":"Liangliang","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4315-0864","authenticated-orcid":false,"given":"Qiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"James","family":"Lam","sequence":"additional","affiliation":[]},{"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhengyou","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,8]]},"reference":[{"key":"533_CR1","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1007\/978-3-319-32552-1_43","volume-title":"Springer handbook of robotics","author":"G Niemeyer","year":"2016","unstructured":"Niemeyer G, Preusche C, Stramigioli S, Lee D (2016) Telerobotics. In: Siciliano B, Khatib O (eds) Springer handbook of robotics, 2nd edn. Springer, Berlin, pp 1085\u20131108","edition":"2"},{"key":"533_CR2","volume-title":"Human factors in technology","author":"R Goertz","year":"1963","unstructured":"Goertz R (1963) Manipulators used for handling radioactive materials. In: Bennett EM (ed) Human factors in technology. McGraw-Hill, New York"},{"issue":"1","key":"533_CR3","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1109\/LRA.2017.2737046","volume":"3","author":"JI Lipton","year":"2018","unstructured":"Lipton JI, Fay AJ, Rus D (2018) Baxter\u2019s homunculus: virtual reality spaces for teleoperation in manufacturing. IEEE Robot Autom Lett 3(1):179\u2013186. https:\/\/doi.org\/10.1109\/LRA.2017.2737046","journal-title":"IEEE Robot Autom Lett"},{"key":"533_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.1997.620053","author":"P Aigner","year":"1997","unstructured":"Aigner P, McCarragher B (1997) Human integration into robot control utilising potential fields. Proc IEEE Int Conf Robot Autom. https:\/\/doi.org\/10.1109\/ROBOT.1997.620053","journal-title":"Proc IEEE Int Conf Robot Autom"},{"issue":"1","key":"533_CR5","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/LRA.2016.2593928","volume":"2","author":"D Gopinath","year":"2017","unstructured":"Gopinath D, Jain S, Argall BD (2017) Human-in-the-loop optimization of shared autonomy in assistive robotics. IEEE Robot Autom Lett 2(1):247\u2013254. https:\/\/doi.org\/10.1109\/LRA.2016.2593928","journal-title":"IEEE Robot Autom Lett"},{"issue":"7","key":"533_CR6","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1177\/0278364913490324","volume":"32","author":"AD Dragan","year":"2013","unstructured":"Dragan AD, Srinivasa SS (2013) A policy-blending formalism for shared control. Int J Robot Res 32(7):790\u2013805. https:\/\/doi.org\/10.1177\/0278364913490324","journal-title":"Int J Robot Res"},{"issue":"4","key":"533_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/1729881419857428","volume":"16","author":"B Xi","year":"2019","unstructured":"Xi B, Wang S, Ye X, Cai Y, Wang R (2019) A robotic shared control teleoperation method based on learning from demonstrations. Int J Adv Robot Syst 16(4):1\u201313. https:\/\/doi.org\/10.1177\/1729881419857428","journal-title":"Int J Adv Robot Syst"},{"key":"533_CR8","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8593766","author":"S Jain","year":"2018","unstructured":"Jain S, Argall B (2018) Recursive bayesian human intent recognition in shared-control robotics. Proc IEEE\/RSJ Int Conf Intell Robot Syst. https:\/\/doi.org\/10.1109\/IROS.2018.8593766","journal-title":"Proc IEEE\/RSJ Int Conf Intell Robot Syst"},{"key":"533_CR9","first-page":"298","volume-title":"AAAI fall symposium series: shared autonomy in research and practice","author":"A Henny","year":"2016","unstructured":"Henny A, Srinivasa SS (2016) Predicting user intent through eye gaze for shared autonomy. AAAI fall symposium series: shared autonomy in research and practice. AAAI Press, London, pp 298\u2013303"},{"key":"533_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2016.7759691","author":"VK Narayanan","year":"2016","unstructured":"Narayanan VK, Spalanzani A, Babel M (2016) A semi-autonomous framework for human-aware and user intention driven wheelchair mobility assistance. Proc IEEE\/RSJ Int Conf Intell Robot Syst. https:\/\/doi.org\/10.1109\/IROS.2016.7759691","journal-title":"Proc IEEE\/RSJ Int Conf Intell Robot Syst"},{"issue":"4","key":"533_CR11","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s10514-013-9350-3","volume":"35","author":"H Kris","year":"2013","unstructured":"Kris H (2013) Recognition, prediction, and planning for assisted teleoperation of freeform tasks. Auton Robot 35(4):241\u2013254. https:\/\/doi.org\/10.1007\/s10514-013-9350-3","journal-title":"Auton Robot"},{"key":"533_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2005.1570266","author":"W Yu","year":"2005","unstructured":"Yu W, Alqasemi R, Dubey R, Pernalete N (2005) Telemanipulation assistance based on motion intention recognition. Proc IEEE Int Conf Robot Autom. https:\/\/doi.org\/10.1109\/ROBOT.2005.1570266","journal-title":"Proc IEEE Int Conf Robot Autom"},{"issue":"8","key":"533_CR13","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1016\/j.robot.2007.11.005","volume":"56","author":"D Aarno","year":"2008","unstructured":"Aarno D, Kragic D (2008) Motion intention recognition in robot assisted applications. Robot Auton Syst 56(8):692\u2013705. https:\/\/doi.org\/10.1016\/j.robot.2007.11.005","journal-title":"Robot Auton Syst"},{"key":"533_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989633","author":"C Schultz","year":"2017","unstructured":"Schultz C, Gaurav S, Monfort M, Zhang L, Ziebart BD (2017) Goal-predictive robotic teleoperation from noisy sensors. Proc IEEE Int Conf Robot Autom. https:\/\/doi.org\/10.1109\/ICRA.2017.7989633","journal-title":"Proc IEEE Int Conf Robot Autom"},{"issue":"30","key":"533_CR15","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.aaw0955","volume":"4","author":"D Rakita","year":"2019","unstructured":"Rakita D, Mutlu B, Gleicher M, Hiatt LM (2019) Shared control-based bimanual robot manipulation. Sci Robot 4(30):eaaw0955. https:\/\/doi.org\/10.1126\/scirobotics.aaw0955","journal-title":"Sci Robot"},{"issue":"1","key":"533_CR16","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s10846-019-01125-8","volume":"99","author":"S Li","year":"2020","unstructured":"Li S, Bowman M, Nobarani H, Zhang X (2020) Inference of manipulation intent in teleoperation for robotic assistance. J Intell Robot Syst 99(1):29\u201343. https:\/\/doi.org\/10.1007\/s10846-019-01125-8","journal-title":"J Intell Robot Syst"},{"key":"533_CR17","doi-asserted-by":"publisher","DOI":"10.5555\/1620270.1620297","author":"BD Ziebart","year":"2008","unstructured":"Ziebart BD, Maas AL, Bagnell JA, Dey AK (2008) Maximum entropy inverse reinforcement learning. Proc AAAI Conf Artif Intell. https:\/\/doi.org\/10.5555\/1620270.1620297","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"6","key":"533_CR18","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1163\/156855300741960","volume":"14","author":"T Sim\u00e9on","year":"2000","unstructured":"Sim\u00e9on T, Laumond JP, Nissoux C (2000) Visibility-based probabilistic roadmaps for motion planning. Adv Robot 14(6):477\u2013493. https:\/\/doi.org\/10.1163\/156855300741960","journal-title":"Adv Robot"},{"issue":"7\u20138","key":"533_CR19","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1177\/0278364904045471","volume":"23","author":"T Sim\u00e9on","year":"2004","unstructured":"Sim\u00e9on T, Laumond JP, Cort\u00e9s J, Sahbani A (2004) Manipulation planning with probabilistic roadmaps. Int J Robot Res 23(7\u20138):729\u2013746. https:\/\/doi.org\/10.1177\/0278364904045471","journal-title":"Int J Robot Res"},{"key":"533_CR20","unstructured":"LaValle SM (1998) Rapidly-exploring random trees: a new tool for path planning. TR 98\u201311, Computer Science Department, Iowa State University. http:\/\/msl.cs.illinois.edu\/~lavalle\/papers\/Lav98c.pdf. Accessed 22 Sept 2021"},{"key":"533_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2000.844730","author":"JJ Kuffner","year":"2000","unstructured":"Kuffner JJ, LaValle SM (2000) RRT-connect: an efficient approach to single-query path planning. Proc IEEE Int Conf Robot Autom. https:\/\/doi.org\/10.1109\/ROBOT.2000.844730","journal-title":"Proc IEEE Int Conf Robot Autom"},{"key":"533_CR22","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2011.5980280","author":"M Kalakrishnan","year":"2011","unstructured":"Kalakrishnan M, Chitta S, Theodorou E, Pastor P, Schaal S (2011) STOMP: stochastic trajectory optimization for motion planning. Proc IEEE Int Conf Robot Autom. https:\/\/doi.org\/10.1109\/ICRA.2011.5980280","journal-title":"Proc IEEE Int Conf Robot Autom"},{"issue":"9\u201310","key":"533_CR23","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1177\/0278364913488805","volume":"32","author":"M Zucker","year":"2013","unstructured":"Zucker M, Ratliff N, Dragan AD, Pivtoraiko M, Klingensmith M, Dellin CM, Bagnell JA, Srinivasa SS (2013) CHOMP: covariant hamiltonian optimization for motion planning. Int J Robot Res 32(9\u201310):1164\u20131193. https:\/\/doi.org\/10.1177\/0278364913488805","journal-title":"Int J Robot Res"},{"issue":"2","key":"533_CR24","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1162\/NECO_a_00393","volume":"25","author":"AJ Ijspeert","year":"2013","unstructured":"Ijspeert AJ, Nakanishi J, Hoffmann H, Pastor P, Schaal S (2013) Dynamical movement primitives: learning attractor models for motor behaviors. Neural Comput 25(2):328\u2013373. https:\/\/doi.org\/10.1162\/NECO_a_00393","journal-title":"Neural Comput"},{"key":"533_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2009.5152385","author":"P Pastor","year":"2009","unstructured":"Pastor P, Hoffmann H, Asfour T, Schaal S (2009) Learning and generalization of motor skills by learning from demonstration. Proc IEEE Int Conf Robot Autom. https:\/\/doi.org\/10.1109\/ROBOT.2009.5152385","journal-title":"Proc IEEE Int Conf Robot Autom"},{"issue":"1","key":"533_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11370-015-0187-9","volume":"9","author":"S Calinon","year":"2016","unstructured":"Calinon S (2016) A tutorial on task-parameterized movement learning and retrieval. Intel Serv Robot 9(1):1\u201329. https:\/\/doi.org\/10.1007\/s11370-015-0187-9","journal-title":"Intel Serv Robot"},{"key":"533_CR27","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1007\/s10514-018-9745-2","volume":"43","author":"I Havoutis","year":"2019","unstructured":"Havoutis I, Calinon S (2019) Learning from demonstration for semi-autonomous teleoperation. Auton Robot 43:713\u2013726. https:\/\/doi.org\/10.1007\/s10514-018-9745-2","journal-title":"Auton Robot"},{"key":"533_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8461249","author":"T Zhang","year":"2017","unstructured":"Zhang T, Mccarthy Z, Jow O, Lee D, Chen X, Goldberg K, Abbeel P (2017) Deep imitation learning for complex manipulation tasks from virtual reality teleoperation. Proc IEEE Int Conf Robot Autom. https:\/\/doi.org\/10.1109\/ICRA.2018.8461249","journal-title":"Proc IEEE Int Conf Robot Autom"},{"issue":"6","key":"533_CR29","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1109\/TRO.2008.2006703","volume":"24","author":"M Hersch","year":"2008","unstructured":"Hersch M, Guenter F, Calinon S, Billard A (2008) Dynamical system modulation for robot learning via kinesthetic demonstrations. IEEE Trans Robot 24(6):1463\u20131467. https:\/\/doi.org\/10.1109\/TRO.2008.2006703","journal-title":"IEEE Trans Robot"},{"issue":"4","key":"533_CR30","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/s10514-012-9287-y","volume":"32","author":"SM Khansari-Zadeh","year":"2012","unstructured":"Khansari-Zadeh SM, Billard A (2012) A dynamical system approach to realtime obstacle avoidance. Auton Robot 32(4):433\u2013454. https:\/\/doi.org\/10.1007\/s10514-012-9287-y","journal-title":"Auton Robot"},{"issue":"5","key":"533_CR31","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1109\/TRO.2011.2159412","volume":"27","author":"SM Khansari-Zadeh","year":"2011","unstructured":"Khansari-Zadeh SM, Billard A (2011) Learning stable nonlinear dynamical systems with gaussian mixture models. IEEE Trans Robot 27(5):943\u2013957. https:\/\/doi.org\/10.1109\/TRO.2011.2159412","journal-title":"IEEE Trans Robot"},{"issue":"11","key":"533_CR32","doi-asserted-by":"publisher","first-page":"3432","DOI":"10.1016\/j.sigpro.2006.03.007","volume":"86","author":"L Li","year":"2006","unstructured":"Li L, Ji H, Gao X (2006) Maximum entropy fuzzy clustering with application to real-time target tracking. Signal Process 86(11):3432\u20133447. https:\/\/doi.org\/10.1016\/j.sigpro.2006.03.007","journal-title":"Signal Process"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-021-00533-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-021-00533-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-021-00533-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T10:25:08Z","timestamp":1659522308000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-021-00533-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,8]]},"references-count":32,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["533"],"URL":"https:\/\/doi.org\/10.1007\/s40747-021-00533-4","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,8]]},"assertion":[{"value":"15 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}