{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:26:13Z","timestamp":1770747973322,"version":"3.49.0"},"reference-count":66,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000},"content-version":"vor","delay-in-days":365,"URL":"http:\/\/www.sagepub.com\/licence-information-for-chorus"}],"funder":[{"DOI":"10.13039\/100000144","name":"Division of Computer and Network Systems","doi-asserted-by":"publisher","award":["NSF Grant CNS 1329891"],"award-info":[{"award-number":["NSF Grant CNS 1329891"]}],"id":[{"id":"10.13039\/100000144","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2020,8]]},"abstract":"<jats:p> We present a data-driven shared control algorithm that can be used to improve a human operator\u2019s control of complex dynamic machines and achieve tasks that would otherwise be challenging, or impossible, for the user on their own. Our method assumes no a priori knowledge of the system dynamics. Instead, both the dynamics and information about the user\u2019s interaction are learned from observation through the use of a Koopman operator. Using the learned model, we define an optimization problem to compute the autonomous partner\u2019s control policy. Finally, we dynamically allocate control authority to each partner based on a comparison of the user input and the autonomously generated control. We refer to this idea as model-based shared control (MbSC). We evaluate the efficacy of our approach with two human subjects studies consisting of 32 total participants (16 subjects in each study). The first study imposes a linear constraint on the modeling and autonomous policy generation algorithms. The second study explores the more general, nonlinear variant. Overall, we find that MbSC significantly improves task and control metrics when compared with a natural learning, or user only, control paradigm. Our experiments suggest that models learned via the Koopman operator generalize across users, indicating that it is not necessary to collect data from each individual user before providing assistance with MbSC. We also demonstrate the data efficiency of MbSC and, consequently, its usefulness in online learning paradigms. Finally, we find that the nonlinear variant has a greater impact on a user\u2019s ability to successfully achieve a defined task than the linear variant. <\/jats:p>","DOI":"10.1177\/0278364920921935","type":"journal-article","created":{"date-parts":[[2020,6,10]],"date-time":"2020-06-10T09:39:37Z","timestamp":1591781977000},"page":"1178-1195","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":36,"title":["Data-driven Koopman operators for model-based shared control of human\u2013machine systems"],"prefix":"10.1177","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9230-7891","authenticated-orcid":false,"given":"Alexander","family":"Broad","sequence":"first","affiliation":[{"name":"Boston Dynamics, Waltham, MA, USA"},{"name":"Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, USA"},{"name":"Department of Mechanical Engineering, Northwestern University, 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