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The controller integrates the biomechanical characteristics of human limbs and dynamically adjusts the robotic impedance parameters\u2014specifically damping, stiffness, and equilibrium trajectory\u2014based on real\u2010time estimations of the user's intent and direction of motion. The primary goal is to minimize the energy expenditure of the coupled human\u2013robot system while maintaining system passivity. To address uncertainties in human behavior and noisy observations, the controller employs Bayesian optimization combined with a Gaussian process. To validate the proposed approach, human experiments are conducted using a standard robotic arm manipulator. The results demonstrate that the controller eliminates the need for manual parameter tuning, a process that is typically time\u2010consuming. A comparative analysis against two variable impedance controllers without user\u2010adaptive parameter adjustments reveal significant benefits, with the controller improving combined performance metrics\u2014such as accuracy, speed, user effort, and smoothness\u2014by over 13%. Notably, all participants in the study preferred the optimized controller over the alternatives. These findings highlight the effectiveness of the biomechanics\u2010based, user\u2010adaptive variable impedance control approach and its potential to enhance physical human\u2013robot interaction in various applications that involve repetitive or continuous motion.<\/jats:p>","DOI":"10.1002\/aisy.202400333","type":"journal-article","created":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T08:01:29Z","timestamp":1725436889000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Biomechanics\u2010Based User\u2010Adaptive Variable Impedance Control for Enhanced Physical Human\u2013Robot Interaction Using Bayesian Optimization"],"prefix":"10.1002","volume":"7","author":[{"given":"Fatemeh","family":"Zahedi","sequence":"first","affiliation":[{"name":"School for Engineering of Matter, Transport and Energy Arizona State University  Tempe AZ 85287 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3853-510X","authenticated-orcid":false,"given":"Hyunglae","family":"Lee","sequence":"additional","affiliation":[{"name":"School for Engineering of Matter, Transport and Energy Arizona State University  Tempe AZ 85287 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechatronics.2018.02.009"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2773127"},{"key":"e_1_2_12_4_1","first-page":"283","volume-title":"Handbook of Clinical Neurology","author":"Krebs H.","year":"2013"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICVR.2007.4362133"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2008.927971"},{"key":"e_1_2_12_7_1","first-page":"375","volume-title":"Robotics and Automation Handbook","author":"Hogan N.","year":"2018"},{"key":"e_1_2_12_8_1","doi-asserted-by":"crossref","unstructured":"H.Lee N.Hogan in2016 IEEE International Conf. on Robotics and Automation (ICRA) IEEE Piscataway NJ2016 pp.3069\u20133074.","DOI":"10.1109\/ICRA.2016.7487472"},{"key":"e_1_2_12_9_1","first-page":"465","volume-title":"Advanced Robotics","author":"Colgate E.","year":"1989"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2008.915438"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364919840415"},{"key":"e_1_2_12_12_1","doi-asserted-by":"crossref","unstructured":"R.Ikeura H.Inooka inProc. of 1995 IEEE Inter. 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