{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T19:46:53Z","timestamp":1793476013629,"version":"4.5.0"},"reference-count":36,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T00:00:00Z","timestamp":1757462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bureau of Science and Technology of Zhongshan City (Social Public Welfare and Basic Research Project of Zhongshan City)","award":["2023B2011"],"award-info":[{"award-number":["2023B2011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To enhance robotic adaptability in dynamic environments, this study proposes a multimodal framework for skill transfer. The framework integrates vision-based kinesthetic teaching with surface electromyography (sEMG) signals to estimate human impedance. We establish a Cartesian-space model of upper-limb stiffness, linearly mapping sEMG signals to end-point stiffness. For flexible task execution, dynamic movement primitives (DMPs) generalize learned skills across varying scenarios. An adaptive admittance controller, incorporating sEMG-modulated stiffness, is developed and validated on a UR5 robot. Experiments involving elastic-band stretching demonstrate that the system successfully transfers human impedance characteristics to the robot, enhancing stability, environmental adaptability, and safety during physical interaction.<\/jats:p>","DOI":"10.3390\/s25185630","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T08:43:54Z","timestamp":1757493834000},"page":"5630","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Human\u2013Robot Variable-Impedance Skill Transfer Learning Based on Dynamic Movement Primitives and a Vision System"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2947-9210","authenticated-orcid":false,"given":"Honghui","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528400, China"},{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4743-7157","authenticated-orcid":false,"given":"Fang","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5612-0271","authenticated-orcid":false,"given":"Miaozhe","family":"Cai","sequence":"additional","affiliation":[{"name":"Faculty of Applied Sciences, Macao Polytechnic University, Macau 999078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102686","DOI":"10.1016\/j.rcim.2023.102686","article-title":"Human-robot shared assembly taxonomy: A step toward seamless human-robot knowledge transfer","volume":"86","author":"Lee","year":"2024","journal-title":"Robot. 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