{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T21:50:26Z","timestamp":1779141026480,"version":"3.51.4"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T00:00:00Z","timestamp":1671494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Research on robotic exoskeletons both in the military and medical fields has rapidly expanded over the previous decade. As a human\u2013robot interaction system, it is a challenge to develop an assistive strategy that makes the exoskeleton supply efficient and natural assistance following the user's intention. This paper proposed a novel interaction learning control strategy for the lower extremity exoskeleton. A powerful representative tool probabilistic movement primitives (ProMPs) is adopted to model the motion and generate the desired trajectory in real-time. To adjust the trajectory by the user's real-time intention, a compensation term based on human\u2013robot interaction force is designed and merged into the ProMPs model. Then, compliant impedance control is adopted as a low-level control where the desired trajectory is put into. Moreover, the model will be dynamically adapted online by penalizing both the interaction force and trajectory mismatch, with all the parameters that can be further learned by learning algorithm PI<jats:sup>BB<\/jats:sup>. The experimental results verified the effectiveness of the proposed control framework.<\/jats:p>","DOI":"10.3389\/fnbot.2022.1086578","type":"journal-article","created":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T05:03:14Z","timestamp":1671512594000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Interaction learning control with movement primitives for lower limb exoskeleton"],"prefix":"10.3389","volume":"16","author":[{"given":"Jiaqi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongmei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongzhuo","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2022,12,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2440","DOI":"10.1017\/S0263574715000107","article-title":"Multilevel control of zero-moment point-based humanoid biped robots: a review","volume":"34","author":"Al-Shuka","year":"2016","journal-title":"Robotica"},{"key":"B2","doi-asserted-by":"publisher","first-page":"3076","DOI":"10.1073\/pnas.0500199102","article-title":"Shared and specific muscle synergies in natural motor behaviors","volume":"102","author":"d'Avella","year":"2005","journal-title":"Proc. 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