{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:31:10Z","timestamp":1753893070296,"version":"3.41.2"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Robot. AI"],"abstract":"<jats:p>Identifying an accurate dynamics model remains challenging for humanoid robots. The difficulty is mainly due to the following two points. First, a good initial model is required to evaluate the feasibility of motions for data acquisition. Second, a highly nonlinear optimization problem needs to be solved to design movements to acquire the identification data. To cope with the first point, in this paper, we propose a curriculum of identification to gradually learn an accurate dynamics model from an unreliable initial model. For the second point, we propose using a large-scale human motion database to efficiently design the humanoid movements for the parameter identification. The contribution of our study is developing a humanoid identification method that does not require the good initial model and does not need to solve the highly nonlinear optimization problem. We showed that our curriculum-based approach was able to more efficiently identify humanoid model parameters than a method that just randomly picked reference motions for identification. We evaluated our proposed method in a simulation experiment and demonstrated that our curriculum was led to obtain a wide variety of motion data for efficient parameter estimation. Consequently, our approach successfully identified an accurate model of an 18-DoF, simulated upper-body humanoid robot.<\/jats:p>","DOI":"10.3389\/frobt.2023.1282299","type":"journal-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T08:26:16Z","timestamp":1701332776000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Curriculum-based humanoid robot identification using large-scale human motion database"],"prefix":"10.3389","volume":"10","author":[{"given":"Sunhwi","family":"Kang","sequence":"first","affiliation":[]},{"given":"Koji","family":"Ishihara","sequence":"additional","affiliation":[]},{"given":"Norikazu","family":"Sugimoto","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Morimoto","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"310","DOI":"10.3311\/ECCOMASMBD2021-233","article-title":"Motion capture based model identification of the humanoid robot REEM-C using static poses","volume-title":"ECCOMAS thematic conference on multibody dynamics","author":"Aller","year":"2021"},{"key":"B2","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1177\/027836498600500306","article-title":"Estimation of inertial parameters of manipulator loads and links","volume":"5","author":"Atkeson","year":"1986","journal-title":"Int. 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