{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T12:51:23Z","timestamp":1753879883234,"version":"3.41.2"},"reference-count":29,"publisher":"ASME International","issue":"4","content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2017,12,1]]},"abstract":"<jats:p>Existing techniques for motion imitation often suffer a certain level of latency due to their computational overhead or a large set of correspondence samples to search. To achieve real-time imitation with small latency, we present a framework in this paper to reconstruct motion on humanoids based on sparsely sampled correspondence. The imitation problem is formulated as finding the projection of a point from the configuration space of a human's poses into the configuration space of a humanoid. An optimal projection is defined as the one that minimizes a back-projected deviation among a group of candidates, which can be determined in a very efficient way. Benefited from this formulation, effective projections can be obtained by using sparsely sampled correspondence, whose generation scheme is also introduced in this paper. Our method is evaluated by applying the human's motion captured by an RGB-depth (RGB-D) sensor to a humanoid in real time. Continuous motion can be realized and used in the example application of teleoperation.<\/jats:p>","DOI":"10.1115\/1.4036923","type":"journal-article","created":{"date-parts":[[2017,5,30]],"date-time":"2017-05-30T16:31:09Z","timestamp":1496161869000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":3,"title":["Motion Imitation Based on Sparsely Sampled Correspondence"],"prefix":"10.1115","volume":"17","author":[{"given":"Shuo","family":"Jin","sequence":"first","affiliation":[{"name":"Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China e-mail:"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengkai","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China e-mail:"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing 100080, China e-mail:"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charlie C. L.","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Design Engineering and TU Delft Robotics Institute, Delft University of Technology, Delft 2628, The Netherlands e-mail:"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33","published-online":{"date-parts":[[2017,6,15]]},"reference":[{"key":"2019100601052689000_bib1","doi-asserted-by":"crossref","unstructured":"Suleiman, W., Yoshida, E., Kanehiro, F., Laumond, J.-P., and Monin, A., 2008, \u201cOn Human Motion Imitation by Humanoid Robot,\u201d IEEE International Conference on Robotics and Automation (ICRA), Pasadena, CA, May 19\u201323, pp. 2697\u20132704.10.1109\/ROBOT.2008.4543619","DOI":"10.1109\/ROBOT.2008.4543619"},{"key":"2019100601052689000_bib2","first-page":"2084","article-title":"Learning to Walk Through Imitation","volume":"7","journal-title":"IJCAI"},{"issue":"8","key":"2019100601052689000_bib3","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1177\/0278364907079430","article-title":"Learning From Observation Paradigm: Leg Task Models for Enabling a Biped Humanoid Robot to Imitate Human Dances","volume":"26","year":"2007","journal-title":"Int. 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