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JRM_25(1)_88.pdf | 3.13 MB | Adobe PDF | 見る/開く |
タイトル: | Abstraction Multimodal Low-Dimensional Representation from High-Dimensional Posture Information and Visual Images |
著者: | Hirose, Tatsuya Taniguchi, Tadahiro |
著者名の別形: | 廣瀬, 達也 |
キーワード: | kernel canonical correlation analysis imitation learning body schema |
発行日: | 2013 |
出版者: | Fuji Technology Press |
誌名: | Journal of Robotics and Mechatronics |
巻: | 25 |
号: | 1 |
開始ページ: | 80 |
終了ページ: | 88 |
抄録: | Imitative learning is an effective method for robots to obtain a novel movement from a person demonstrating many kinds of movement. Many problems need to be solved, however, before a robot can achieve imitative learning. One problem is how to convert visual information on the demonstrator’s motion to kinematic posture information for the learner. This is referred to as a correspondence problem and we have focused on this problem in this study. To solve it, we focus on the formation of a low-dimensional representation that integrates sensory information from two different modalities. We propose a computation method for constructing the low-dimensional representation combining posture information and visual images by using Kernel Canonical Correlation Analysis (KCCA). Using this method, a robot becomes able to estimate posture information from visual images in a bottom-up way. Using several experiments we show how effective our proposed method is in estimating kinematic information. |
著作権等: | (C) 2013 Fuji Technology Press Co, . Ltd. This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 |
URI: | http://hdl.handle.net/2433/172346 |
DOI(出版社版): | 10.20965/jrm.2013.p0080 |
関連リンク: | http://www.fujipress.jp/finder/xslt.php?mode=present&inputfile=ROBOT002500010008.xml |
出現コレクション: | 学術雑誌掲載論文等 |
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