著者

Komizunai Shunsuke Nishizaki Keisuke Wada Kyohei Kijima Takuya Konno Atsushi

出版者

Fuji Technology Press

雑誌

Journal of robotics and mechatronics (ISSN:09153942)

巻号頁・発行日

vol.28, no.6, pp.790-798, 2016-12

著者

Hirose Tatsuya Taniguchi Tadahiro

出版者

Fuji Technology Press

巻号頁・発行日

vol.25, no.1, pp.80-88, 2013

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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.

著者

岡本 一志 Kazushi Okamoto

出版者

Fuji Technology Press

雑誌

Journal of Advanced Computational Intelligence and Intelligent Informatics (ISSN:13430130)

巻号頁・発行日

vol.22, no.6, pp.943-955, 2018-10-20

著者

岡本 一志 Kazushi Okamoto

出版者

Fuji Technology Press

雑誌

Journal of Advanced Computational Intelligence and Intelligent Informatics (ISSN:13430130)

巻号頁・発行日

vol.21, no.3, pp.534-542, 2017

著者

Fujinawa Yukio Kouda Ryoichi Noda Yoichi

出版者

Fuji Technology Press

雑誌

Journal of disaster research (ISSN:18812473)

巻号頁・発行日

vol.10, no.2, pp.319-325, 2015-04