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JRM Vol.25 No.1 pp. 80-88
doi: 10.20965/jrm.2013.p0080
(2013)

Paper:

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Abstraction Multimodal Low-Dimensional Representation from High-Dimensional Posture Information and Visual Images

Tatsuya Hirose* and Tadahiro Taniguchi**

*Kyoto University, Oiwake-cho, Kitashirakawa, Sakyo-ku, Kyoto, Japan

**Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga, Japan

Received:
February 16, 2012
Accepted:
May 7, 2012
Published:
February 20, 2013
Creative Commons License
Keywords:
kernel canonical correlation analysis, imitation learning, body schema
Abstract
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.
Cite this article as:
T. Hirose and T. Taniguchi, “Abstraction Multimodal Low-Dimensional Representation from High-Dimensional Posture Information and Visual Images,” J. Robot. Mechatron., Vol.25 No.1, pp. 80-88, 2013.
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