2012年
Online Object Categorization Using Multimodal Information Autonomously Acquired by a Mobile Robot
ADVANCED ROBOTICS
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- 巻
- 26
- 号
- 17
- 開始ページ
- 1995
- 終了ページ
- 2020
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1080/01691864.2012.728693
- 出版者・発行元
- TAYLOR & FRANCIS LTD
In this paper, we propose a robot that acquires multimodal information, i.e. visual, auditory, and haptic information, fully autonomously using its embodiment. We also propose batch and online algorithms for multimodal categorization based on the acquired multimodal information and partial words given by human users. To obtain multimodal information, the robot detects an object on a flat surface. Then, the robot grasps and shakes it to obtain haptic and auditory information. For obtaining visual information, the robot uses a small hand-held observation table with an XBee wireless controller to control the viewpoints for observing the object. In this paper, for multimodal concept formation, multimodal latent Dirichlet allocation using Gibbs sampling is extended to an online version. This framework makes it possible for the robot to learn object concepts naturally in everyday operation in conjunction with a small amount of linguistic information from human users. The proposed algorithms are implemented on a real robot and tested using real everyday objects to show the validity of the proposed system. (C) 2012 Taylor & Francis and The Robotics Society of Japan
- リンク情報
- ID情報
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- DOI : 10.1080/01691864.2012.728693
- ISSN : 0169-1864
- eISSN : 1568-5535
- Web of Science ID : WOS:000310611500004