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Manifold Learning Approach Toward Constructing State Representation for Robot Motion Generation

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Book cover Transactions on Computational Collective Intelligence XXIV

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 9770))

Abstract

This paper presents a bottom-up approach to building internal representation of an autonomous robot. The robot creates its state space for planning and generating actions adaptively based on collected information of image features without pre-programmed physical model of the world. For this purpose, image-feature-based state space construction method is proposed using manifold learning approach. The visual feature is extracted from sample images by SIFT (scale invariant feature transform). SOM (Self Organizing Map) is introduced to find appropriate labels of image features throughout images with different configurations of robot. The vector of visual feature points mapped to low dimensional space express relation between the robot and its environment with LLE (locally linear embedding). The proposed method was evaluated by experiment with a humanoid robot collision classification and motion generation in an obstacle avoidance task.

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Acknowledgment

This work was partly supported by Kayamori Foundation of Informational Science Advancement.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu, Shizuoka, Japan

    Yuichi Kobayashi & Ryosuke Matsui

Authors
  1. Yuichi Kobayashi
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  2. Ryosuke Matsui
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Corresponding author

Correspondence to Yuichi Kobayashi .

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Editors and Affiliations

  1. Faculty of Computer Science and Manageme, Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Hawthorn, Australia

    Ryszard Kowalczyk

  3. Escola Superior de Tecnologiy de Setébual, Setúbal, Portugal

    Joaquim Filipe

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Kobayashi, Y., Matsui, R. (2016). Manifold Learning Approach Toward Constructing State Representation for Robot Motion Generation. In: Nguyen, N., Kowalczyk, R., Filipe, J. (eds) Transactions on Computational Collective Intelligence XXIV. Lecture Notes in Computer Science(), vol 9770. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53525-7_6

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  • DOI: https://doi.org/10.1007/978-3-662-53525-7_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-53524-0

  • Online ISBN: 978-3-662-53525-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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