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Planning-Space Shift Motion Generation: Variable-space Motion Planning Toward Flexible Extension of Body Schema

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Abstract

To improve the flexibility of robotic learning, it is important to realize an ability to generate a hierarchical structure. This paper proposes a learning framework which can dynamically change the planning space depending on the structure of tasks. Synchronous motion information is utilized to generate ’modes’ and hierarchical structure of the controller is constructed based on the modes. This enables efficient planning and control in low-dimensional planning space, though the dimension of the total state space is in general very high. Three types of object manipulation tasks are tested as applications, where an object is found and used as a tool (or as a part of the body) to extend the ability of the robot. The proposed framework is expected to be a basic learning model to account for body schema acquisition including tool affordances.

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Authors and Affiliations

  1. Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, Japan

    Yuichi Kobayashi

  2. RIKEN-TRI Collaboration Center for Human-Interactive Robot Research (RTC), Nagoya University, Shimoshidami, Anagahora, Nagoya, Japan

    Shigeyuki Hosoe

Authors
  1. Yuichi Kobayashi
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  2. Shigeyuki Hosoe
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Correspondence to Yuichi Kobayashi.

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Kobayashi, Y., Hosoe, S. Planning-Space Shift Motion Generation: Variable-space Motion Planning Toward Flexible Extension of Body Schema. J Intell Robot Syst 62, 467–500 (2011). https://doi.org/10.1007/s10846-010-9465-0

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  • DOI: https://doi.org/10.1007/s10846-010-9465-0

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