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Action Feasibility Learning with Cell-Based Multi-Object Representation for Task and Motion Planning

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Intelligent Autonomous Systems 16 (IAS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 412))

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Abstract

This paper proposes a description method and a deep neural network classifier for action feasibility learning for robotic tasks. The proposed method is able to deal with multiple three-dimensional obstacles and continuous action parameters. These advantages significantly enhance the effectiveness of the method in an unplanned environment. Thus, we expect our method can significantly increase the usability of task and motion planning in daily-life applications.

This research was supported by Korea Advanced Research Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (2020M3H8A1114905).

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Author information

Authors and Affiliations

  1. POSTECH, Pohang, 37673, Korea

    Junsu Kang, Wan Kyun Chung & Keehoon Kim

Authors
  1. Junsu Kang
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  2. Wan Kyun Chung
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  3. Keehoon Kim
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Corresponding author

Correspondence to Keehoon Kim .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

    Marcelo H. Ang Jr

  2. Department of Precision Engineering, University of Tokyo, Tokyo, Japan

    Hajime Asama

  3. Singapore Institute of Manufacturing Technology, Singapore, Singapore

    Wei Lin

  4. Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore

    Shaohui Foong

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Kang, J., Chung, W.K., Kim, K. (2022). Action Feasibility Learning with Cell-Based Multi-Object Representation for Task and Motion Planning. In: Ang Jr, M.H., Asama, H., Lin, W., Foong, S. (eds) Intelligent Autonomous Systems 16. IAS 2021. Lecture Notes in Networks and Systems, vol 412. Springer, Cham. https://doi.org/10.1007/978-3-030-95892-3_36

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