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International Conference on Neural Information Processing

ICONIP 2020: Neural Information Processing pp 695–703Cite as

Adaptive Neural Control for Efficient Rhythmic Movement Generation and Online Frequency Adaptation of a Compliant Robot Arm

Part of the Communications in Computer and Information Science book series (CCIS,volume 1333)

Abstract

In this paper, we propose an adaptive and simple neural control approach for a robot arm with soft/compliant materials, called GummiArm. The control approach is based on a minimal two-neuron oscillator network (acting as a central pattern generator) and an error-based dual integral learning (DIL) method for efficient rhythmic movement generation and frequency adaptation, respectively. By using this approach, we can precisely generate rhythmic motion for GummiArm and allow it to quickly adapt its motion to handle physical and environmental changes as well as interacting with a human safely. Experimental results for GummiArm in different scenarios (e.g., dealing with different joint stiffnesses, working against elastic loads, and interacting with a human) are provided to illustrate the effectiveness of the proposed adaptive neural control approach.

Keywords

  • Adaptive robot behavior
  • Soft robot
  • Human-machine interaction
  • Artificial intelligence

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Acknowledgements

We thank Martin Stoelen to provide the technical details of GummiArm. This research was supported by Center for BioRobotics at the University of Southern Denmark and VISTEC-research funding on Bio-inspired Robotics.

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Authors and Affiliations

  1. Faculty of Engineering Technology, Technologiecampus Ghent, Ku Leuven, Leuven, Belgium

    Florentijn Degroote & Emilia Motoasca

  2. ML6, Esplanade Oscar Van De Voorde 1, 9000, Ghent, Belgium

    Florentijn Degroote

  3. Embodied AI and Neurorobotics Lab, SDU Biorobotics, The Mærsk Mc-Kinney Møller Instituttet, University of Southern Denmark, Odense, Denmark

    Mathias Thor, Jevgeni Ignasov, Jørgen Christian Larsen & Poramate Manoonpong

  4. BRAIN Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand

    Poramate Manoonpong

Authors
  1. Florentijn Degroote
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  2. Mathias Thor
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  3. Jevgeni Ignasov
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  4. Jørgen Christian Larsen
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  5. Emilia Motoasca
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  6. Poramate Manoonpong
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Corresponding author

Correspondence to Poramate Manoonpong .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Dr. Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    Prof. James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Dr. Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Prof. Irwin King

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Degroote, F., Thor, M., Ignasov, J., Larsen, J.C., Motoasca, E., Manoonpong, P. (2020). Adaptive Neural Control for Efficient Rhythmic Movement Generation and Online Frequency Adaptation of a Compliant Robot Arm. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_79

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  • DOI: https://doi.org/10.1007/978-3-030-63823-8_79

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63822-1

  • Online ISBN: 978-3-030-63823-8

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