Abstract
This paper proposes an iterative learning scheme for in-hand manipulation systems by utilizing the learning gain adaptation concept of deep learning. The advantages of the proposed method are that (1) there is no need to generate theoretical analytical models for the learning process and (2) the proposed method is robust against uncertainties such as measurement errors, friction force, and contact state. Finally, the validity of the proposed method is verified through experiments.
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Acknowledgments
This work was supported by JSPS KAKENHI Grant Number JP17K12765.
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© 2019 CISM International Centre for Mechanical Sciences
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Yamawaki, T., Yashima, M. (2019). Application of Adam to Iterative Learning for an In-Hand Manipulation Task. In: Arakelian, V., Wenger, P. (eds) ROMANSY 22 – Robot Design, Dynamics and Control. CISM International Centre for Mechanical Sciences, vol 584. Springer, Cham. https://doi.org/10.1007/978-3-319-78963-7_35
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DOI: https://doi.org/10.1007/978-3-319-78963-7_35
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Online ISBN: 978-3-319-78963-7
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