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Published in:
Challenges for Mechanism Design in Robotics Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Application of Adam to Iterative Learning for an In-Hand Manipulation Task

Authors : Tasuku Yamawaki, Masahito Yashima

Published in: ROMANSY 22 – Robot Design, Dynamics and Control

Publisher: Springer International Publishing

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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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previous chapter Robotic Friction Stir Welding Path Planning with Deflection Compensation Using B-Splines
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Metadata
Title
Application of Adam to Iterative Learning for an In-Hand Manipulation Task
Authors
Tasuku Yamawaki
Masahito Yashima
Copyright Year
2019
Book
ROMANSY 22 – Robot Design, Dynamics and Control

Print ISBN: 978-3-319-78962-0

Electronic ISBN: 978-3-319-78963-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-78963-7_35