Application of Adam to Iterative Learning for an In-Hand Manipulation Task
Application of Adam to Iterative Learning for an In-Hand Manipulation Task
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DOI：
10.1007/978-3-319-78963-7_35
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
T. Yamawaki;M. Yashima
中科院分区：
文献类型：
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作者：
T. Yamawaki;M. Yashima
本文提出了一种迭代学习方案，利用深度学习的学习增益自适应概念的手操作系统。所提出的方法的优点是：（1）不需要为学习过程生成理论分析模型;（2）所提出的方法对测量误差、摩擦力和接触状态等不确定性具有鲁棒性。最后通过实验验证了该方法的有效性。
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.