Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot

被引：2

作者：

Sakaino, Sho ^{[1
,2
]}

机构：

[1] Univ Tsukuba, Dept Intelligent Interact Technol, Tsukuba, Ibaraki, Japan

[2] PRESTO, JST, Tsukuba, Ibaraki, Japan

来源：

PROCEEDINGS OF 2021 IEEE 30TH INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE) | 2021年

基金：

日本科学技术振兴机构;

关键词：

Imitation learning; learning from demonstration; bilateral control; manipulation; machine learning; deep learning; MANIPULATION; TASK;

D O I：

10.1109/ISIE45552.2021.9576326

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Machine learning is now playing important role in robotic object manipulation. In addition, force control is necessary for manipulating various objects to achieve robustness against perturbations of configurations and stiffness. The author's group revealed that fast and dynamic object manipulation with force control can be obtained by bilateral control-based imitation learning. However, the method is applicable only in robots that can control torque, while it is not applicable in robots that can only follow position or velocity commands like many commercially available robots. Then, in this research, a way to implement bilateral control-based imitation learning to velocity-controlled robots is proposed. The validity of the proposed method is experimentally verified by a mopping task.

引用

页数：6

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