Human-Machine Interface for Remote Training of Robot Tasks

被引：0

作者：

Spranger, Jordi ^{[1
]}

Buzatoiu, Roxana ^{[1
]}

Polydoros, Athanasios ^{[2
]}

Nalpantidis, Lazaros ^{[1
]}

Boukas, Evangelos ^{[1
]}

机构：

[1] Aalborg Univ, Mat & Prod Dept, Robot Vis & Machine Intelligence Lab, Copenhagen, Denmark

[2] Univ Innsbruck, Intelligent & Interact Syst Lab, Innsbruck, Austria

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON IMAGING SYSTEMS AND TECHNIQUES (IST) | 2018年

关键词：

Human-Machine Interface; Remote Robots; Robot Tasks; Hand Detection; Remote Training; Dynamic Motion Primitives;

D O I：

暂无

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

Regardless of their industrial or research application, the streamlining of robot operations is limited by the proximity of experienced users to the actual hardware. Be it massive open online robotics courses, crowd-sourcing of robot task training, or remote research on massive robot farms for machine learning, the need to create an apt remote Human-Machine Interface is quite prevalent. The paper at hand proposes a novel solution to the programming/training of remote robots employing an intuitive and accurate user-interface which offers all the benefits of working with real robots without imposing delays and inefficiency. The system includes: a vision-based 3D hand detection and gesture recognition subsystem, a simulated digital twin of a robot as visual feedback, and the "remote" robot learning/executing trajectories using dynamic motion primitives. Our results indicate that the system is a promising solution to the problem of remote training of robot tasks.

引用

页码：243 / 247

页数：5

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