Development of a Basic Educational Kit for Robot Development Using Deep Neural Networks

Momomi Kanamura, Yuki Suga, Tetsuya Ogata

研究成果: Conference contribution

抄録

In this paper, we propose a basic educational kit for robotic system development using deep neural networks (DNNs). To develop systems robust to changes in dynamic environments, much research is focusing on learning-based recognition and robotic manipulation systems. However, existing robotic techniques and DNNs are not systematically integrated and packages for beginners have not yet been developed. Therefore, we developed a robotic system using DNNs and a system manual to serve as a basic educational kit that can be easily used by anyone. Our goal was to educate beginners in both robotics and machine learning, especially when DNNs are used. Initially, we set the following requirements for the kit: (1) easy to understand; (2) experience-based; and (3) applicable in many areas. We analyzed the research and development of DNNs and divided the process into Date Collecting (DC), Machine Learning (ML), and Task Execution (TE) steps. Finally, we applied our hierarchical system architecture to a physical robotic grasping system. We implemented the DC and TE steps in the physical robotic system by using robotics middleware and in the ML step, we prepared a sample script.

本文言語English
ホスト出版物のタイトルProceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1360-1363
ページ数4
ISBN(電子版)9781728166674
DOI
出版ステータスPublished - 2020 1
イベント2020 IEEE/SICE International Symposium on System Integration, SII 2020 - Honolulu, United States
継続期間: 2020 1 122020 1 15

出版物シリーズ

名前Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020

Conference

Conference2020 IEEE/SICE International Symposium on System Integration, SII 2020
CountryUnited States
CityHonolulu
Period20/1/1220/1/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Biomedical Engineering
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Instrumentation

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