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A framework for industrial robot training in cloud manufacturing with deep reinforcement learning
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2020 (English)In: ASME 2020 15th International Manufacturing Science and Engineering Conference, MSEC 2020, American Society of Mechanical Engineers , 2020Conference paper, Published paper (Refereed)
Abstract [en]

Cloud manufacturing is a service-oriented networked manufacturing model that embraces the concept of 'Everything-as-a-Service'. In cloud manufacturing, distributed manufacturing resources encompassed in the product lifecycle are transformed into manufacturing services. Industrial robots are an important category of manufacturing resources in cloud manufacturing. During the past years, robots have been demonstrated to be able to learn various dexterous manipulation skills through training with deep reinforcement learning (DRL). In cloud manufacturing, there are many complex industrial application scenarios that require dexterous robots. Hence, robot training, which enables robots to learn various manipulation skills, becomes an important requirement for cloud manufacturing in the future, leading to the concept of 'Robot Training-as-a-Service'. This paper focuses on industrial robot training in the context of cloud manufacturing. First, related work on cloud manufacturing, DRL, DRL-based robot training, and cloud-edge collaboration is briefly reviewed and analyzed. Then, a framework for industrial robot training in cloud manufacturing with DRL is proposed, and a simplified case study is presented to demonstrate the basic principle of the framework. Finally, possible future research issues are discussed.

Place, publisher, year, edition, pages
American Society of Mechanical Engineers , 2020.
Keywords [en]
Cloud manufacturing, Deep reinforcement learning, Industrial robot training, Transfer learning, Computer aided manufacturing, Deep learning, Educational robots, Industrial robots, Life cycle, Manufacture, Reinforcement learning, Robot learning, Basic principles, Dexterous manipulation, Distributed manufacturing, Manufacturing resource, Manufacturing service, Networked-manufacturing, Product-life-cycle, Engineering education
National Category
Robotics and automation
Identifiers
URN: urn:nbn:se:kth:diva-302888DOI: 10.1115/MSEC2020-8355ISI: 000850907300012Scopus ID: 2-s2.0-85101429070OAI: oai:DiVA.org:kth-302888DiVA, id: diva2:1599867
Conference
ASME 2020 15th International Manufacturing Science and Engineering Conference, MSEC 2020, Virtual/Online, 3 September 2020
Note

QC 20230921

Available from: 2021-10-02 Created: 2021-10-02 Last updated: 2025-02-09Bibliographically approved

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Wang, Lihui

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf