A framework for industrial robot training in cloud manufacturing with deep reinforcement learning

被引:0
作者
Liu, Yongkui [1 ]
Yao, Junying [1 ]
Lin, Tingyu [2 ]
Xu, He [1 ]
Shi, Feng [3 ]
Xiao, Yingying [2 ]
Zhang, Lin [4 ]
Wang, Lihui [5 ]
机构
[1] Xidian Univ, Ctr Intelligent Mfg Syst & Robot, Sch Mechanoelect Engn, Xian 710071, Peoples R China
[2] State Key Lab Intelligent Mfg Syst Technol, Beijing 100854, Peoples R China
[3] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China
[4] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
[5] KTH Royal Inst Technol, Dept Prod Engn, S-10044 Stockholm, Sweden
来源
PROCEEDINGS OF THE ASME 2020 15TH INTERNATIONAL MANUFACTURING SCIENCE AND ENGINEERING CONFERENCE (MSEC2020), VOL 2B | 2020年
基金
中国国家自然科学基金;
关键词
Cloud manufacturing; Deep reinforcement learning; Industrial robot training; Transfer learning;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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 ate discussed.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Defensive deception framework against reconnaissance attacks in the cloud with deep reinforcement learning
    Li, Huanruo
    Guo, Yunfei
    Huo, Shumin
    Hu, Hongchao
    Sun, Penghao
    SCIENCE CHINA-INFORMATION SCIENCES, 2022, 65 (07)
  • [22] DeepMRA: An Efficient Microservices Resource Allocation Framework with Deep Reinforcement Learning in the Cloud
    Si, Qi
    Shi, Jilin
    Li, Weiyi
    Lu, Xuesong
    Pu, Peng
    ADVANCED INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, PT II, ICIC 2024, 2024, 14863 : 455 - 466
  • [23] Defensive deception framework against reconnaissance attacks in the cloud with deep reinforcement learning
    Huanruo Li
    Yunfei Guo
    Shumin Huo
    Hongchao Hu
    Penghao Sun
    Science China Information Sciences, 2022, 65
  • [24] Simulation Service System with Cloud Manufacturing for Industrial Robot
    Pan, Junhao
    Zhuo, Yong
    Liao, Shenghui
    Hou, Liang
    PROCEEDINGS OF THE 2017 7TH INTERNATIONAL CONFERENCE ON ADVANCED DESIGN AND MANUFACTURING ENGINEERING (ICADME 2017), 2017, 136 : 136 - 139
  • [25] Logistics-involved task scheduling in cloud manufacturing with offline deep reinforcement learning
    Wang, Xiaohan
    Zhang, Lin
    Liu, Yongkui
    Zhao, Chun
    JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION, 2023, 34
  • [26] Spatiotemporal path tracking via deep reinforcement learning of robot for manufacturing internal logistics
    Fan, Fei
    Xu, Guanglin
    Feng, Na
    Li, Lin
    Jiang, Wei
    Yu, Lianqin
    Xiong, Xiaoshuang
    JOURNAL OF MANUFACTURING SYSTEMS, 2023, 69 : 150 - 169
  • [27] A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
    Liu, Ning
    Li, Zhe
    Xu, Jielong
    Xu, Zhiyuan
    Lin, Sheng
    Qiu, Qinru
    Tang, Jian
    Wang, Yanzhi
    2017 IEEE 37TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2017), 2017, : 372 - 382
  • [28] A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework
    del Real Torres, Alejandro
    Stefan Andreiana, Doru
    Ojeda Roldan, Alvaro
    Hernandez Bustos, Alfonso
    Acevedo Galicia, Luis Enrique
    APPLIED SCIENCES-BASEL, 2022, 12 (23):
  • [29] An optimal defensive deception framework for the container-based cloud with deep reinforcement learning
    Li, Huanruo
    Guo, Yunfei
    Sun, Penghao
    Wang, Yawen
    Huo, Shumin
    IET INFORMATION SECURITY, 2022, 16 (03) : 178 - 192
  • [30] Deep Reinforcement Learning for Humanoid Robot Behaviors
    Muzio, Alexandre F. V.
    Maximo, Marcos R. O. A.
    Yoneyama, Takashi
    JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2022, 105 (01)