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
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
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