{"title":"用Simulink控制工业机器人","authors":"Bucur Cosmin, Andrei Alexandru, Tasu Sorin","doi":"10.1109/ECAI58194.2023.10193974","DOIUrl":null,"url":null,"abstract":"Implementing machine learning algorithms like reinforcement learning in robotics is a continuously changing topic due to continuous tool changes and updates to keep track of new algorithms and tools. This paper presents a new toolchain to implement such algorithms with open-source packages like ROS2 for industrial robots. We developed new tools and procedures to enable the implementation of reinforcement learning algorithms through simulation or controlling real robots with Matlab.","PeriodicalId":391483,"journal":{"name":"2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Controlling industrial robots with Simulink\",\"authors\":\"Bucur Cosmin, Andrei Alexandru, Tasu Sorin\",\"doi\":\"10.1109/ECAI58194.2023.10193974\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Implementing machine learning algorithms like reinforcement learning in robotics is a continuously changing topic due to continuous tool changes and updates to keep track of new algorithms and tools. This paper presents a new toolchain to implement such algorithms with open-source packages like ROS2 for industrial robots. We developed new tools and procedures to enable the implementation of reinforcement learning algorithms through simulation or controlling real robots with Matlab.\",\"PeriodicalId\":391483,\"journal\":{\"name\":\"2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECAI58194.2023.10193974\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECAI58194.2023.10193974","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Implementing machine learning algorithms like reinforcement learning in robotics is a continuously changing topic due to continuous tool changes and updates to keep track of new algorithms and tools. This paper presents a new toolchain to implement such algorithms with open-source packages like ROS2 for industrial robots. We developed new tools and procedures to enable the implementation of reinforcement learning algorithms through simulation or controlling real robots with Matlab.