{"title":"基于人机交互的机器人自学习方法在家庭环境中的通用技能学习","authors":"Tao Cao, Dayou Li, C. Maple, Renxi Qiu","doi":"10.1109/ROBIO.2013.6739459","DOIUrl":null,"url":null,"abstract":"Unstructured domestic environments present a substantial challenge to effective robotic operation. Domestic environment requires service robots to deal with unexpected environment changes, novel objects, and user manipulations. We present an approach to enable service robots to actively learn high-level skills in an unstructured environment. This involves using a combination of processing environment changes, recording and learning user manipulation data, setting up meaningful hypothesis, proactively performing test actions and interacting with user feedback, and logic reasoning. We demonstrate our Robot Self-Learning (RSL) approach by using ROS (Robotic Operating System) and Care-O-bot (COB) 3. These methods enable service robots to learn generalized high-level skills in a sophisticated and changing environment. The RSL approach allows robots to learn new actions imposed by a human and action condition from perception changes from the environment. We also present logic based reasoning engine to speed up the self learning process.","PeriodicalId":434960,"journal":{"name":"2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Human interaction based Robot Self-Learning approach for generic skill learning in domestic environment\",\"authors\":\"Tao Cao, Dayou Li, C. Maple, Renxi Qiu\",\"doi\":\"10.1109/ROBIO.2013.6739459\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unstructured domestic environments present a substantial challenge to effective robotic operation. Domestic environment requires service robots to deal with unexpected environment changes, novel objects, and user manipulations. We present an approach to enable service robots to actively learn high-level skills in an unstructured environment. This involves using a combination of processing environment changes, recording and learning user manipulation data, setting up meaningful hypothesis, proactively performing test actions and interacting with user feedback, and logic reasoning. We demonstrate our Robot Self-Learning (RSL) approach by using ROS (Robotic Operating System) and Care-O-bot (COB) 3. These methods enable service robots to learn generalized high-level skills in a sophisticated and changing environment. The RSL approach allows robots to learn new actions imposed by a human and action condition from perception changes from the environment. We also present logic based reasoning engine to speed up the self learning process.\",\"PeriodicalId\":434960,\"journal\":{\"name\":\"2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ROBIO.2013.6739459\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROBIO.2013.6739459","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Human interaction based Robot Self-Learning approach for generic skill learning in domestic environment
Unstructured domestic environments present a substantial challenge to effective robotic operation. Domestic environment requires service robots to deal with unexpected environment changes, novel objects, and user manipulations. We present an approach to enable service robots to actively learn high-level skills in an unstructured environment. This involves using a combination of processing environment changes, recording and learning user manipulation data, setting up meaningful hypothesis, proactively performing test actions and interacting with user feedback, and logic reasoning. We demonstrate our Robot Self-Learning (RSL) approach by using ROS (Robotic Operating System) and Care-O-bot (COB) 3. These methods enable service robots to learn generalized high-level skills in a sophisticated and changing environment. The RSL approach allows robots to learn new actions imposed by a human and action condition from perception changes from the environment. We also present logic based reasoning engine to speed up the self learning process.