R. Gutierrez, Elaine Schaertl Short, S. Niekum, A. Thomaz
{"title":"从纠正演示中学习","authors":"R. Gutierrez, Elaine Schaertl Short, S. Niekum, A. Thomaz","doi":"10.1109/HRI.2019.8673287","DOIUrl":null,"url":null,"abstract":"Robots deployed in human environments will inevitably encounter unmodeled scenarios which are likely to result in execution failures. To address this issue, we would like to allow co-present naive users to correct and improve the robot's behavior as these edge cases are encountered over time.","PeriodicalId":6600,"journal":{"name":"2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)","volume":"28 1","pages":"712-714"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Learning from Corrective Demonstrations\",\"authors\":\"R. Gutierrez, Elaine Schaertl Short, S. Niekum, A. Thomaz\",\"doi\":\"10.1109/HRI.2019.8673287\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Robots deployed in human environments will inevitably encounter unmodeled scenarios which are likely to result in execution failures. To address this issue, we would like to allow co-present naive users to correct and improve the robot's behavior as these edge cases are encountered over time.\",\"PeriodicalId\":6600,\"journal\":{\"name\":\"2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)\",\"volume\":\"28 1\",\"pages\":\"712-714\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HRI.2019.8673287\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HRI.2019.8673287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robots deployed in human environments will inevitably encounter unmodeled scenarios which are likely to result in execution failures. To address this issue, we would like to allow co-present naive users to correct and improve the robot's behavior as these edge cases are encountered over time.
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