{"title":"利用CycleGANs生成情感机器人运动","authors":"Michael Suguitan, Mason Bretan, Guy Hoffman","doi":"10.1109/HRI.2019.8673281","DOIUrl":null,"url":null,"abstract":"Social robots use gestures to express internal and affective states, but their interactive capabilities are hindered by relying on preprogrammed or hand-animated behaviors, which can be repetitive and predictable. We propose a method for automatically synthesizing affective robot movements given manually-generated examples. Our approach is based on techniques adapted from deep learning, specifically generative adversarial neural networks (GANs).","PeriodicalId":6600,"journal":{"name":"2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)","volume":"02 1","pages":"534-535"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Affective Robot Movement Generation Using CycleGANs\",\"authors\":\"Michael Suguitan, Mason Bretan, Guy Hoffman\",\"doi\":\"10.1109/HRI.2019.8673281\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Social robots use gestures to express internal and affective states, but their interactive capabilities are hindered by relying on preprogrammed or hand-animated behaviors, which can be repetitive and predictable. We propose a method for automatically synthesizing affective robot movements given manually-generated examples. Our approach is based on techniques adapted from deep learning, specifically generative adversarial neural networks (GANs).\",\"PeriodicalId\":6600,\"journal\":{\"name\":\"2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)\",\"volume\":\"02 1\",\"pages\":\"534-535\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"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.2019.8673281\",\"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.2019.8673281","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Affective Robot Movement Generation Using CycleGANs
Social robots use gestures to express internal and affective states, but their interactive capabilities are hindered by relying on preprogrammed or hand-animated behaviors, which can be repetitive and predictable. We propose a method for automatically synthesizing affective robot movements given manually-generated examples. Our approach is based on techniques adapted from deep learning, specifically generative adversarial neural networks (GANs).