{"title":"使用监督学习对服务机器人的操作活动进行监控","authors":"S. Ruehl, Zhixing Xue, R. Dillmann","doi":"10.1109/ICRA.2012.6224803","DOIUrl":null,"url":null,"abstract":"To be a good helper, grasping and manipulation are the most important abilities of a service robot. It should be able to adapt its manipulation actions to new tasks and environments. During the execution, it is important to rate the success of actions, so that the robot can plan and execute further actions to correct and recover from the failed actions. The successful execution of manipulation actions depends on various factors during the whole execution, such as the position of the robotic hand and forces exerted by the robot. The goal of the manipulation action monitoring is to estimate the success state from the huge amount of data collected during the execution. The main challenge to solve this problem is to identify the success or failure state from the the high dimensional data collection. We propose a method to classify ongoing activities using a set of support vector machines (SVM). After a supervised training process with manually labeled successful or failure results, our system can correctly estimate the resulting state of a manipulation activity. We present experiments on our bimanual manipulation demonstrator and evaluate the results.","PeriodicalId":246173,"journal":{"name":"2012 IEEE International Conference on Robotics and Automation","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Monitoring of manipulation activities for a service robot using supervised learning\",\"authors\":\"S. Ruehl, Zhixing Xue, R. Dillmann\",\"doi\":\"10.1109/ICRA.2012.6224803\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To be a good helper, grasping and manipulation are the most important abilities of a service robot. It should be able to adapt its manipulation actions to new tasks and environments. During the execution, it is important to rate the success of actions, so that the robot can plan and execute further actions to correct and recover from the failed actions. The successful execution of manipulation actions depends on various factors during the whole execution, such as the position of the robotic hand and forces exerted by the robot. The goal of the manipulation action monitoring is to estimate the success state from the huge amount of data collected during the execution. The main challenge to solve this problem is to identify the success or failure state from the the high dimensional data collection. We propose a method to classify ongoing activities using a set of support vector machines (SVM). After a supervised training process with manually labeled successful or failure results, our system can correctly estimate the resulting state of a manipulation activity. We present experiments on our bimanual manipulation demonstrator and evaluate the results.\",\"PeriodicalId\":246173,\"journal\":{\"name\":\"2012 IEEE International Conference on Robotics and Automation\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Robotics and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICRA.2012.6224803\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Robotics and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRA.2012.6224803","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Monitoring of manipulation activities for a service robot using supervised learning
To be a good helper, grasping and manipulation are the most important abilities of a service robot. It should be able to adapt its manipulation actions to new tasks and environments. During the execution, it is important to rate the success of actions, so that the robot can plan and execute further actions to correct and recover from the failed actions. The successful execution of manipulation actions depends on various factors during the whole execution, such as the position of the robotic hand and forces exerted by the robot. The goal of the manipulation action monitoring is to estimate the success state from the huge amount of data collected during the execution. The main challenge to solve this problem is to identify the success or failure state from the the high dimensional data collection. We propose a method to classify ongoing activities using a set of support vector machines (SVM). After a supervised training process with manually labeled successful or failure results, our system can correctly estimate the resulting state of a manipulation activity. We present experiments on our bimanual manipulation demonstrator and evaluate the results.