{"title":"Natural task decomposition with intrinsic potential fields","authors":"Stephen Hart, R. Grupen","doi":"10.1109/IROS.2007.4399481","DOIUrl":null,"url":null,"abstract":"Any given task can be solved in a number of ways, whether through path-planning, modeling, or control techniques. In this paper, we present a methodology for natural task decomposition through the use of intrinsically meaningful potential fields. Specifically, we demonstrate that using classical conditioning measures in a concurrent control framework provides a domain-general means for solving tasks. Among the conditioning measures we use are manipulability [T. Yoshikawam, 1985], localizability [J. Uppala et al., 2002], and range of motion. To illustrate the value of our approach we demonstrate its applicability to an industrially relevant inspection task.","PeriodicalId":227148,"journal":{"name":"2007 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"232 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE/RSJ International Conference on Intelligent Robots and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IROS.2007.4399481","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
Any given task can be solved in a number of ways, whether through path-planning, modeling, or control techniques. In this paper, we present a methodology for natural task decomposition through the use of intrinsically meaningful potential fields. Specifically, we demonstrate that using classical conditioning measures in a concurrent control framework provides a domain-general means for solving tasks. Among the conditioning measures we use are manipulability [T. Yoshikawam, 1985], localizability [J. Uppala et al., 2002], and range of motion. To illustrate the value of our approach we demonstrate its applicability to an industrially relevant inspection task.
任何给定的任务都可以通过多种方式解决,无论是通过路径规划、建模还是控制技术。在本文中,我们提出了一种利用内在意义势场进行任务自然分解的方法。具体来说,我们证明了在并发控制框架中使用经典条件反射措施为解决任务提供了一种领域通用的方法。我们使用的条件反射测量方法包括可操控性[T。[J]。Uppala et al., 2002],和运动范围。为了说明我们的方法的价值,我们展示了它对工业相关检查任务的适用性。