{"title":"Active Sensing for Continuous State and Action Spaces via Task-Action Entropy Minimization.","authors":"Tipakorn Greigarn, M Cenk Çavuşoğlu","doi":"10.1109/IROS.2016.7759688","DOIUrl":null,"url":null,"abstract":"<p><p>In this paper, a new task-oriented active-sensing method is presented. Most active sensing methods choose sensing actions that minimize the uncertainty of the state according to some information-theoretic measure. While this is reasonable for most applications, minimizing state uncertainty may not be most relevant when the state information is used to perform a task. This is because the uncertainty in some subspace of the state space could have more impact on the performance of the task than the others at a given time. The active-sensing method presented in this paper takes the task into account when selecting sensing actions by minimizing the uncertainty in future task action.</p>","PeriodicalId":74523,"journal":{"name":"Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"2016 ","pages":"4678-4684"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/IROS.2016.7759688","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IROS.2016.7759688","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2016/12/1 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, a new task-oriented active-sensing method is presented. Most active sensing methods choose sensing actions that minimize the uncertainty of the state according to some information-theoretic measure. While this is reasonable for most applications, minimizing state uncertainty may not be most relevant when the state information is used to perform a task. This is because the uncertainty in some subspace of the state space could have more impact on the performance of the task than the others at a given time. The active-sensing method presented in this paper takes the task into account when selecting sensing actions by minimizing the uncertainty in future task action.