Active Sensing for Continuous State and Action Spaces via Task-Action Entropy Minimization.
Active Sensing for Continuous State and Action Spaces via Task-Action Entropy Minimization.
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DOI：
10.1109/iros.2016.7759688
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发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
Çavuşoğlu MC
中科院分区：
文献类型：
--
作者：
Greigarn T;Çavuşoğlu MC
本文提出了一种新的面向任务的主动感知方法。大多数主动传感方法根据某种信息论度量选择使状态不确定性最小化的传感动作。虽然这对于大多数应用是合理的，但是当状态信息用于执行任务时，最小化状态不确定性可能不是最相关的。这是因为在给定时间，状态空间的某些子空间中的不确定性可能对任务的性能产生比其他子空间更大的影响。本文提出的主动感知方法通过最小化未来任务动作的不确定性来选择感知动作。
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.