A challenging category of robotics problems arises when sensing incurs substantial costs. This paper examines settings in which a robot wishes to limit its observations of state, for instance, motivated by specific considerations of energy management, stealth, or implicit coordination. We formulate the problem of planning under uncertainty when the robot's observations are intermittent but their timing is known via a pre-declared schedule. After having established the appropriate notion of an optimal policy for such settings, we tackle the problem of joint optimization of the cumulative execution cost and the number of state observations, both in expectation under discounts. To approach this multi-objective optimization problem, we introduce an algorithm that can identify the Pareto front for a class of schedules that are advantageous in the discounted setting. The algorithm proceeds in an accumulative fashion, prepending additions to a working set of schedules and then computing incremental changes to the value functions. Because full exhaustive construction becomes computationally prohibitive for moderate-sized problems, we propose a filtering approach to prune the working set. Empirical results demonstrate that this filtering is effective at reducing computation while incurring only negligible reduction in quality. In summarizing our findings, we provide a characterization of the run-time vs quality trade-off involved.

翻译：一类具有挑战性的机器人问题出现在传感过程需要高昂成本时。本文研究了机器人希望限制其状态观测的场景，例如出于能量管理、隐身或隐式协调等特定考量。我们建立了机器人在观测间歇发生但其时间通过预声明排程已知的情况下，进行不确定性下规划的问题框架。在确立了此类场景中最优策略的适当定义后，我们着手解决累积执行成本与状态观测次数的联合优化问题（两者均在折扣因子下取期望）。为处理这一多目标优化问题，我们提出了一种能够识别折扣设置下优势排程类别中帕累托前沿的算法。该算法以累积方式运行：将新增排程预置入工作集，然后计算价值函数的增量变化。由于中等规模问题的穷举构建在计算上变得不可行，我们提出了一种过滤方法来精简工作集。实验结果表明，这种过滤方法能有效降低计算量，同时仅带来可忽略的质量损失。在总结研究成果时，我们给出了运行时间与质量之间权衡的定性描述。