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 some characterization of the run-time vs quality trade-off involved.

翻译：机器人学中一类具有挑战性的问题源于感知过程产生高昂代价。本文针对机器人希望限制状态观测的场景展开研究——这些限制可能源于对能耗管理、隐蔽性或隐式协调等特定因素的考量。我们提出在机器人观测具有间歇性但观测时间通过预设安排已知的情况下，进行不确定性下的规划问题建模。在确定了此类场景下最优策略的适当概念后，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（两者均在贴现条件下取期望值）。为处理这一多目标优化问题，我们引入了一种算法，能够识别贴现场景中具有优势的某类调度方案的帕累托前沿。该算法采用累进式方法运作：通过向工作调度集中添加新元素，并逐步计算值函数的增量变化。由于对中等规模问题进行全枚举穷举计算代价过高，我们提出一种过滤方法来精简工作集。实验结果表明，该过滤策略能在有效降低计算量的同时，仅产生可忽略的性能损失。在总结研究发现时，我们对其中涉及的运行时间与输出质量之间的权衡关系进行了特征化刻画。