In this paper we investigate the notion of in sequential decision tasks under uncertainty. Previous works that extend legibility to scenarios beyond robot motion either focus on deterministic settings or are computationally too expensive. Our proposed approach, dubbed PoLMDP, is able to handle uncertainty while remaining computationally tractable. We establish the advantages of our approach against state-of-the-art approaches in several scenarios of varying complexity. We also showcase the use of our legible policies as demonstrations in machine teaching scenarios, establishing their superiority in teaching new behaviours against the commonly used demonstrations based on the optimal policy. Finally, we assess the legibility of our computed policies through a user study, where people are asked to infer the goal of a mobile robot following a legible policy by observing its actions.

中文翻译：

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“猜猜我在做什么”：将易读性扩展到顺序决策任务

在本文中，我们研究了不确定性下顺序决策任务的概念。先前将易读性扩展到机器人运动之外的场景的工作要么专注于确定性设置，要么计算成本太高。我们提出的方法被称为 PoLMDP，能够处理不确定性，同时保持计算上的易处理性。我们在不同复杂性的几种场景中确立了我们的方法相对于最先进方法的优势。我们还展示了在机器教学场景中使用我们的清晰策略作为演示，相对于基于最优策略的常用演示，确立了它们在教授新行为方面的优势。最后，我们通过用户研究评估计算策略的可读性，要求人们通过观察移动机器人的行为来推断移动机器人遵循清晰策略的目标。