论文标题
“猜猜我在做什么”：扩展到顺序决策任务的知名度
"Guess what I'm doing": Extending legibility to sequential decision tasks
论文作者
论文摘要
在本文中，我们研究了不确定性下的顺序决策任务中可读性的概念。以前的作品将可透明度扩展到方案以外的机器人运动之外的方案要么集中在确定性设置上，要么在计算上太昂贵。我们提出的称为POL-MDP的方法能够处理不确定性，同时剩下计算障碍。在几种不同复杂性的模拟场景中，我们建立了反对最新方法的方法的优势。我们还展示了将我们的清晰政策用作反向加强学习推动者的演示，并根据最佳政策建立了他们的优越性。最后，我们通过用户研究评估计算出的政策的可读性，在该研究中，要求人们通过观察其行动来推断移动机器人的目标。
In this paper we investigate the notion of legibility 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 PoL-MDP, is able to handle uncertainty while remaining computationally tractable. We establish the advantages of our approach against state-of-the-art approaches in several simulated scenarios of different complexity. We also showcase the use of our legible policies as demonstrations for an inverse reinforcement learning agent, establishing their superiority 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.
