Robots that Take Advantage of Human Trust
Robots that Take Advantage of Human Trust
复制标题
利用人类信任的机器人
DOI：
10.1109/iros40897.2019.8968564
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Dorsa Sadigh
中科院分区：
文献类型：
--
作者：
Dylan P. Losey;Dorsa Sadigh
人类常常认为机器人是理性的。我们相信机器人会根据其目标采取最优行动；因此，当我们不确定机器人的目标是什么时，我们会依据自己对其目标的估计，将机器人的行动解读为最优的。当机器人直接优化其目标时，这种方法是合理的，并且能让人类了解机器人试图实现什么。然而，我们的见解是——当机器人意识到人类通过相信机器人的行动是理性的来学习时——智能机器人不会按照人类的预期行动；相反，它们会利用人类的信任，并利用这种信任更有效地优化自身的目标。在本文中，我们使用一个双人博弈正式对人机交互（HRI）中人类不知道机器人目标的情况进行建模。我们阐述了机器人对不确定的人类进行建模的不同方式，并比较了当机器人具有保守型、乐观型、理性型和信任型人类模型时该博弈的解决方案。在一个离线线性二次型案例研究和一个实时用户研究中，我们表明信任型人类模型能够自然地导致机器人的交流行为，这种行为会影响终端用户并增加他们的参与度。
Humans often assume that robots are rational. We believe robots take optimal actions given their objective; hence, when we are uncertain about what the robot’s objective is, we interpret the robot’s actions as optimal with respect to our estimate of its objective. This approach makes sense when robots straightforwardly optimize their objective, and enables humans to learn what the robot is trying to achieve. However, our insight is that–when robots are aware that humans learn by trusting that the robot actions are rational–intelligent robots do not act as the human expects; instead, they take advantage of the human’s trust, and exploit this trust to more efficiently optimize their own objective. In this paper, we formally model instances of human-robot interaction (HRI) where the human does not know the robot’s objective using a two-player game. We formulate different ways in which the robot can model the uncertain human, and compare solutions of this game when the robot has conservative, optimistic, rational, and trusting human models. In an offline linear-quadratic case study and a real-time user study, we show that trusting human models can naturally lead to communicative robot behavior, which influences end-users and increases their involvement.
影响因子：
3.5
作者：
Huang, Sandy H.;Held, David;Dragan, Anca D.
通讯作者：
Dragan, Anca D.