Enabling robots to communicate their objectives
Enabling robots to communicate their objectives
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DOI：
10.1007/s10514-018-9771-0
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发表时间：
2019-02-01
影响因子：
3.5
通讯作者：
Dragan, Anca D.
中科院分区：
文献类型：
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作者：
Huang, Sandy H.;Held, David;Dragan, Anca D.
这项工作的首要目标是有效地使终端用户能够在新情况下正确预测机器人的行为。由于机器人的行为往往是其潜在目标函数的直接结果，我们的见解是，终端用户需要对这个目标函数有一个准确的心理模型，以便理解和预测机器人将会做什么。虽然人们通过观察机器人的行为会自然地逐渐形成这样一个心理模型，但这个熟悉过程可能很漫长。我们的方法通过让机器人模拟人们如何从观察到的行为推断目标来减少这个时间，以便展示那些信息量最大的行为。我们引入两个因素来定义人类推理的候选模型，并表明某些模型确实产生了一些机器人行为示例，这些示例能更好地使用户预测机器人在新情况下会做什么。我们的结果还表明，选择合适的模型是关键，并暗示我们的候选模型不能完全捕捉人类如何从机器人行为的示例中进行推断。我们利用这些发现提出了在这种情况下一个更强的人类学习模型，并通过分析假设的人类学习模型可能不正确的不同方式的影响来得出结论。
The overarching goal of this work is to efficiently enable end-users to correctly anticipate a robot's behavior in novel situations. And since a robot's behavior is often a direct result of its underlying objective function, our insight is that end-users need to have an accurate mental model of this objective function in order to understand and predict what the robot will do. While people naturally develop such a mental model over time through observing the robot act, this familiarization process may be lengthy. Our approach reduces this time by having the robot model how people infer objectives from observed behavior, in order to then show those behaviors that are maximally informative. We introduce two factors to define candidate models of human inference, and show that certain models indeed produce example robot behaviors that better enable users to anticipate what it will do in novel situations. Our results also reveal that choosing the appropriate model is key, and suggest that our candidate models do not fully capture how humans extrapolate from examples of robot behavior. We leverage these findings to propose a stronger model of human learning in this setting, and conclude by analyzing the impact of different ways in which the assumed model of human learning may be incorrect.