A tale of two explanations: Enhancing human trust by explaining robot behavior
A tale of two explanations: Enhancing human trust by explaining robot behavior
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DOI：
10.1126/scirobotics.aay4663
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发表时间：
2019-12-18
期刊：
影响因子：
25
通讯作者：
Zhu, Song-Chun
中科院分区：
文献类型：
--
作者：
Edmonds, Mark;Gao, Feng;Zhu, Song-Chun
对所选择的行动提供全面解释的能力是智力的标志。这种能力的缺乏阻碍了人工智能和机器人系统在关键任务中的普遍接受。本文探讨了什么形式的解释最能促进人类对机器的信任，并提出了一个框架，在这个框架中，从功能和机械的角度来生成解释。机器人系统学习从人类示范打开药瓶使用（i）一个具体的触觉预测模型，从感官反馈中提取知识，（ii）一个随机的语法模型诱导捕获的组成结构的多步任务，和（iii）一个改进的厄利解析算法，以共同利用触觉和语法模型。机器人系统不仅展示了向人类示范者学习的能力，而且还成功地打开了新的、看不见的瓶子。使用机器人系统生成的不同形式的解释，我们进行了一项心理实验，以研究什么形式的解释最能促进人类对机器人的信任。我们发现，机器人内部决策的全面和实时可视化在促进人类信任方面比基于摘要文本描述的解释更有效。此外，最适合培养信任的解释形式不一定与有助于最佳任务绩效的模型组件相对应。这种分歧表明，机器人社区需要集成模型组件，以增强任务执行和人类对机器的信任。
The ability to provide comprehensive explanations of chosen actions is a hallmark of intelligence. Lack of this ability impedes the general acceptance of Al and robot systems in critical tasks. This paper examines what forms of explanations best foster human trust in machines and proposes a framework in which explanations are generated from both functional and mechanistic perspectives. The robot system learns from human demonstrations to open medicine bottles using (i) an embodied haptic prediction model to extract knowledge from sensory feedback, (ii) a stochastic grammar model induced to capture the compositional structure of a multistep task, and (iii) an improved Earley parsing algorithm to jointly leverage both the haptic and grammar models. The robot system not only shows the ability to learn from human demonstrators but also succeeds in opening new, unseen bottles. Using different forms of explanations generated by the robot system, we conducted a psychological experiment to examine what forms of explanations best foster human trust in the robot. We found that comprehensive and real-time visualizations of the robot's internal decisions were more effective in promoting human trust than explanations based on summary text descriptions. In addition, forms of explanation that are best suited to foster trust do not necessarily correspond to the model components contributing to the best task performance. This divergence shows a need for the robotics community to integrate model components to enhance both task execution and human trust in machines.