论文标题
建模人类对机器人错误的反应以及时错误检测
Modeling Human Response to Robot Errors for Timely Error Detection
论文作者
论文摘要
在人机协作中，机器人错误是不可避免的 - 损害用户信任，愿意共同努力以及任务绩效。先前的工作表明，人们自然会对机器人错误的社会响应，并且在社交互动中，可以使用人类反应来检测错误。但是，在非社会，物理人类机器人协作（例如组装和工具检索）的领域几乎没有探索。在这项工作中，我们调查了人们对机器人错误的有机社会反应如何用于及时自动检测物理人类机器人相互作用中的错误。我们进行了一项数据收集研究，以获得面部响应，以培训一种实时检测算法和案例研究，以探索我们通过不同的任务设置和错误的方法的普遍性。我们的结果表明，自然的社会响应是即使在非社会上下文中的机器人错误的及时检测和定位的有效信号，并且我们的方法在各种任务上下文，机器人错误和用户响应中都有坚固态度。这项工作有助于无需详细的任务规格而进行强大的错误检测。
In human-robot collaboration, robot errors are inevitable -- damaging user trust, willingness to work together, and task performance. Prior work has shown that people naturally respond to robot errors socially and that in social interactions it is possible to use human responses to detect errors. However, there is little exploration in the domain of non-social, physical human-robot collaboration such as assembly and tool retrieval. In this work, we investigate how people's organic, social responses to robot errors may be used to enable timely automatic detection of errors in physical human-robot interactions. We conducted a data collection study to obtain facial responses to train a real-time detection algorithm and a case study to explore the generalizability of our method with different task settings and errors. Our results show that natural social responses are effective signals for timely detection and localization of robot errors even in non-social contexts and that our method is robust across a variety of task contexts, robot errors, and user responses. This work contributes to robust error detection without detailed task specifications.
