Detecting Contingency for HRI in Open-World Environments
Detecting Contingency for HRI in Open-World Environments
复制标题
检测开放世界环境中 HRI 的意外事件
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
A. Thomaz
中科院分区：
文献类型：
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作者：
Elaine Schaertl Short;M. L. Chang;A. Thomaz
本文提出了一种新的算法，检测偶然的反应，机器人的行为在嘈杂的现实世界的环境与天真的用户。先前的工作已经确定，检测偶然性的一种方式是通过计算机器人探测环境之前和之后的传感器数据之间的差异度量。我们的算法，CIRCLE（应急互动实时分类的参与）提供了一种新的方法来计算这种差异和检测应急，提高运行时间的差异计算从2.5秒到约0.001秒的1100样本向量，并有效地实现实时检测偶发事件。我们显示的准确性相媲美的最佳离线结果检测应急以这种方式（89.5%对91%在以前的工作），并证明实用程序的实时应急检测的实地研究中的调查管理机器人在嘈杂的开放世界环境与天真的用户，这表明机器人可以减少请求的数量（从38个减少到13个），同时更有效地收集调查响应（30%的响应率而不是26.3%）。
This paper presents a novel algorithm for detecting contingent reactions to robot behavior in noisy real-world environments with naive users. Prior work has established that one way to detect contingency is by calculating a difference metric between sensor data before and after a robot probe of the environment. Our algorithm, CIRCLE (Contingency for Interactive Real-time CLassification of Engagement) provides a new approach to calculating this difference and detecting contingency, improving the running time for the difference calculation from 2.5 seconds to approximately 0.001 seconds on an 1100-sample vector, and effectively enabling real-time detection of contingent events. We show accuracy comparable to the best offline results for detecting contingency in this way (89.5% vs 91% in prior work), and demonstrate the utility of the real-time contingency detection in a field study of a survey-administering robot in a noisy open-world environment with naïve users, showing that the robot can decrease the number of requests it makes (from 38 to 13) while more efficiently collecting survey responses (30% response rate rather than 26.3%).