用于HRI中故障检测的旁观者影响检测（BAD）数据集
The Bystander Affect Detection (BAD) Dataset for Failure Detection in HRI
摘要：
对于机器人来说，要修复自己的错误，它必须首先知道自己犯了错误。人们发现错误的一种方式是从旁观者的隐含反应中--他们的困惑、傻笑或咯咯的笑声暗示我们发生了意想不到的事情。为了使机器人能够检测到旁观者对任务失败的反应并采取行动，我们开发了一种新的方法来引起旁观者对人类和机器人错误的反应。使用46个不同的刺激视频，具有各种各样的人类和机器的任务失败，我们收集了来自54名参与者的2452个网络摄像头视频的人类反应。为了测试所收集数据的可行性，我们使用旁观者反应数据集作为深度学习模型BADNet的输入，以预测故障发生。我们测试了不同的数据标记方法，并了解了它们如何影响模型性能，实现了90%以上的精度。我们讨论的策略，以模拟旁观者的反应和预测失败，以及如何将这种方法可以用于在现实世界中的机器人部署，以检测错误和提高机器人的性能。作为这项工作的一部分，我们还贡献了旁观者反应的“旁观者影响检测”（BAD）数据集，支持开发更好的预测模型。
英文摘要：
For a robot to repair its own error, it must first know it has made a mistake. One way that people detect errors is from the implicit reactions from bystanders -- their confusion, smirks, or giggles clue us in that something unexpected occurred. To enable robots to detect and act on bystander responses to task failures, we developed a novel method to elicit bystander responses to human and robot errors. Using 46 different stimulus videos featuring a variety of human and machine task failures, we collected a total of 2452 webcam videos of human reactions from 54 participants. To test the viability of the collected data, we used the bystander reaction dataset as input to a deep-learning model, BADNet, to predict failure occurrence. We tested different data labeling methods and learned how they affect model performance, achieving precisions above 90%. We discuss strategies to model bystander reactions and predict failure and how this approach can be used in real-world robotic deployments to detect errors and improve robot performance. As part of this work, we also contribute with the "Bystander Affect Detection" (BAD) dataset of bystander reactions, supporting the development of better prediction models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
