Robots that carry out tasks and interact in complex environments will inevitably commit errors. Error detection is thus an important ability for robots to master, to work in an efficient and productive way. People leverage social cues from others around them to recognize and repair their own mistakes. With advances in computing and AI, it is increasingly possible for robots to achieve a similar error detection capability. In this work, we review current literature around the topic of how social cues can be used to recognize task failures for human-robot interaction (HRI). This literature review unites insights from behavioral science, human-robot interaction, and machine learning, to focus on three areas: 1) social cues for error detection (from behavioral science), 2) recognizing task failures in robots (from HRI), and 3) approaches for autonomous detection of HRI task failures based on social cues (from machine learning). We propose a taxonomy of error detection based on self-awareness and social feedback. Finally, we leave recommendations for HRI researchers and practitioners interested in developing robots that detect (physical) task errors using social cues from bystanders.

翻译：在复杂环境中执行任务并交互的机器人不可避免地会犯错。因此，错误检测是机器人高效工作所需掌握的重要能力。人类利用周围他人的社交线索来识别和纠正自身错误。随着计算与人工智能的发展，机器人越来越有可能实现类似的错误检测能力。本文围绕“如何利用社交线索识别人机交互（HRI）任务失败”这一主题，对现有文献进行综述。该综述融合了行为科学、人机交互与机器学习领域的见解，聚焦于三个研究方向：1）用于错误检测的社交线索（来自行为科学），2）机器人任务失败的识别（来自人机交互），3）基于社交线索自主检测HRI任务失败的方法（来自机器学习）。我们提出了一种基于自我意识与社交反馈的错误检测分类体系。最后，为有兴趣开发利用旁观者社交线索检测（物理）任务错误的机器人的人机交互研究人员与实践者提供了建议。