There is a knowledge gap regarding which types of failures robots undergo in domestic settings and how these failures influence customer experience. We classified 10,072 customer reviews of small utilitarian domestic robots on Amazon by the robotic failures described in them, grouping failures into twelve types and three categories (Technical, Interaction, and Service). We identified sources and types of failures previously overlooked in the literature, combining them into an updated failure taxonomy. We analyzed their frequencies and relations to customer star ratings. Results indicate that for utilitarian domestic robots, Technical failures were more detrimental to customer experience than Interaction or Service failures. Issues with Task Completion and Robustness & Resilience were commonly reported and had the most significant negative impact. Future failure-prevention and response strategies should address the technical ability of the robot to meet functional goals, operate and maintain structural integrity over time. Usability and interaction design were less detrimental to customer experience, indicating that customers may be more forgiving of failures that impact these aspects for the robots and practical uses examined. Further, we developed a Natural Language Processing model capable of predicting whether a customer review contains content that describes a failure and the type of failure it describes. With this knowledge, designers and researchers of robotic systems can prioritize design and development efforts towards essential issues.

翻译：关于在国内环境中哪些类型的失败机器人会发生,以及这些失败如何影响客户经验,存在着知识差距。我们将10 072个客户审查亚马孙上小型实用型国内机器人因机器人失败而在亚马逊上进行分类,将失败分为12种类型和三类(技术、互动和服务)。我们查明了文献中以前忽视的失败的来源和类型,将其合并为最新的失败分类。我们分析了它们的频率和与客户明星评级的关系。结果显示,对于实用型国内机器人而言,技术失败比互动或服务失败更不利于客户经验。任务完成和强力和复原力问题经常被报告,并具有最重大的负面影响。未来的失败预防和应对战略应当解决机器人实现功能目标、操作和保持结构完整性的技术能力问题,使用性和互动设计对客户经验没有多大的损害,表明客户可能更认识到这些方面的失败会影响机器人和实际用途。此外,我们开发了一种自然语言处理模型,能够预测客户审查是否包含描述失败和失败内容的内容,并能够描述其基本研发工作的优先度和优先度。有了这一系统,设计师和设计师能描述这些系统。