In the real world, robots frequently make errors, yet little is known about people's social responses to errors outside of lab settings. Prior work has shown that social signals are reliable and useful for error management in constrained interactions, but it is unclear if this holds in the real world - especially with a non-social robot in repeated and group interactions with successive or propagated errors. To explore this, we built a coffee robot and conducted a public field deployment ($N = 49$). We found that participants consistently expressed varied social signals in response to errors and other stimuli, particularly during group interactions. Our findings suggest that social signals in the wild are rich (with participants volunteering information about the interaction), but "noisy." We discuss lessons, benefits, and challenges for using social signals in real-world HRI.

翻译：在现实世界中，机器人频繁出错，但人们对实验室环境之外错误的社会反应却鲜为人知。先前研究表明，在受限交互中，社交信号对于错误管理是可靠且有用的，但尚不清楚这在现实世界中是否成立——尤其是在与非社交机器人进行重复和群体交互，且错误连续发生或传播的情况下。为探究此问题，我们构建了一台咖啡机器人并进行了公开实地部署（$N = 49$）。我们发现，参与者对错误及其他刺激（尤其在群体交互期间）持续表现出多样化的社交信号。我们的研究结果表明，现实环境中的社交信号是丰富的（参与者会主动提供交互相关信息），但也是“嘈杂的”。我们讨论了在现实世界人机交互中使用社交信号的经验教训、优势与挑战。