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Robots that carry out tasks and interact in complex environments willinevitably commit errors. Error detection is thus an important ability forrobots to master, to work in an efficient and productive way. People leveragesocial cues from others around them to recognize and repair their own mistakes.With advances in computing and AI, it is increasingly possible for robots toachieve a similar error detection capability. In this work, we review currentliterature around the topic of how social cues can be used to recognize taskfailures for human-robot interaction (HRI). This literature review unitesinsights from behavioral science, human-robot interaction, and machinelearning, to focus on three areas: 1) social cues for error detection (frombehavioral 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 onself-awareness and social feedback. Finally, we leave recommendations for HRIresearchers and practitioners interested in developing robots that detect(physical) task errors using social cues from bystanders.

Using Social Cues to Recognize Task Failures for HRI: A Review of Current Research and Future Directions