  Robots that carry out tasks and interact in complex environments will
inevitably commit errors. Error detection is thus an essential ability for
robots to master to work efficiently and productively. People can leverage
social feedback to get an indication of whether an action was successful or
not. With advances in computing and artificial intelligence (AI), it is
increasingly possible for robots to achieve a similar capability of collecting
social feedback. In this work, we take this one step further and propose a
framework for how social cues can be used as feedback signals to recognize task
failures for human-robot interaction (HRI). Our proposed framework sets out a
research agenda based on insights from the literature on behavioral science,
human-robot interaction, and machine learning to focus on three areas: 1)
social cues as feedback (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
provide recommendations for HRI researchers and practitioners interested in
developing robots that detect task errors using human social cues. This article
is intended for interdisciplinary HRI researchers and practitioners, where the
third theme of our analysis provides more technical details aiming toward the
practical implementation of these systems.
