While substantial effort has been invested in making robots more reliable, experience demonstrates that robots operating in unstructured environments are often challenged by frequent failures. Despite this, robots have not yet reached a level of design that allows effective management of faulty or unexpected behavior by untrained users. To understand why this may be the case, an in-depth literature review was done to explore when people perceive and resolve robot failures, how robots communicate failure, how failures influence people's perceptions and feelings toward robots, and how these effects can be mitigated. Fifty-two studies were identified relating to communicating failures and their causes, the influence of failures on human-robot interaction (HRI), and mitigating failures. Since little research has been done on these topics within the HRI community, insights from the fields of human computer interaction (HCI), human factors engineering, cognitive engineering and experimental psychology are presented and discussed. Based on the literature, we developed a model of information processing for robotic failures (Robot Failure Human Information Processing, RF-HIP), that guides the discussion of our findings. The model describes the way people perceive, process, and act on failures in human robot interaction. The model includes three main parts: (1) communicating failures, (2) perception and comprehension of failures, and (3) solving failures. Each part contains several stages, all influenced by contextual considerations and mitigation strategies. Several gaps in the literature have become evident as a result of this evaluation. More focus has been given to technical failures than interaction failures. Few studies focused on human errors, on communicating failures, or the cognitive, psychological, and social determinants that impact the design of mitigation strategies. By providing the stages of human information processing, RF-HIP can be used as a tool to promote the development of user-centered failure-handling strategies for HRIs.

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In three laboratory experiments, we examine the impact of personally relevant failures (PeRFs) on users’ perceptions of a collaborative robot. PeR is determined by how much a specific issue applies to a particular person, i.e., it affects one's own goals and values. We hypothesized that PeRFs would reduce trust in the robot and the robot's Likeability and Willingness to Use (LWtU) more than failures that are not personal to participants. To achieve PeR in human–robot interaction, we utilized three different manipulation mechanisms: (A) damage to property, (B) financial loss, and (C) first-person versus third-person failure scenarios. In total, 132 participants engaged with a robot in person during a collaborative task of laundry sorting. All three experiments took place in the same experimental environment, carefully designed to simulate a realistic laundry sorting scenario. Results indicate that the impact of PeRFs on perceptions of the robot varied across the studies. In experiments A and B, the encounters with PeRFs reduced trust significantly relative to a no failure session. But not entirely for LWtU. In experiment C, the PeR manipulation had no impact. The work highlights challenges and adjustments needed for studying robotic failures in laboratory settings. We show that PeR manipulations affect how users perceive a failing robot. The results bring about new questions regarding failure types and their perceived severity on users' perception of the robot. Putting PeR aside, we observed differences in the way users perceive interaction failures compared (experiment C) to how they perceive technical ones (A and B).