Expectations, Explanations, and Embodiment: Attempts at Robot Failure Recovery

TL;DR

The paper examines how user expectations, shaped via short priming videos, influence judgments of robot failures and the effectiveness of explanations in HRI. Using two distinct robot embodiments (Pepper and Furhat) across two online studies, it first validates priming as a trigger for high or low expectations and then tests whether explanations about failures improve perception and satisfaction, with results showing stronger benefits when initial expectations are low and when the robot’s embodiment supports expressive communication. The findings highlight embodiment and task characteristics as key moderators of explanatory usefulness, suggesting tailored explanation strategies in HRI to preserve trust and satisfaction. Practically, the work informs design choices for explanation modalities and context-aware expectation management to mitigate the impact of inevitable robot mistakes.

Abstract

Expectations critically shape how people form judgments about robots, influencing whether they view failures as minor technical glitches or deal-breaking flaws. This work explores how high and low expectations, induced through brief video priming, affect user perceptions of robot failures and the utility of explanations in HRI. We conducted two online studies ($N=600$ total participants); each replicated two robots with different embodiments, Furhat and Pepper. In our first study, grounded in expectation theory, participants were divided into two groups, one primed with positive and the other with negative expectations regarding the robot's performance, establishing distinct expectation frameworks. This validation study aimed to verify whether the videos could reliably establish low and high-expectation profiles. In the second study, participants were primed using the validated videos and then viewed a new scenario in which the robot failed at a task. Half viewed a version where the robot explained its failure, while the other half received no explanation. We found that explanations significantly improved user perceptions of Furhat, especially when participants were primed to have lower expectations. Explanations boosted satisfaction and enhanced the robot's perceived expressiveness, indicating that effectively communicating the cause of errors can help repair user trust. By contrast, Pepper's explanations produced minimal impact on user attitudes, suggesting that a robot's embodiment and style of interaction could determine whether explanations can successfully offset negative impressions. Together, these findings underscore the need to consider users' expectations when tailoring explanation strategies in HRI. When expectations are initially low, a cogent explanation can make the difference between dismissing a failure and appreciating the robot's transparency and effort to communicate.

Figures (7)

• Figure 1: Priming scenario with Pepper (left) and Furhat (right).
• Figure 2: Experiment flow for the priming study.
• Figure 3: EmCorp results for the priming study.
• Figure 4: GAToRS results for the priming study.
• Figure 5: Experiment flow for the main study.