Human-robot collaboration in industrial settings is an expanding research field in robotics. When working together, robot mistakes are an important factor to decrease trust and therefore interferes with cooperation. It is unclear whether explanations help to restore human-robot trust after a mistake. In our study, we investigate whether system explanations as a trust-repairing action after a robot makes a mistake in a collaborative task is helpful. Our pilot study revealed that users are more interested in solutions to errors than they are in just why the error happened. Therefore, in our main study, we evaluated three levels of mistake explanations (no explanation, explanation, and explanation with solution) after a robot in VR made a mistake in executing a shared objective. After testing with 30 participants we found that the robot making a mistake significantly affects trust toward the robot, compared to it completing the task successfully. While participants found the explanations helpful to trust or distrust the robot, the levels of the explanation did not lead to an increase in trust towards the robot after a mistake. In addition, we found no significant impact of explanations on self-efficacy and the emotional state of the participants. Our results show that explanations alone are not sufficient to increase human-computer trust after robot mistakes.
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Hald et al. (2021) studied this question.
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