Enabling Novices to Diagnose Robot Failures by Aligning Users' Mental Models of Robots

As robots continue to be adopted into our everyday lives they may encounter unforeseen circumstances, resulting in failures that require assistance from nearby people.When people enter interactions with robots, they leverage their mental models of the system and its functions.These mental models are based on a person's knowledge of and experiences with that robot and others.For this reason, the models are often incomplete or inaccurate, resulting in inefficient interactions.Understanding a complex robot and its functions is difficult, especially for novices.Therefore, when robots require assistance it is necessary for them to explain their failures in a manner that not only provides enough context for a person to resolve the error, but that also helps correct people's misaligned mental models.Through this work, I aim to enable non-experts to more efficiently and effectively diagnose and resolve robot failures.