How Do We Perceive Our Trainee Robots? Exploring the Impact of Robot Errors and Appearance When Performing Domestic Physical Tasks on Teachers' Trust and Evaluations

To be successful, robots that can learn new tasks from humans should interact effectively with them while being trained, and humans should be able to trust the robots' abilities after teaching. Typically, when human learners make mistakes, their teachers tolerate those errors, especially when students exhibit acceptable progress overall. But how do errors and appearance of a trainee robot affect human teachers' trust while the robot is generally improving in performing a task? First, an online survey with 173 participants investigated perceived severity of robot errors in performing a cooking task. These findings were then used in an interactive online experiment with 138 participants, in which the participants were able to remotely teach their food preparation preferences to trainee robots with two different appearances. Compared with an untidy-looking robot, a tidy-looking robot was rated as more professional, without impacting participants' trust. Furthermore, while larger errors at the end of iterative training had a greater impact, even a small error could significantly reduce trust in a trainee robot performing the domestic physical task of food preparation, regardless of the robot's appearance. The present study extends human-robot interaction knowledge about teachers' perception of trainee robots, particularly when teachers observe them accomplishing domestic physical tasks.