Interactive Human-Robot Teaching Recovers and Builds Trust, Even With Imperfect Learners

Building and maintaining trust is critically important for continued human-robot teaching and the prospect of robots learning social skills from natural environments. Whereas previous work often explored strategies to reduce system errors, mitigate trust loss, or enhance learning by interactive teaching, few studies have investigated the possible benefits of fully engaged, interactive teaching on human trust. Motivated by a pair of discrepant previous investigations, the present studies for the first time directly tested the causal impact of interactivity on the loss and recovery of trust in a human-robot social skills training context. Building on a previously developed experimental paradigm, we randomly assigned participants to one of two modes of interaction: interactive teacher vs. supervisor of an experimentally controlled virtual robot. The robot was engaged in learning norm-appropriate behavior in a healthcare setting and improved from mistake-prone to near-flawless performance. Participants indicated their changing trust during the 15-trial training session and how much they attributed the robot’s improvement to their own training contributions. Interactive teachers were more resilient to initial trust loss, showed increased trust in the robot’s performance on additional tasks, and attributed more of the robot’s improvement to themselves than did supervisors, even when the robots were slow learners.CCS CONCEPTS• Applied computing → Psychology; • Human-centered computing → Empirical studies in HCI.