Human-Robot Trust Assessment Using Top-Down Visual Tracking After Robot Task Execution Mistakes

Abstract

With increased interest in close-proximity human-robot collaboration in production settings it is important that we understand how robot behaviors and mistakes affect human-robot trust, as a lack of trust can cause loss in productivity and over-trust can lead to hazardous misuse. We designed a system for real-time human-robot trust assessment using a top-down depth camera tracking setup with the goal of using signs of physical apprehension to infer decreases in trust toward the robot. In an experiment with 20 participants we evaluated the tracking system in a repetitive collaborative pick-and-place task where the participant and the robot had to move a set of cones across a table. Midway through the tasks we disrupted the participants expectations by having the robot perform a trust-dampening action. Throughout the tasks we measured the participant’s preferred proximity and their trust toward the robot. Comparing irregular robot movements versus task execution mistakes as well simultaneous versus turn-taking collaboration, we found reported trust was significantly decreased when the robot performed an execution mistake going counter to the shared objective. This decrease was higher for participant working simultaneously as the robot. The effect of the trust-dampening actions on preferred proximity was inconclusive due to unexplained movement trends between tasks throughout the experiment. Despite being given the option to stop the robot in case of abnormal behavior, the trust-dampening actions did not increase the number of participant disruptions for the actions we tested.