Trust is a factor that is becoming more prominent in human robot interaction research. Only few approaches so far tackle the challenge of data-driven trust assessment. In this paper, we present a data set consisting of motion tracking data from an industrial human robot collaboration task. The data is collected during a trust manipulation experiment that has been designed to elicit different trust levels in the participants. Additionally, participants filled out a standard trust questionnaire. The data set allows for developing and testing data-driven trust assessment algorithms.

Trust in automation is usually assessed with post-interaction questionnaires. For human robot collaboration it would be beneficial to assess the trust level during the interaction to adjust the robot’s collaboration behavior to the user expectations. In this paper we investigate if trust can be estimated from observable behavior like movements during the interaction with a large industrial manipulator. To this end, we report on a data collection for two tasks during collaborative draping, the transport of large cut pieces and the actual draping process in close proximity to the robot. The data is used to train and compare different deep learning models. Results show that automatic trust estimation is feasible, which opens up to using trust as a parameter for informing the interaction with robots.