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.

Our goal is to design a framework for holistic evaluation of human-robot collaboration systems. To this end we utilize several standardized questionnaires administered while participants perform collaborative tasks in robot work cells. We used Standard Usability Scale and the Usability metric for user experience questionnaires to access usability, NASA Task-Load for workload, two questionnaires for human-robot trust as well as the Unified theory of acceptance and use of technology questionnaires. We performed two pilot tests of our framework with human-robot collabotation work cells at two test sites as part of the DrapeBot project. The goal of the project is to enable human-robot collaboration in the process of carbon fiber draping the production of outer parts. After utilizing the evaluation framework at the two test sites we found that the collection of questionnaires were easy to adapt to each work cell and the practical limitation around running the experiments. Both work cells scored high in usability, expected increase of productivity, as well as high trust and low anxiety, but both work cells scored low on expectancy of use for work in the future at their current state of development.

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.

Close-proximity human-robot collaboration (HRC) requires an appropriate level of trust from the operator to the robot to maintain safety and efficiency. Maintaining an appropriate trust level during robot-aided production requires non-obstructive real-time human-robot trust assessment. To this end we performed an experiment with 20 participants performing two types of HRC tasks in close proximity to a Kuka KR 300 R2500 ultra robot. The two tasks involved collaborative transport of textiles and collaborative draping, respectively. During the experiment we performed full body motion tracking and administered human-robot trust questionnaires in order investigate the correlation between trust and operator movement patterns. From the initial per-session analyses we see the effects of task types on movement patterns, but the correlations with trust are weak overall. Further analysis at higher temporal resolution and with correction for participants’ base movement patterns are required.

Human-robot collaboration in industrial settings is an expanding research field in robotics. When working together, robot mistakes are an important factor to decrease trust and therefore interferes with cooperation. It is unclear whether explanations help to restore human-robot trust after a mistake. In our study, we investigate whether system explanations as a trust-repairing action after a robot makes a mistake in a collaborative task is helpful. Our pilot study revealed that users are more interested in solutions to errors than they are in just why the error happened. Therefore, in our main study, we evaluated three levels of mistake explanations (no explanation, explanation, and explanation with solution) after a robot in VR made a mistake in executing a shared objective. After testing with 30 participants we found that the robot making a mistake significantly affects trust toward the robot, compared to it completing the task successfully. While participants found the explanations helpful to trust or distrust the robot, the levels of the explanation did not lead to an increase in trust towards the robot after a mistake. In addition, we found no significant impact of explanations on self-efficacy and the emotional state of the participants. Our results show that explanations alone are not sufficient to increase human-computer trust after robot mistakes.

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.

In this study we set out to design a computer vision-based system to assess human-robot trust in real time during close-proximity human-robot collaboration. This paper presents the setup and hardware for an augmented reality-enabled human-robot collaboration cell as well as a method of measuring operator proximity using an infrared camera. We tested this setup as a tool for assessing trust through physical apprehension signals in a collaborative drawing task, where participants hold a piece of paper on a table while the robot draws between their hands. Midway through the test we attempt to induce a decrease in trust with an unexpected change in robot speed and evaluate subject motions along with self-reported trust and emotional arousal through galvanic skin response. After performing the experiment with forty participants, we found that reported trust was significantly affected when robot movement speed was increased. The galvanic skin response measurement were not significantly different between the test conditions. The motion tracking method used in this study did not suggest that subjects' motions were significantly affected by the decrease in trust.

We propose a method of human-robot trust assessment in close-proximity human-robot collaboration involving body tracking for recognition of physical signs of apprehension. We tested this by performing skeleton tracking on 30 participant while they repeated a shared task with a Sawyer robot while reporting trust between tasks. We tested different robot velocity and environment conditions with an unannounced increase in velocity midway through to provoke a dip trust. Initial analysis show significant effect for the test conditions on participant movements and reported trust as well as linear correlations between tracked signs of apprehension and reported trust.