Anthropomorphism and trust are two variables that are measured in a multitude of HRI studies. These two variables are often treated as dependent variables influenced by a particular robot design or behavior. However, psychological literature suggests that people have individual tendencies to anthropomorphize, influencing these two variables. We ran an experiment in which 70 participants were confronted with two robots, a humanoid and a non-humanoid robot. Before exposing them to the robots, they were screened for their individual tendency to anthropomorphize. After exposure, they rated the robots in terms of anthropomorphism and trust. With this experiment, we show that the individual tendency to anthropomorphize has a clear impact on measuring anthropomorphism and trust with frequently used questionnaires. Given our results, we emphasize that future studies should screen participants for their tendency to anthropomorphize to avoid unwanted biases.

Trust is important for collaboration.In hybrid teams of humans and robots, trust enables smooth collaboration and reduces risks.Just as collaboration between humans and robots differs from interpersonal collaboration, so does the nature of trust in human-robot interaction (HRI).Therefore, further investigations on trust formation and dissolution in HRI, factors affecting it, and means for keeping trust on an appropriate level are needed.However, our knowledge of interpersonal trust and trust in autonomous agents cannot be transferred directly to HRI.In this paper, we present a study with 32 participants on trust formation and dissolution as well as forecasting to influence trust in an industry robot.Results show differences in dynamics and factors of trust formation and dissolution.Additionally, we find that the effect of forecasting on trust depends on task success.These findings support the design of trustful human-robot interaction and corresponding robotic team members.

Robots are gradually being freed from their safety fences due to the advances in safety features integrated with most new robots. These robots enable a new form of human-robot interaction in which contact is possible. There are two robot-related factors that play a decisive role in the interaction dynamics and human perception in such a case – the motion speed and distance the robot keeps away from the human. Literature indicates that these factors influence whether humans perceive trust, safety, and comfort, which are essential components in the acceptance of robots by their end users. However, although speed and distance were intensively investigated in isolation, little is known about their combined effect. To this end, we conducted an experiment investigating the impact of the industrial robot arm’s approaching speed and stopping distance on the users’ trust, safety, and comfort while they were performing a collaborative task. Our results provide interesting insights into the implications of speed and trust. While we were able to replicate former studies in terms of implications of speed and distance, our data does not show any interaction effect between the two variables. However, our participants’ observations indicated that distance impacts the dependent variables more severely than speed.

Digital assistive systems, enable workers with disabilities to perform complex industrial work. However, the previously presented systems considered only a single workplace and a single user. This paper presents an assembly line that enables a joint processing of complex tasks by multiple workers with and without disabilities. The aim was to investigate the use of interaction technologies such as in-situ projections and hand-tracking to enable the processing of complex assembly tasks by work teams with highly heterogeneous abilities. The developed assembly line assists users and coordinates the joint work by distributing single assembly steps to workers based on the individual workers' abilities. Besides presenting the concept and implementation of the assembly line, we report our findings after six months of operation. Our results indicate that using the assistive assembly line has positive impacts, such as increased satisfaction and independence of the workers combined with a higher productivity.

Augmented Reality (AR) systems are on their way to industrial application, e.g. projection-based AR is used to enhance assembly work. Previous studies showed advantages of the systems in permanent-use scenarios, such as faster assembly times. In this paper, we investigate whether such systems are suitable for training purposes. Within an experiment, we observed the training with a projection-based AR system over multiple sessions and compared it with a personal training and a paper manual training. Our study shows that projection-based AR systems offer only small benefits in the training scenario. While a systematic mislearning of content is prevented through immediate feedback, our results show that the AR training does not reach the personal training in terms of speed and recall precision after 24 hours. Furthermore, we show that once an assembly task is properly trained, there are no differences in the long-term recall precision, regardless of the training method.

This late breaking report describes two explorations of effects of using social media in human-robot interaction. The first is an exploration of how 'autonomous creatures' can use information shared via social awareness streams by implementing a Nabaztag to use information from its 'friends' on location-sharing service foursquare. The second is an informal analysis of tweets sent to an existing robot-associated twitter account as a case. We show parallels to prior research and discuss questions that these simple explorations pose for the future of robots and social media.