The presented work focuses on investigating the influence of different hand-over timing strategies on the fluency and efficiency of a human-robot team in an assembly task. To this aim, four different timing strategies were experimentally evaluated: (I) a fixed time interval between two handovers, (II) a reactive behavior, where the robot is triggered by the human, (III) fixed time intervals depending on the current component, and (IV) a predictive assembly duration estimation algorithm. During the experiment, the time-to-completion of the task and the waiting times for human and robot were measured as reciprocal indication for the efficiency and respectively the fluency of the team. The results indicate that the efficiency of the human-robot team is significantly increased using a predictive timing strategy, because it enables the robot to provide the needed component just-in-time. The decrease in waiting times for the human worker leads to improved fluency of the collaboration. In addition, the predictive strategy enables the robot to perform preliminary tasks if no assistance is needed. Therefore, a nearly full usage of both partners' capacities can be reached.

The aim of the presented study was to measure physiological correlates of emotions that are of particular interest in the field of human-robot interaction (HRI). Therefore, we did not focus on self-induced basic emotions but rather evoked states that might occur naturally in this context. Our video shows how such states (namely stress, boredom, surprise, and perplexity) were elicited during a joint construction task with an industrial robot (see figure 1). Participants were asked to build different LEGO objects, while the robot arm was passing the bricks with predetermined velocity. States of stress and boredom were generated by varying the handover interval from 3 seconds (stress) to 5 seconds (normal working condition) up to 35 seconds (boredom). Surprise was induced by passing an unexpected component. At the end of the experiment, we additionally wanted to know how people react if the robot seems to tease them by repeatedly changing the handover position.

In many future joint-action scenarios, humans and robots will have to interact physically in order to cooperate successfully. Ideally, human-robot interaction should not require training on the human side, but should be intuitive and simple. Previously, we reported on a simple case of physical human-robot interaction, a hand-over task. Even such a basic task as manually handing over an object from one agent to another requires that both partners agree upon certain basic prerequisites and boundary conditions. While some of them are negotiated explicitly, e.g. by verbal communication, others are determined indirectly and adaptively in the course of the cooperation. In the previous study we compared a human-human hand-over interaction with the same task performed by a human and a robot. However, the trajectories used for the robot, a conventional trapezoidal velocity profile in joint coordinates and a minimum-jerk profile of the end-effector, have little resemblance to the natural movements of humans. In this study we introduce a novel trajectory generator that is a variation of the traditional minimum-jerk profile, the `decoupled minimum-jerk' profile. Its trajectory is much closer to those observed in human-human experiments. We evaluated its performance concerning human comfort and acceptance in a simple hand-over experiment by using a post-test questionnaire. The evaluation of the questionnaire revealed no difference with respect to comfort, human-likeness, or subjective safety of the new planner compared to the minimum-jerk profile. Thus, the `decoupled minimum-jerk' planner, which offers important advantages with respect to target approach, proved to be a promising alternative to the previously used minimum-jerk profile.

While humor is an important aspect of human interaction, this topic has been largely neglected in the field of robotic research. Apart from single text-based humor production systems, non-verbal humor based on gestures, facial expressions, or whole body movements has not been the target of HRI-research so far. In this paper, we present our results on the effects of nonverbal humor displayed by a service robot. Participants were served by a conventional and a ldquofunnyrdquo robot in our experiment. The humorous robot exerted four different kinds of nonverbal humor during the interaction process whereas the conventional robot simply delivered the requested objects. The users' ratings demonstrate the positive effect of humor on the evaluation of different characteristics of the robot, as well as on the perceived interaction quality. To our knowledge, the study described here is the first to investigate the effect of behavioral humor produced by a robot.

Contrary to common emotion recognition techniques by face or speech analysis, physiological data are involuntary and continuously available. Thus, they allow for emotion detection even in situations without spoken words or in case of non-extreme emotions, which are more likely to occur in human-robot interaction (HRI). In this paper, we describe the results of an experiment investigating non-extreme emotional states relevant for HRI scenarios (over- and underchallenge). Those states occurred naturally during the course of a LEGO construction task by manipulating working speed. Data collected from 28 subjects were analyzed and the results of different types of discriminant analysis and nearest neighbour methods were compared. Based on two physiological modalities (HR, SCR), correct classification rates of up to 76% for seven features and 74% for only two features were achieved. Overchallenge could be discriminated very well from the other two conditions (96.4 - 85.7%), whereas underchallenge is often confused with the intermediate condition with normal working speed.