Reciprocity [48] is an important factor in human-human interaction (HHI), so it can be expected that it should also play a major role in Human-Robot Interaction (HRI). Participants in our study played the Repeated Prisoner's Dilemma game (RPDG) and the mini Ultimatum Game (mUG) with robot and human agents, with the agents using either Tit for Tat (TfT) or Random strategies. As part of the study we also measured the perceived personality traits in the agents using the TIPI test after every round of RPDG and mUG. The results show that the participants collaborated more with humans than with a robot, however they tended to be equally reciprocal with both agents. The experiment also showed the TfT strategy as the most profitable strategy; affecting collaboration, reciprocation, profit and joint profit in the game. Most of the participants tended to be fairer with the human agent in mUG. Furthermore, robots were perceived as less open and agreeable than humans. Consciousness, extroversion and emotional stability were perceived roughly the same in humans and robots. TfT strategy became associated with an extroverted and agreeable personality in the agents. We could observe that the norm of reciprocity applied in Human-Robot Interaction has potential implications for robot design.

Engagement is a concept of the utmost importance in human-computer interaction, not only for informing the design and implementation of interfaces, but also for enabling more sophisticated interfaces capable of adapting to users. While the notion of engagement is actively being studied in a diverse set of domains, the term has been used to refer to a number of related, but different concepts. In fact it has been referred to across different disciplines under different names and with different connotations in mind. Therefore, it can be quite difficult to understand what the meaning of engagement is and how one study relates to another one accordingly. Engagement has been studied not only in human-human, but also in human-agent interactions i.e., interactions with physical robots and embodied virtual agents. In this overview article we focus on different factors involved in engagement studies, distinguishing especially between those studies that address task and social engagement, involve children and adults, are conducted in a lab or aimed for long term interaction. We also present models for detecting engagement and for generating multimodal behaviors to show engagement.

This paper presents a study that allows users to define intuitive gestures to navigate a humanoid robot. For eleven navigational commands, 385 gestures, performed by 35 participants, were analyzed. The results of the study reveal user-defined gesture sets for both novice users and expert users. In addition, we present, a taxonomy of the userdefined gesture sets, agreement scores for the gesture sets, time performances of the gesture motions, and present implications to the design of the robot control, with a focus on recognition and user interfaces.

In this article, we present a novel application domain for human computation, specifically for crowdsourcing, which can help in understanding particle-tracking problems. Through an interdisciplinary inquiry, we built a crowdsourcing system designed to detect tracer particles in industrial tomographic images, and applied it to the problem of bulk solid flow in silos. As images from silo-sensing systems cannot be adequately analyzed using the currently available computational methods, human intelligence is required. However, limited availability of experts, as well as their high cost, motivates employing additional nonexperts. We report on the results of a study that assesses the task completion time and accuracy of employing nonexpert workers to process large datasets of images in order to generate data for bulk flow research. We prove the feasibility of this approach by comparing results from a user study with data generated from a computational algorithm. The study shows that the crowd is more scalable and more economical than an automatic solution. The system can help analyze and understand the physics of flow phenomena to better inform the future design of silos, and is generalized enough to be applicable to other domains.

In social robotics, the behavior of humanoid robots is intended to be designed in a way that they behave in a human-like manner and serve as natural interaction partners for human users. Several aspects of human behavior such as speech, gestures, eye-gaze as well as the personal and social background of the user need therefore to be considered. In this paper, we investigate interpersonal distance as a behavioral aspect that varies with the cultural background of the user. We present two studies that explore whether users of different cultures (Arabs and Germans) expect robots to behave similar to their own cultural background. The results of the first study reveal that Arabs and Germans have different expectations on the interpersonal distance between themselves and robots in a static setting. In the second study, we use the results of the first study to investigate the users' reactions on robots using the observed interpersonal distances themselves. Although the data of this dynamic setting is not conclusive, it suggests that users prefer robots that show behavior that has been observed for their own cultural background before.

This paper presents a framework that allows users to interact with and navigate a humanoid robot using body gestures. The first part of the paper describes a study to define intuitive gestures for eleven navigational commands based on analyzing 385 gestures performed by 35 participants. From the study results, we present a taxonomy of the user-defined gesture sets, agreement scores for the gesture sets, and time performances of the gesture motions. The second part of the paper presents a full body interaction system for recognizing the user-defined gestures. We evaluate the system by recruiting 22 participants to test for the accuracy of the proposed system. The results show that most of the defined gestures can be successfully recognized with a precision between 86−100 % and an accuracy between 73−96 %. We discuss the limitations of the system and present future work improvements.Markerless body tracking technologies based on depth sensors allowed researchers to have an easy-to-use platform for developing algorithms for recognizing full body gestures and postures in real time [1,2]. Recently, researchers are increas-M. Obaid (B) t2i Lab,

This paper presents a study that compares a humanoid robotic tutor to a human tutor when instructing school children to build a LEGO house. A total of 27 students, between the ages of 11-15, divided into two groups, participated in the study and data were collected to investigate the participants' success rate, requests for help, engagement, and attitude change toward robots following the experiment. The results reveal that both groups are equally successful in executing the task. However, students ask the human tutor more often for help, while students working with the robotic tutor are more eager to perform well on the task. Finally, all students get a more positive attitude toward a robotic tutor following the experiment, but those in the robot condition change their attitude somewhat more for certain questions, illustrating the importance of real interaction experiences prior to eliciting students' attitudes toward robots. The paper concludes that students do follow instructions from a robotic tutor but that more long-term interaction is necessary to study lasting effects.

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