Individuals perceive phantom costs, such as ulterior motives and risks, when a person makes an unreasonably generous offer without sufficient explanation. Prior research relying exclusively on the Nao robot found similar effects, though smaller than with humans. To better understand these differences, we manipulated agent human-likeness across five robots and a human. Participants read a vignette in which the agent either offered a free parking spot (reasonable) or added an unjustified $10 incentive (unreasonably generous). They then decided whether to accept the offer, explained why they thought the agent made this offer, and rated the agent's anthropomorphism and perceived phantom costs. Results showed that unreasonably generous offers prompted participants to attribute more mind to the agents, increasing perceived phantom costs. Anthropomorphism also influenced phantom costs: Animacy and Disturbance increased them, while Intentionality and Sociability decreased them. This study advances our knowledge of phantom costs in HRI, suggesting that people adopt the intentional stance to explain a robot's behaviour—especially when it deviates from social norms—highlighting the need for careful anthropomorphic design of social robots to minimize phantom costs perception.

Recent studies found that people imagine phantom costs—bad intentions and risks—when a human or a robot makes an overly generous offer without sufficient explanations. However, these studies used a paradigm in which the agent justified a cookie with $2 offer by saying they had eaten cookies with friends, a scenario we think implausible for robots. The present study replicated this paradigm while measuring perceived plausibility of the agent's justification. Results indicated that, unlike humans, the justification of eating cookies with friends was perceived as implausible when said by a robot. This perceived implausibility increased perceived phantom costs, reduced trust in the robot, and decreased offer acceptance. This study suggests that phantom costs occur when explanations are both insufficient and implausible, highlighting the need for sufficient and plausible explanations to promote effective HRI.

A recent psychology study found that people are more likely to reject an overly generous offer from an agent (human or robot) when there is no clear justification. This “money backfire effect” is said to stem from perceived phantom costs (e.g., agent's bad intentions). However, their online version provided less clear results. To test its robustness, we conducted a high-powered direct replication study aiming to know whether money backfiring and phantom costs effects occur in human-robot interaction. A human or robot offered participants a cookie with or without money. Participants had to decide whether they would eat the cookie and answer some questions regarding the scenario. Results indicated that participants perceived more phantom costs when the offer included money, negatively influencing their decisions to eat the cookie. However, participant's likelihood to eat the cookie did not differ as a function of agent and offer. This study provides new insights regarding the validity of online studies. We advise scholars to adapt their study setting regarding the effect they want to explore.

The constantly rising demand for human-like conversational agents and the accelerated development of natural language processing technology raise expectations for a breakthrough in intelligent machine research and development. However, measuring intelligence is impossible without a proper test. Alan Turing proposed a test for machine intelligence based on imitation and unconstrained conversations between a machine and a human. To the best of our knowledge, no one has ever conducted Turing’s test as Turing prescribed, even though the Turing Test has been a bone of contention for more than seventy years. Conducting a bona fide Turing Test will contribute to machine intelligence evaluation research and has the potential to advance AI researchers in their ultimate quest, developing an intelligent machine.

Robots that are capable of outperforming human beings on mental and physical tasks provoke perceptions of threat. In this article we propose that implicit self-theory (core beliefs about the malleability of self-attributes, such as intelligence) is a determinant of whether one person experiences threat perception to a greater degree than another. We test for this possibility in a novel experiment in which participants watched a video of an apparently autonomous intelligent robot defeating human quiz players in a general knowledge game. Following the video, participants received either social comparison feedback, improvement-oriented feedback, or no feedback, and were then given the opportunity to play against the robot. We show that those who adopt a malleable self-theory (incremental theorists) are more likely to play against a robot after imagining losing to it, as well as exhibit more favorable responses and less identity threats than entity theorists (those adopting a fixed self-theory). Moreover, entity theorists (vs. incremental theorists) perceive autonomous intelligent robots to be significantly more threatening (both in terms of realistic and identity threats). These findings offer novel theoretical and practical implications, in addition to enriching the HRI literature by demonstrating that implicit self-theory is, in fact, an influential variable underpinning perceived threat.

Previous studies showed that using the 'shooter bias' paradigm, people demonstrate a similar racial bias toward dark colored robots over light colored robots (i.e., Black vs. White) as they do toward humans of similar skin tones [3]. However, such an effect could be argued to be the result of social priming. Additionally, it raises the question of how people might respond to robots that are in the middle of the color spectrum (i.e., brown) and whether such effects are moderated by the perceived anthropomorphism of the robots. We conducted two experiments to first examine whether shooter bias tendencies shown towards robots is driven by social priming, and then whether diversification of robot color and level of anthropomorphism influenced shooter bias. Our results showed that shooter bias was not influenced by social priming, and interestingly, introducing a new color of robot removed shooter bias tendencies entirely. However, varying the anthropomorphism of the robots did not moderate the level of shooter bias, and contrary to our expectations, the robots were not perceived by the participants as having different levels of anthropomorphism.

Emotions play an important role in human-agent interaction. To realise natural interaction it is essential for an agent to be able to analyse the sentiment in users' utterances. Modern agents use a distributed service model in which their functions can be located on any number of computers including cloud-based servers. Outsourcing the speech recognition and sentiment analysis to a cloud service enables even simple agents to adapt their behaviour to the emotional state of their users. In this study we test whether sentiment analysis tools can accurately gauge sentiment in human-chatbot interaction. To that effect, we compare the quality of sentiment analysis obtained from three major suppliers of cloud-based sentiment analysis services (Microsoft, Amazon and Google). In addition, we compare their results with the leading lexicon-based software, as well as with human ratings. The results show that although the sentiment analysis tools agree moderately with each other, they do not correlate well with human ratings. While the cloud-based services would be an extremely useful tool for human-agent interaction, their current quality does not justify their usage in human-agent conversations.

Most robots currently being sold or developed are either stylized with white material or have a metallic appearance. In this research we used the shooter bias paradigm and several questionnaires to investigate if people automatically identify robots as being racialized, such that we might say that some robots are 'White' while others are 'Asian', or 'Black'. To do so, we conducted an extended replication of the classic social psychological shooter bias paradigm using robot stimuli to explore whether effects known from human-human intergroup experiments would generalize to robots that were racialized as Black and White. Reaction-time based measures revealed that participants demonstrated 'shooter-bias' toward both Black people and robot racialized as Black. Participants were also willing to attribute a race to the robots depending on their racialization and demonstrated a high degree of inter-subject agreement when it came to these attributions.

Humans recognise and respond to robots as social agents, to such extent that they occasionally attempt to bully a robot. The current paper investigates whether aggressive behaviour directed towards robots is influenced by the same social processes that guide human bullying behaviour. More specifically, it measured the effects of dehumanisation primes and anthropomorphic qualities of the robot on participants» verbal abuse of a virtual robotic agents. Contrary to previous findings in human-human interaction, priming participants with power did not result in less mind attribution. However, evidence for dehumanisation was still found, as the less mind participants attributed to the robot, the more aggressive responses they gave. In the main study this effect was moderated by the manipulations of power and robot anthropomorphism; the low anthropomorphic robot in the power prime condition endured significantly less abuse, and mind attribution remained a significant predictor for verbal aggression in all conditions save the low anthropomorphic robot with no prime. It is concluded that dehumanisation occurs in human-robot interaction and that like in human-human interaction, it is linked to aggressive behaviour. Moreover, it is argued that this dehumanisation is different from anthropomorphism as well as human-human dehumanisation, since anthropomorphism itself did not predict aggressive behaviour and dehumanisation of robots was not influenced by primes that have been established in human-human dehumanisation research.

In this study, we set out to ask three questions. First, does lexical entrainment with a robot interlocutor persist after an interaction? Second, how does the influence of social robots on humans compare with the influence of humans on each other? Finally, what role is played by personality traits in lexical entrainment to robots, and how does this compare with the role of personality in entrainment to other humans? Our experiment shows that first, robots can indeed prompt lexical entrainment that persists after an interaction is over. This finding is interesting since it demonstrates that speakers can be linguistically influenced by a robot, in a way that is not merely motivated by a desire to be understood. Second, we find similarities between lexical entrainment to the robot peer and lexical entrainment to a human peer, although the effects are stronger when the peer is human. Third, we find that whether the peer is a robot or a human, similar personality traits contribute to lexical entrainment. In both peer conditions, participants who score higher on ``Openness to experience" are more likely to adopt less conventional terminology.

Using game balancing techniques can provide the right level of challenge and hence enhance player engagement for sport players with different skill levels. Digital technology can support and enhance balancing techniques in sports, for example, by adjusting players' level of intensity based on their heart rate. However, there is limited knowledge on how to design such balancing and its impact on the user experience. To address this we created two novel balancing techniques enabled by digitally augmenting a table tennis table. We adjusted the more skilled player's performance by inducing two different styles of play and studied the effects on game balancing and player engagement. We showed that by altering the more skilled player's performance we can balance the game through: (i) encouraging game mistakes, and (ii) changing the style of play to one that is easier for the opponent to counteract. We outline the advantages and disadvantages of each approach, extending our understanding of game balancing design. We also show that digitally augmenting sports offers opportunities for novel balancing techniques while facilitating engaging experiences, guiding those interested in HCI and sports.

Reciprocity is a cornerstone of human relationships and apparently it also appears in human-robot interaction independently of the context. It is expected that reciprocity will play a principal role in HRI in the future. The negative side of reciprocal phenomena has not been entirely explored in human-robot interaction. For instance, a reciprocal act such as bribery between Humans and robots is a very novel area. In this paper, we try to evaluate the questions: Can a robot bribe a human? To what extent is a robot bribing a human affect his/her reciprocal response? We performed an experiment using the Rock, Paper, Scissors game (RPSG). The robot bribes the participant by losing intentionally in certain rounds to obtain his/her favour later, and through using direct and indirect speech in certain rounds. The participants could obtain between 20%- 25% more money when the robot bribed them than in the control condition. The robot also used either direct or indirect speech requesting a favour in a second task. Our results show that the bribing robot received significantly less reciprocation than in the control condition regardless of whether the request was couched in direct or indirect speech. However there is a significant interaction effect between the bribe and speech conditions. Moreover, just three of sixty participants reported the robot-bribe in an interview as a malfunction, though they did not mention any moral judgement about its behaviour. Further, just 10% of the participants reported the bribe in the online questionnaire. We consider that our experiment makes an early contribution to continue the exploration of morally ambiguous and controversial reciprocal situations in HRI. Robot designers should consider the reciprocal human response towards robots in different contexts including bribery scenarios. Additionally our study could be used in guidelines for robot behavioural design to model future HRI interactions in terms of moral decisions.

In this paper we present a study that investigates human-robot interpersonal distances and the influence of posture, either sitting or standing on the interpersonal distances. The study is based on a human approaching a robot and a robot approaching a human, in which the human/robot maintain either a sitting or standing posture while being approached. We collected and analysed data from twenty-two participants and the results revealed that robot posture has a significant impact on the interpersonal distances in human-robot interactions. Previous interactions with a robot, and lower negative attitudes towards robots also impacted interpersonal distances. Although the effects of gender, height and age did not yield significant results, we discuss their influence on the interpersonal distances between humans and robots and how they are of interest for future research. We present design implications for human-robot interaction research and humanoid robot design.

The present research examines how a robot's physical anthropomorphism interacts with perceived ability of robots to impact the level of realistic and identity threat that people perceive from robots and how it affects their support for robotics research. Experimental data revealed that participants perceived robots to be significantly more threatening to humans after watching a video of an android that could allegedly outperform humans on various physical and mental tasks relative to a humanoid robot that could do the same. However, when participants were not provided with information about a new generation of robots' ability relative to humans, then no significant differences were found in perceived threat following exposure to either the android or humanoid robots. Similarly, participants also expressed less support for robotics research after seeing an android relative to a humanoid robot outperform humans. However, when provided with no information about robots' ability relative to humans, then participants showed marginally decreased support for robotics research following exposure to the humanoid relative to the android robot. Taken together, these findings suggest that very humanlike robots can not only be perceived as a realistic threat to human jobs, safety, and resources, but can also be seen as a threat to human identity and uniqueness, especially if such robots also outperform humans. We also demonstrate the potential downside of such robots to the public's willingness to support and fund robotics research.

HRI is a multi-disciplinary research field and integrating the range of expertise into a single project can be challenging. Enabling experts on human behavior to design fluent animations and behaviors for advanced robots is problematic, since the tools available for such robots are often in their prototype stage. We have built The Robot Engine (TRE) based on the Unity 3D Game Engine to control robots with Unity 3D. Unity 3D allows non-programmers to use a set of powerful animation and interaction design tools to visually program and animate robots. We review several animation techniques that are common in computer games and that could make the movements of robots more natural and convincing. We demonstrate the use of TRE with two different Arduino based robot platforms and believe that it can easily be extended for use with other robots. We further believe that this unconventional integration of technologies has the potential to fully bring the expertise of interaction designers into the process of advanced human-robot interaction projects.

Standardized metrics for assessing the success of robots is a necessity for a research field to compare and validate results. The Godspeed Questionnaire Series (GQS) is one of the most frequently used questionnaires in the field of Human-Robot Interaction (HRI) with over 160 citations as of October 2014. In this paper, we present a meta analysis of studies that used the GQS. The HRI community uses a large variety of robotic platforms and only the NAO robot seems to be used by multiple research groups. A qualitative meta analysis of 18 NAO studies reveals accumulated findings on perceived intelligence, likability, and anthropomorphism, but also reveals contradictions on how the robot's behaviour and task context impact GQS ratings. The paper closes with a reflection on how added value of data analysis and presentation could be achieved for the HRI community in future.

Empathy plays an important role in the interaction between humans and robots. The contagious effect of yawning is moderated by the degree of social closeness and empathy. We propose to analyse the contagion of yawns as an indicator for empathy. We conducted pilot studies to test different experimental procedures for this purpose. We hope to be able to report on experimental results in the near future.

In HRI anthropomorphism has been considered to be a uni-dimensional construct. However, social psychological studies of the potentially reverse process to anthropomorphisation - known as dehumanization - indicate that there are two distinct senses of humanness with different consequences for people who are dehumanized by deprivation of some of the aspects of these dimensions. These attributes are crucial for perception of others as humans. Therefore, we hypothesized that the same attributes could be used to anthropomorphize a robot in HRI and only a two-dimensional measures would be suitable to distinguish between different forms of making a robot more humanlike. In a study where participants played a quiz based on the TV show ``Jeopardy!" we manipulated a NAO robot's intelligence and emotionality. The results suggest that only emotionality, not intelligence, makes robots be perceived as more humanlike. Furthermore, we found some evidence that anthropomorphism is a multi-dimensional phenomenon.

The question put forward in this paper is whether robots can create conformity by means of group pressure. We recreate and expand on a classic social psychology experiment by Solomon Asch, so as to explore three main dimensions. First, we wanted to know whether robots can prompt conformity in human subjects, and whether there is a significant difference between the degree to which individuals conform to a group of robots as opposed to a group of humans. Secondly we ask whether group pressure (from human or robot peers) can exert influence in verbal judgments, analogously to the influence on visual judgments that is known from previous research [3], [2]. Thirdly, we investigate whether the level of conformity differs between an ambiguous situation and a non-ambiguous situation. Our results show that in both visual and verbal tasks, participants exhibit conformity with human peers, but not with robot peers. The social influence of robot peers is not a significant predictor of verbal or visual judgments in our tasks. Furthermore, the level of conformity is significantly higher in an ambiguous (unclear) situation.

Speech Recognition has not fully permeated in our interaction with devices. Therefore we advocate a speech recognition friendly artificial language (ROILA) that initially was shown to outperform English, however under constraints. ROILA is intended to be used to talk to robots and therefore in this paper we present an experimental study where the recognition of ROILA is compared to English when speech is input using a robot's microphones and both when the robot's head is moving and stationary. Our results show that there was no significant difference between ROILA and English but that the type of microphone and robot's head movement had a significant effect. In conclusion we suggest implications for Human Robot (Speech) Interaction.

In our research we present a speech recognition friendly artificial language that is specially designed and implemented for humans to talk to robots. We call this language Robot Interaction Language (ROILA). In this paper, we describe our current work with ROILA that utilizes the Nao humanoid robot. Our current demo implementation will allow users to interact with the Nao robot without the usage of any external laptops or microphones. Therefore the purpose of our demo is two-fold: 1) to demonstrate ``live" that ROILA has improved recognition accuracy over English and 2) to demonstrate that users can interact with the Nao robot in ROILA without the use of any external devices.

Tactile interaction is at the heart of human-robot relationships. The physical presence of the robot is what distinguishes human-robot interaction from human-virtual agent interaction. Although first attempts for tactile human-robot interaction have been made, robots are still far inferior to the abilities of humans in providing tactile experiences. This study investigates if a head massage given by others is more pleasurable than a massage given oneself and if a simple robot is significantly worse in giving a head massage. Furthermore we investigate if the physical experience with a robot changes the participants' attitudes towards robots. We ran a within-participants experiment with 18 subjects. The results show that receiving a massage by a human masseur is significantly more enjoyable than receiving it by a robot or by oneself. However, the participants displayed significantly more facial expressions of happiness in the robot condition. The participants did not significantly change their attitudes towards robots due their experience with the robot. We conclude that a robotic massage is a promising field for human robot interaction for further investigation.

This paper presents a study on the effects of using virtual worlds as medium for interaction with telepresence robots. The Prototype for Assisted Communication PAC4 is also introduced. This system connects virtual worlds with real robots, allowing virtual world users to access the robot capabilities in a telepresence scenario. A user experiment was conducted to evaluate the effects of PAC4 on the feeling of social presence experienced by virtual world users.

In this video, "Nao Haka", four robots and a haka leader perform a traditional Maori Haka. The Haka leader, who performs the main actions is supported by Aldebaran Nao Robots, which are controlled by an external performer, using a Microsoft Kinect as the input device. This device allows for full-body user tracking. This Video was made as a supportive gesture towards the All Blacks Rugby World Cup Campaign 2011.

This paper presents our approach to using semantic technologies to describe robot embodiments. We introduce a prototype implementation of RoboDB, a robot database based on semantic web technologies with the functionality necessary to store meaningful information about the robot's body structure. We present a heuristic evaluation of the user interface to the system, and discuss the possibilities of using the semantic information gathered in the database for applications like building a robot ontology, and the development of robot middleware systems.

Teaching is inherently a social interaction between teacher and student. Despite this knowledge, many educational tools, such as vocabulary training programs, still model the interaction in a tutoring scenario as unidirectional knowledge transfer rather than a social dialog. Therefore, ongoing research aims to develop virtual agents as more appropriate media in education. Virtual agents can induce the perception of a life-like social interaction partner that communicates through natural modalities such as speech, gestures and emotional expressions. This effect can be additionally enhanced with a physical robotic embodiment.

This essay investigates the situation of young researchers in the HRI community. I argue that we need to have a more child friendly environment to encourage young staff members to create children.

Nonverbal behaviors serve as a rich source of information in inter human communication. In particular, motion cues can reveal details on a person's current physical and mental state. Research has shown, that people do not only interpret motion cues of humans in these terms, but also the motion of animals and inanimate devices such as robots. In order to successfully integrate mobile robots in domestic environments, designers have therefore to take into account how the device will be perceived by the user.

Alan Turing proposed a test for the intelligence of machines in 1950 [1]. Despite great efforts, no computer has passed this test so far. Each year, chat bots compete for the Loebner Prize, the first formal instantiation of a Turing Test. No contender was able to fool the jury yet. Major problems of the chat bots are the lack of common knowledge and the logical consistency of a dialogue.

The productivity of scientists and the quality of their papers differ enormously. Still, all papers written get published eventually and the impact factor of the publication channel is not correlated to the citations that individual papers receive. Hence it does not matter where to publish papers. Based on these two conjectures, I conclude that all papers should be published. The review process should focus on feedback that helps authors to improve their manuscripts. But we should no longer waste effort to a selection procedure. This All-In policy would decrease the number of published papers and would refocus the attention of the authors on the quality of their papers and not their quantity.

The possibility of improving speech recognition accuracy within human computer and robot interaction by having users interact in a constrained natural language or an artificial language has been explored. However, what has not been evaluated yet is the user acceptance of such forms of interaction. In this paper we discuss two separate but similar studies which were aimed at assessing the usability of constrained and artificial languages in contrast to natural languages. The interaction context was implemented in a game played between children and the iCat robot. We subjectively measured various variables related to gaming experience and interacting in the new languages. Our results reveal that there were no significant differences in the user experience across the two interaction mediums in comparison to natural languages.

The CHI conference has grown rapidly over the last 26 years. We present a quantitative analysis on the countries and organizations that contribute to its success. Only 7.8 percent of the countries are responsible for 80 percent of the papers in the CHI proceedings, and the USA is clearly the country with most papers. But the success of a country or organization does not depend only on the number of accepted papers, but also on their quality. We present a ranking of countries and organizations based on the h index, an indicator that tries to balance the quantity and quality of scientific output based on a bibliometric analysis. The bibliometric analysis also allowed us to demonstrate the difficulty of judging quality. The papers acknowledged by the best paper award committee were not cited more often than a random sample of papers from the same years. The merit of the award is therefore unclear, and it might be worthwhile to allow the visitor to the conference to vote for the best paper.

Autonomous robots tend to induce the perception of a personality through their behavior and appearance. It has been suggested that the personality of a robot can be used as a design guideline and as a mental model of the robot. We propose a method to design and evaluate personality and expressions for domestic robots.

The Uncanny Valley hypothesis has been widely used in the areas of computer graphics and human-robot interaction to motivate research and to explain the negative impressions that participants report after exposure to highly realistic characters or robots. Despite its frequent use, empirical proof for the hypothesis remains scarce. This study empirically tested two predictions of the hypothesis: a) highly realistic robots are liked less than real humans and b) the highly realistic robot's movement decreases its likeability. The results do not support these hypotheses and hence expose a considerable weakness in the Uncanny Valley hypothesis. Anthropomorphism and likeability may be multi-dimensional constructs that cannot be projected into a two-dimensional space. We speculate that the hypothesis' popularity may stem from the explanatory escape route it offers to the developers of characters and robots. In any case, the Uncanny Valley hypothesis should no longer be used to hold back the development of highly realistic androids.

The first robotic copies of real humans have become available. They enable their users to be physically present in multiple locations at the same time. This study investigates what influence the embodiment of an agent has on its persuasiveness and its perceived personality. Is a robotic copy as persuasive as its human counterpart? Does it have the same personality? We performed an experiment in which the embodiment of the agent was the independent variable and the persuasiveness and perceived personality were the dependent measurement. The persuasive agent advertised a Bluetooth headset. The results show that an android is found to be as persuasive as a real human or a video recording of a real human. The personality of the participant had a considerable influence on the measurements. Participants that were more open to new experiences rated the persuasive agent lower on agreeableness and extroversion. They were also more willing to spend money on the advertised product.

Following the great success of the first video session at the HRI2007 conference (Bartneck & Kanda, 2007), the Human Robot Interaction 2008 conference hosted the second video session, in which movies of interesting, important, illustrative, or humorous HRI research moments were shown. Robots and humans do not always behave as expected and the results can be entertaining and even enlightening -- therefore instances of failures have also been considered in the video session. Besides the importance of the lessons learned and the novelty of the situation, the videos have also an entertaining value. The video session had the character of a design competition.

This study investigates to what degree the userspsila cultural background influences their perception of a robotpsilas anthropomorphism and likeability. More specifically, robots with a conventional robot-like appearance were compared to highly anthropomorphic androids. The US American participants like the robots on average more than the Japanese participants do, but a strong interaction effect was observed between the participantspsila cultural background and the type of robot. The Japanese participants had a strong preference for conventional robots. This confirms the stereotype that Japanese like conventional robots. However, this does not hold true for highly anthropomorphic androids, which they liked less than the US American participants did. This study focused on the perception of static images of robots and the results may be different for the perception of movies of moving robots or, to an even greater extent, the perception when standing right in front of a moving robot.

Robots can exhibit life like behavior, but are according to traditional definitions not alive. Current robot users are confronted with an ambiguous entity and it is important to understand the users perception of these robots. This study analyses if a robot's intelligence and its agreeableness influence its perceived animacy. The robot's animacy was measured, amongst other measurements, by the users' hesitation to switch it off. The results show that participants hesitated three times as long to switch off an agreeable and intelligent robot as compared to a non agreeable and unintelligent robot. The robots' intelligence had a significant influence on its perceived animacy. Our results suggest that interactive robots should be intelligent and exhibit an agreeable attitude to maximize its perceived animacy.

The Human Robot Interaction 2007 conference hosted a video session, in which movies of interesting, important, illustrative, or humorous HRI research moments are shown. This paper summarizes the abstracts of the presented videos. Robots and humans do not always behave as expected and the results can be entertaining and even enlightening - therefore instances of failures have also been considered in the video session. Besides the importance of the lessons learned and the novelty of the situation, the videos have also an entertaining value.

Robots are being introduced in our society but their social status is still unclear. A critical issue is if the robot's exhibition of intelligent life-like behavior leads to the users' perception of animacy. The ultimate test for the life-likeness of a robot is to kill it. We therefore conducted an experiment in which the robot's intelligence and the participants' gender were the independent variables and the users' destructive behavior of the robot the dependent variables. Several practical and methodological problems compromised the acquired data, but we can conclude that the robot's intelligence had a significant influence on the users' destructive behavior. We discuss the encountered problems and the possible application of this animacy measuring method.

The uncanny valley theory proposed by Mori in 1970 has been a hot topic in human robot interaction research, in particular since the development of increasingly human-like androids and computer graphics. In this paper we describe an empirical study that attempts to plot Mori's hypothesized curve. In addition, the influence of framing on the users' perception of the stimuli was investigated. Framing had no significant influence on the measurements. The pictures of robots and humans were rated independently of whether the participants knew a particular picture showed a robot or human. Anthropomorphism had a significant influence on the measurements, but not even pictures of real humans were rated as likeable as the pictures of humanoids or toy robots. As a result we suggest the existence of an uncanny cliff model as an alternative to the uncanny valley model. However, this study focused on the perception of pictures of robots and the results, including the suggested model, may be different for the perception of movies of moving robots or the perception of standing right in front of a moving robot.

The development of interactive robots is a complicated process, involving a plethora of psychological, technical, and contextual influences. To design a robot capable of operating ldquointelligentlyrdquo in everyday situations, one needs a profound understanding of human-robot interaction (HRI). We propose an approach based on integral analysis of multimodal data to pursue this understanding and support interdisciplinary research and development in the field of robotics. To adopt this approach, a software tool named interaction debugger was developed that features user-friendly navigation, browsing, searching, viewing, and annotation of data; it enables fine-grained inspection of the HRI. In four case studies, we demonstrated how our analysis approach aids the development process of interactive robots.

Along with the development of interactive robots, controlled experiments and field trials are regularly conducted to stage human-robot interaction. Experience in this field has shown that analyzing human-robot interaction for evaluation purposes fosters the development of improved systems and the generation of new knowledge. In this paper, we present the interaction debugging approach, which is based on the collection and analysis of data from robots and their environment. Considering the multimodality of robotic technology, often only audio and video are insufficient for detailed analysis of human-robot interaction. Therefore, in our analysis we integrate multimodal information using audio, video, sensory data, and intermediate variables. An important aspect of the interaction debugging approach is using a tool called Interaction Debugger to analyze data. By supporting user-friendly data presentation, annotation and navigation, Interaction Debugger enables fine-grained inspection of human-robot interaction. The main goal of this paper is to address how an integral approach to the analysis of human-robot interaction can be adopted. This is demonstrated by three case studies.

Robots, specifically androids, become increasingly important in the consumer market where they are marketed as toys or companions, as well as in the industry, where they will increasingly often play the role of a co-worker. The developers in various robotics communities are divided about design issues in these companion-worker androids. While some robot developers believe people will work more effectively with humanoid robots in the role of companion or co-worker because of a more natural interaction, others think it's necessary to maintain a machine-like interface to avoid distractions. Consequently, the ability of humans to coordinate and interact with robots, and human perceptions and actions based on varying levels of humanlike robot interfaces are of great interest. This paper presents preliminary results from a study that investigated how people use praise and punishment in a collaborative game scenario. Subjects played a game together with humans, computers, and anthropomorphic and zoomorphic robots. They could give plus points and minus points as praise and punishment for correct or wrong partner answers. Results show that praise and punishment were used the same way for computer and human partners. Yet robots, which are essentially computers with an embodiment, were treated differently. Very machinelike robots were treated just like the computer and the human; robots very high on anthropomorphism/zoomorphism were praised more and punished less

This study reports on an experiment in which participants had to collaborate with either another human or a robot (partner). The robot would either be present in the room or only be represented on the participants' computer screen (presence). Furthermore, the participants' partner would either make 20 % errors or 40 % errors (error rate). We automatically measured the praising and punishing behavior of the participants towards their partners and also asked the participant to estimate their own behavior. The participants unconsciously praised Aibo more than the human partner, but punished it just as much. Robots that adapt to the users' behavior should therefore pay extra attention to the users' praises, compared to their punishments

Listening to music while travelling is a pleasant activity. The latest MP3 players demonstrate that storage and management of music will not be a problem in the near future. Besides listening to music the user might also want to gather new music from the Internet. We propose a music gathering application that helps the user to collect music and that is able to proactively search and download music based on the users music preferences. Furthermore, we developed an emotional interface character that provides instant and natural feedback on the status of the application