Eduardo Benítez Sandoval

Human Robot Interaction and Fiction: A Contradiction

In this position paper a perspective on how movies and science fiction are currently shaping the design of robots is presented. This analysis includes both behaviour and embodiment in robots. We discuss popular movies that involve robots as characters in their storyline, and how people’s beliefs and expectations are affected by what they see in robot movies. A mismatch or contradiction emerges in what the robots of today can accomplish and what the movies portray. In order to overcome this mismatch we present design implications that may be of benefit to HRI designers.

Reciprocity in Human-Robot Interaction: A Quantitative Approach Through the Prisoner's Dilemma and the Ultimatum Game

Reciprocity is an important factor in human–human interaction, 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 HRI has potential implications for robot design.

A peer pressure experiment: Recreation of the Asch conformity experiment with robots

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.

The robot engine — Making the unity 3D game engine work for HRI

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.

Persistent Lexical Entrainment in HRI

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.

Stop! That is close enough. How body postures influence human-robot proximity

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.

Can a Robot Bribe a Human?: The Measurement of the Negative Side of Reciprocity in Human Robot Interaction

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.

Robot Maori Haka

In this paper we described how robots can be used as cultural preservationists. As part of larger a case study, Nao Robots are programmed to preserve New Zealands most traditional war dance, the Haka.

Using Video Preferences to Understand the Human Perception of Real and Fictional Robots

In this paper the nexus between fictional and real robots in Human Robot Interaction (HRI) is explored. We claim that design guidelines for HRI must not be borrowed from fiction blindly, as contradictions between the two may emerge with respect to what is desired by the human user. To understand human perception of robots appearing in movies we analyse viewing statistics and qualitative comments of a set of YouTube videos comprising of fictional and real robots. Analysis of the viewing statistics showed that real robots are more popular. Furthermore, analysis of the comments showed that two real robots (Nao and Shakey) generated significantly more positive comments and significantly more attributions of usage in human society as compared to the two fictional robots (AstroBoy and HAL9000). Based on the sample of robots considered in this research, our results reveal that contrary to expectation humans are more exposed to real robots and are more preferred, and we conclude by reasserting the contradiction that emerges between real and fictional robots.

Towards a Conversational Corpus for Human-Robot Conversations

Conversational corpora based on human-human dialogues have often been used for training of data-driven dialogue systems. However, human-human conversations might not be the optimal inputs for machine learning training aims used in HRI. This paper suggests the creation of a conversational corpus based on Human-Robot conversations as input for the training of a dialogue system used in future conversational robots. We propose that the significant differences between Human-Human Conversation (HHC) and Human-Robot Conversation (HRC) in terms of used language and other aspects (e.g., humanlikeness, embodiment, etc.) might affect the quality of the responses from a conversational robot. Hence, the use of HRCs as an input could improve the responses of the robots when the conversational machine learning system is trained using a more realistic model of HRI conversations rather than a HHI model. Future applications of conversational robots in education and health care could be enhanced by using an appropriate HRC corpus.