Hiroshi Ishiguro

In this paper, we move toward offering psychological benchmarks to measure success in building increasingly humanlike robots. By psychological benchmarks we mean categories of interaction that capture conceptually fundamental aspects of human life, specified abstractly enough to resist their identity as a mere psychological instrument, but capable of being translated into testable empirical propositions. Nine possible benchmarks are considered: autonomy, imitation, intrinsic moral value, moral accountability, privacy, reciprocity, conventionality, creativity, and authenticity of relation. Finally, we discuss how getting the right group of benchmarks in human–robot interaction will, in future years, help inform on the foundational question of what constitutes essential features of being human.

In this paper, we move toward offering psychological benchmarks to measure success in building increasingly humanlike robots. By psychological benchmarks we mean categories of interaction that capture conceptually fundamental aspects of human life, specified abstractly enough to resist their identity as a mere psychological instrument, but capable of being translated into testable empirical propositions. Nine possible benchmarks are considered: autonomy, imitation, intrinsic moral value, moral accountability, privacy, reciprocity, conventionality, creativity, and authenticity of relation. Finally, we discuss how getting the right group of benchmarks in human–robot interaction will, in future years, help inform on the foundational question of what constitutes essential features of being human.

In 2008 the authors held Involving Interface, a lively interdisciplinary event focusing on issues of biological, sociocultural, and technological interfacing (see Acknowledgments). Inspired by discussions at this event, in this article, we further discuss the value of input from neuroscience for developing robots and machine interfaces, and the value of philosophy, the humanities, and the arts for identifying persistent links between human interfacing and broader ethical concerns. The importance of ongoing interdisciplinary debate and public communication on scientific and technical advances is also highlighted. Throughout, the authors explore the implications of the extended mind hypothesis for notions of moral accountability and robotics.

Enhancing synchronization among people when synchronization is lacking is believed to improve their social skills, learning processes, and proficiency in musical rhythmic development. Greater synchronization among people can be induced to improve the rhythmic interaction of a system with multiple dancing robots that dance to a drum beat. A series of experiments were conducted to examine the human–human synchrony between persons that participated in musical sessions with robots. In this study, we evaluated: (a) the effect of the number of robots on a subject’s ability to synchronize with an experimenter; (b) the effect of the type of robot synchrony, namely, whether the robots did or did not represent the subject’s rhythm; (c) the effect of an in-sync and out-of-sync robot on a subject’s behavior. We found that: (a) three robots increased the level of synchronization between the subject and experimenter and their enjoyment level; (b) robots may induce greater synchronization between the subject and experimenter by reproducing the rhythms of not only the experimenter but also of the subject compared to when only the experimenter’s rhythms had been reproduced; (c) the robots in-sync had greater influence on the natural rhythm of the subject.

Previous research has shown that although infants follow the gaze direction of robots, robot gaze does not facilitate infants’ learning for objects. The present study examined whether robot gaze affects infants’ object learning when the gaze behavior was accompanied by verbalizations. Twelve-month-old infants were shown videos in which a robot with accompanying verbalizations gazed at an object. The results showed that infants not only followed the robot’s gaze direction but also preferentially attended to the cued object when the ostensive verbal signal was present. Moreover, infants showed enhanced processing of the cued object when ostensive and referential verbal signals were increasingly present. These effects were not observed when mere nonverbal sound stimuli instead of verbalizations were added. Taken together, our findings indicate that robot gaze accompanying verbalizations facilitates infants’ object learning, suggesting that verbalizations are important in the design of robot agents from which infants can learn. Keywords: gaze following; humanoid robot; infant learning; verbalization; cognitive development

This paper reports our research efforts on social robots that recognize interpersonal relationships. These investigations are carried out by observing group behaviors while the robot interacts with people. Our humanoid robot interacts with children by speaking and making various gestures. It identifies individual children by using a wireless tag system, which helps to promote interaction such as the robot calling a child by name. Accordingly, the robot is capable of interacting with many children, causing spontaneous group behavior from the children around it. Here, group behavior is associated with social relationships among the children themselves. For example, a child may be accompanied by his or her friends and then play together with them. We propose the hypothesis that our interactive robot prompts a child’s friends to accompany him or her; thus, we can estimate their friendship by simply observing their accompanying behaviors. We conducted a field experiment for two weeks in a Japanese elementary school to verify this hypothesis. In the experiment, two “Robovie” robots were placed where children could freely interact with them during recesses. As a result, we found that they mostly prompted friend-accompanying behavior. Moreover, we could estimate some of their friendly relationships, in particular among the children who often appeared around the robot. For example, we could estimate 5% of all friendships with 80% accuracy, and 15% of them with nearly 50% accuracy. Thus, this result basically supports our hypothesis on friendship estimation from an interactive humanoid robot. We believe that this ability to estimate human relationships is essential for robots to behave socially.

This paper reports the results of questionnaire-based research conducted at an exhibition of interactive humanoid robots that was held at the Osaka Science Museum, Japan. The aim of this exhibition was to investigate the feasibility of communication robots connected to a ubiquitous sensor network, under the assumption that these robots will be practically used in daily life in the not-so-distant future. More than 90,000 people visited the exhibition. A questionnaire was given to the visitors to explore their opinions of the robots. Statistical analysis was done on the data of 2,301 respondents. It was found that the visitors’ opinions varied according to age; younger visitors did not necessarily like the robots more than elderly visitors; positive evaluation of the robots did not necessarily conflict with negative evaluations such as anxiety; there was no gender difference; and there was almost no correlation between opinions and the length of time spent near the robots

Young children generally learn words from other people. Recent research has shown that children can learn new actions and skills from nonhuman agents. This study examines whether young children could learn words from a robot. Preschool children were shown a video in which either a woman or a mechanical robot labeled novel objects. Then the children were asked to select the objects according to the names used in the video. The results revealed that children in the human condition were more likely to select the correct objects than those in the robot condition. Nevertheless, the five-year-old children in the robot condition performed significantly better than chance level, while the four-year olds did not. Thus there is a developmental difference in children’s potential to learn words from a robot. The results contribute to our understanding of how children interact with non-human agents. Keywords: developmental cybernetics; word learning; social cognition; cognitive development

We present a nonparametric approach to prediction of the n-back n \in {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby realizing the potential that they can offer for their detection in real world scenarios (e.g., difficulty of a conversation). Our approach takes advantage of intrinsic linearity that is inherent in the components of the NIRS time series to adopt a one step regression strategy. We demonstrate the correctness of our approach through its mathematical analysis. Furthermore, we study the performance of our model in an inter-subject setting in contrast with state-of-the-art techniques in the literature to show a significant improvement on prediction of these tasks (82.50% and 86.40% for female and male participants, respectively). Moreover, our empirical analysis suggest a gender difference effect on the performance of the classifiers (with male data exhibiting a higher non-linearity) along with the left-lateralized activation in both genders with higher specificity in females.

Compared with other communication media such as cellphones and video chat, teleoperated robots have a physical existence which increases the feeling of copresence. However, the appearance of a teleoperated robot is always the same regardless of the characteristics of its operator. Since people can determine their partner’s personality from his/her appearance, a teleoperated robot’s appearance might construct a personality that confuses the user. Our research focuses on establishing what kind of appearance of the telecommunication media could prevent confusion and increase the feeling of copresence. In this study, we compare the appearance of three types of communication media. The result shows that, in the case of the human-like robot, the consistency of the personality judgment is better than in the case of the nonhuman-like robot. Also, we found that teleoperated robots transmit a more appropriate context-based atmosphere, while the video chat transmits more nonverbal information, such as facial expressions.

To let humanoid robots behave socially adequate in a future society, we started to explore laughter as an important para-verbal signal known to influence relationships among humans rather easily. We investigated how the naturalness of various types of laughter in combination with different humanoid robots was judged, first, within a situational context that is suitable for laughter and, second, without describing the situational context. Given the variety of human laughter, do people prefer a certain style for a robot’s laughter? And if yes, how does a robot’s outer appearance affect this preference, if at all? Is this preference independent of the observer’s cultural background? Those participants, who took part in two separate online surveys and were told that the robots would laugh in response to a joke, preferred one type of laughter regardless of the robot type. This result is contrasted by a detailed analysis of two more surveys, which took place during presentations at a Japanese and a German high school, respectively. From the results of these two surveys, interesting intercultural differences in the perceived naturalness of our laughing humanoids can be derived and challenging questions arise that are to be addressed in future research

The development of robots that closely resemble human beings can contribute to cognitive research. An android provides an experimental apparatus that has the potential to be controlled more precisely than any human actor. However, preliminary results indicate that only very humanlike devices can elicit the broad range of responses that people typically direct toward each other. Conversely, to build androids capable of emulating human behavior, it is necessary to investigate social activity in detail and to develop models of the cognitive mechanisms that support this activity. Because of the reciprocal relationship between android development and the exploration of social mechanisms, it is necessary to establish the field of android science. Androids could be a key testing ground for social, cognitive, and neuroscientific theories as well as platform for their eventual unification. Nevertheless, subtle flaws in appearance and movement can be more apparent and eerie in very humanlike robots. This uncanny phenomenon may be symptomatic of entities that elicit our model of human other but do not measure up to it. If so, very humanlike robots may provide the best means of pinpointing what kinds of behavior are perceived as human, since deviations from human norms are more obvious in them than in more mechanical-looking robots. In pursuing this line of inquiry, it is essential to identify the mechanisms involved in evaluations of human likeness. One hypothesis is that, by playing on an innate fear of death, an uncanny robot elicits culturally-supported defense responses for coping with death’s inevitability. An experiment, which borrows from methods used in terror management research, was performed to test this hypothesis. [Thomson Reuters Essential Science Indicators: Fast Breaking Paper in Social Sciences, May 2008]

This paper reports our research efforts on social robots that recognize interpersonal relationships. These investigations are carried out by observing group behaviors while the robot interacts with people. Our humanoid robot interacts with children by speaking and making various gestures. It identifies individual children by using a wireless tag system, which helps to promote interaction such as the robot calling a child by name. Accordingly, the robot is capable of interacting with many children, causing spontaneous group behavior from the children around it. Here, group behavior is associated with social relationships among the children themselves. For example, a child may be accompanied by his or her friends and then play together with them. We propose the hypothesis that our interactive robot prompts a child’s friends to accompany him or her; thus, we can estimate their friendship by simply observing their accompanying behaviors. We conducted a field experiment for two weeks in a Japanese elementary school to verify this hypothesis. In the experiment, two “Robovie” robots were placed where children could freely interact with them during recesses. As a result, we found that they mostly prompted friend-accompanying behavior. Moreover, we could estimate some of their friendly relationships, in particular among the children who often appeared around the robot. For example, we could estimate 5% of all friendships with 80% accuracy, and 15% of them with nearly 50% accuracy. Thus, this result basically supports our hypothesis on friendship estimation from an interactive humanoid robot. We believe that this ability to estimate human relationships is essential for robots to behave socially.