With the rapid advancement of robotics, robots’ helping behaviors are increasingly framed not only as functional assistance but also as prosocially meaningful interaction. In this context, the resource cost borne by the help-provider is a critical factor, yet it has not been systematically explored in existing human-robot interaction (HRI) research. Understanding how humans perceive and respond to different types of helping is essential for building better human–robot relationships. This study addresses this gap through two experiments. Study 1 examined the role of agent resource type (robot’s own resources vs. external resources). Results showed that when robots shared their own resources, participants did not report significant differences in overall attitudes or prosocial behavior, but attributed higher performance trust and expressed stronger feelings of gratitude and guilt. Study 2 further examined the importance of agent resources (robot battery level: high vs. low) . The results showed that even when relative costs were the same, participants tended to perceive sharing from a low-battery robot as more reliable, while variations in resource type or importance did not significantly change social responses. These findings suggest that human evaluations of robots are shaped not only by the outcomes of helping but also by the perceived cost and sacrifice underlying robot actions. Our work offers an initial direction for integrating resource cost considerations into the design of social robots.

Hugging provides a sense of psychological security and is an essential form of support contributing to stress relief. Studies are being conducted to reproduce this effect using robots in human-robot interaction. In this study, we developed a prototype that acquired the user's biometric information using the huggable robot Moffuly-MS and tested its operation. The robot was equipped with a touch sensor, IMU, ultrasonic sensor, and temperature and humidity sensor. We confirmed that it was possible to use these to acquire information on the user's behavior, movements during the hug, and physiological changes. In the future, it is hoped that by improving the system's accuracy and integrating it with dialogue systems, new possibilities will be opened for stress relief and psychological care in the medical and welfare fields.

In this study, we explore how robot self-sacrifice can influence human perceptions and behaviors toward robots. While traditional research in human-robot interaction (HRI) often addresses moral dilemmas, such as the trolley problem, our work examines more relatable scenarios where robots engage in self-sacrificial behavior, such as offering their own battery to charge a user's device instead of relying on external resources. Through an experiment with 30 participants, we found that robots demonstrating self-sacrifice significantly promoted prosocial behaviors compared to robots that did not. However, no significant differences were observed between the groups in terms of the perceptions of robots. These results highlight that while self-sacrificial behavior did not alter perceptions of the robot's social traits, it clearly influenced participants' willingness to engage in prosocial actions. This research underscores the potential of robots to foster prosocial behavior through self-sacrifice, offering valuable insights for designing robots that encourage a flourishing society in which humans and robots coexist.

Therapeutic robots are used in facilities for older people, but many of them can only provide mechanical responses to users. To improve the quality of interactions, it is crucial to address emotional consistency. Thus, we propose Semantic Babbling, a system that enables interaction with users by utilizing the baby robot's “inner voice”. By generating inner voices and selecting babbling based on sentiment, Semantic Babbling aims to mimic infant-like responses. A crowd-sourcing survey demonstrated the significance of emotional consistency and the display of inner voice in Semantic Babbling, revealing that it improves the impression of baby robots.

Although most people interact with social robots in daily environments in a friendly manner, some do not. They sometimes destroy, insult, bully, or abuse them. Uncovering such antisocial attitudes and behaviors is a painful but critical research topic in the process of deploying social robots in real society. In this study, we describe the robot harassment we observed in a three-year exhibition with an android robot. During the exhibition, visitors talked freely with the android robot, which autonomously handled the conversation topics. Many people enjoyed chatting with it in a friendly manner, although a few behaved rudely toward it. A coder labeled 26/971 scenes as “harassment” interactions and organized them into four subcategories: unwelcome flirting, rude attitudes, unsolicited comments about appearance, and inappropriate touching. We introduce these details and discuss possible research topics necessary to reduce such harassment behaviors.

To provide more services on multiple floors in buildings, recent mobile robots have the capability to take elevators and need to board them with other passengers. When a mobile robot boards an elevator, the current technological limitations result in stress for the surrounding passengers. However, it is still unclear what impressions a robot’s quick boarding behavior will impart to surrounding passengers, when such limitations are removed in the future and mobile robots acquire human-like quick movements. It is also difficult to increase the speed of robots drastically in a manner which ensures the safety of experiment participants. In this study, we developed a mixed reality system that integrates a virtual robot into the real world to ensure the physical safety of participants during the experiment. Visual and auditory information about the virtual robot was provided to participants through a head-mounted display and was synchronized with the robot’s behavior. We focused on three basic factors of robot’s boarding behavior: forward speed during entry, rotation speed, and rotation direction in the elevator car. A statistical analysis revealed that quick, human-like entry into an elevator by a virtual robot reduces perceived anthropomorphism, likeability, intelligence, and safety, and increases the stress levels experienced by the passengers. The statistical analysis also showed that quick rotation inside the elevator by the virtual robot reduces perceived likeability. Our results provide useful knowledge for designing the behavior of future robots when they board elevators with other passengers.

As societies age rapidly, baby-sized robots using non-verbal, infant-like interactions have gained attention for their potential emotional benefits, even for users with cognitive impairments. Although occasional rough handling can disrupt natural interactions with these robots, little attention has been paid to mechanisms that recognize such behaviors and respond appropriately. To address this issue, we implemented and evaluated a function that detects rough handling using onboard inertial sensors and triggers crying sounds in a baby-sized robot. The experimental results indicated that while this crying response effectively signaled the robot’s discomfort—leading participants to perceive it as "experiencing unpleasantness"—it did not improve the impression ratings of the robot or significantly increase users’ guilt. These findings are likely influenced by the short-term, task-driven nature of the experiment, necessitating further investigation in more natural, long-term interactions.

Understanding how different levels of robotic assistance influence human perception, trust, and prosocial behavior is critical in human-robot interaction (HRI) research. This study investigates how the cost of help provided by a robot affects human perception, trust, and prosocial behavior by presenting participants with a video-based experiment. In the experiment, participants observed a humanoid robot, Sota, providing assistance under two conditions: high-cost help, where the robot shared power from its own battery, and low-cost help, where the robot facilitated power transfer from an external mobile battery. Results showed that participants perceived the robot as more anthropomorphic and intelligent in the high-cost condition, with increased trust ratings in both performance and moral trust dimensions. However, no significant difference was observed in participants’ prosocial behavior towards the robot. These findings suggest that while higher-cost robotic assistance enhances perception and trust, it does not necessarily lead to greater prosocial responses from humans. This study contributes to the broader understanding of how varying levels of robotic assistance impact human social responses and has implications for designing socially interactive robots in cooperative settings.

Social robots are increasingly being used as interactive partners to facilitate people’s understanding and appreciation of art. Considering people's stages of aesthetic development is essential to richer understanding of artworks, but such a viewpoint is less of a focus in human-robot interaction contexts. Therefore, we developed a robot system for collaborative art appreciation using a Visual Thinking Strategies (VTS) method to enhance people’s engagement with art by considering their stages of aesthetic experience. We conducted an experiment to investigate the effectiveness of our system in supporting art appreciation of participants in a laboratory setting. We also investigated the effects of embodiment, i.e., the physical body of a robot, on art-appreciation support. The experiment results indicate that our system significantly increased the intention to use it, which is related to social acceptance, and embodiment significantly influenced likeability, perceived intelligence, and perceived enjoyment.

Due to the distribution of various deceptive techniques in our daily environments, we must consider a way of educating interaction literacy, i.e., enabling trust calibration and appropriate responsive action through critical thinking and observations of interacting partners. For this purpose, in this study, we conducted an experiment where a social robot deliberately deceives participants under low-risk and non-harmful tasks (such as consent form readings) in both face-to-face and online survey settings, as an analogy of an evacuation drill or inoculation theory. We developed a semi-autonomous robot system where the robot provides information with or without deceptive strategies to attempt to either mislead (with deception) or encourage careful reading (without deception) of the consent form. After the tasks, the participants were informed of the robot’s role and evaluated their impressions of it. Additionally, following the laboratory experiment, participants voluntarily completed a follow-up online survey and participated in a similar consent form reading task online. The experimental results showed that the participants were more deceived due to the robot’s deceptive strategies, but their negative impressions of the robot were limited. Moreover, the participants who were deceived during the first task were also deceived in the second task. This suggests that our approach can be used as a promising method for identifying individuals who are easily deceived by others in non-harmful situations, thus aiding in the development of interaction literacy programs.

As the loneliness problem grows more severe among older people living in nursing homes, the use of baby-like robots as companions is becoming an option to relieve this problem. However, it remains unclear how baby-like behavior affects older people. This study investigates the effect of the emotional expression of baby-like robots on older people to realize a companion robot that relieves their loneliness. Through two experiments, we investigated older adults’ impressions of one or two robots that express different emotions. They rated their impressions of the robots by watching videos of one baby-like robot that vocalized different emotional sounds (crying, laughing, or babbling) in Experiment I and two baby-like robots that vocalized different combinations of emotional sounds in Experiment II. We found that the crying robot reduced their willingness and enjoyment to interact with the robot and suppressed the emotional effects of the other robot. Our findings can help improve the well-being of older adults, providing insight into how to support older adults using baby-like robots.

As virtual spaces continue to attract more and more attention, the development of virtual agents is also gaining momentum. Agents capable of expressing emotions are being developed, and various methods exist through which they express such feelings. Although much research has focused on the implementation of human actions in agents, no research has yet implemented facial self-touch for agents used in emotional expressions, even though such transmission of human emotions is a critical tool for them. In this study, we developed a system that enriches agents' emotional expressions by implementing self-touches in virtual agents. From an evaluation of our system, we found that it enhances the emotions of virtual agents. The naturalness of their emotional expressions varied depending on the emotion.

Using multiple robots in presentations more effectively attracts customers and provides information to them than a single robot. Many robotics researchers have employed this approach to achieve effective robot-presentation systems in real environments. Although these systems implicitly equalize the amount of speech in the conversational content among multiple robots, it remains unknown whether that strategy is actually important. For example, the effects of the second robot that provides only back-channel responses have not yet been investigated. Unveiling the relationships between the presentation effects and the amount of speech in multiple robot presentations is undoubtedly crucial for designing the content of multiple robots’ conversations. For this purpose, we conducted a video-based web-survey whose findings suggest that using multiple robots for product recommendations increases their commercial attractiveness. The results showed some advantages by equalizing the amount of speech compared to biasing that amount in presentations.

The landscape of education with social robots is evolving, especially within the realm of music education. However, past studies have focused on children as music learners and such verbal behaviors as praise. Therefore, it remains unknown whether existing studies are effective for adult learners in musical education as well as how to effectively design the non-verbal behaviors of robots. This study investigates the effective behaviors of social robots by comparing three kinds of listening behaviors: none, nodding (simple listening), and enjoying (affective listening). We developed a music education support system that consists of a social robot and a MIDI keyboard and conducted an experiment with adult participants. Our experimental results described the advantages of affective-listening behavior during music education over simple listening and non-listening based on gender.

Apology behavior design is becoming important for social robots that work in daily environments because of their widespread use. Robotics researchers have reported that multiple robots can effectively achieve more acceptance and trust in apology situations. Unfortunately, such effects have only been confirmed in a single country: Japan. Some studies investigated cultural differences in apologies between Japan and other cultures and reported how the former influences the perceived function and meaning of apologies. Therefore, we conducted a web-based survey to investigate whether using multiple robots in apology situations is effective in another country. We compared such perceived feelings as forgiveness and trust toward a robot’s apologies between Japan and the U.S. by using the visual stimuli of one and two robots. The experiment results showed that U.S. people felt that multiple robot apologies are more acceptable than apologies from just one robot, similar to results with Japanese participants. Perceived trust did show a different phenomenon between the two countries.

Recent mobile robots have the capability to take elevators from different floors in buildings. Although various technologies enable robots to use elevators for their daily environments, past research works mainly focused on the side of mobile robots, e.g., social behaviors through the robot’s device. In other words, the effects of social behaviors from smart elevators and the collaborative situations between mobile robots and elevators have received inadequate attention. Therefore, in this study, we investigate the effects of interaction design for a mobile robot and a smart elevator when the former takes the latter. We conducted an experiment where a mobile robot and/or an elevator spoke to passengers as voice cues and evaluated their perceived impressions toward the robot and the elevator. The experiment results showed positive effects of the voice cues of the smart elevator and the mobile robot, although we found no combination effects of both voice cues. On the other hand, either voice cue creates a positive impression toward the other, i.e., the elevator voice cue improves the perceived impressions of the mobile robot and vice versa. This phenomenon provides useful knowledge for both elevator/robot developers.

Head-tilting motions are social signals that play a key role in communication. In human-robot interaction, researchers have found that this motion increases the observer’s feelings of kawaii toward a robot. Kawaii is a Japanese word meaning cute and adorable, and the feeling of kawaii refers to such affectionate, tender feelings toward an object. The feeling of kawaii has proven to be crucial for designing socially acceptable robots. However, the impact of the head-tilting speed of social robots on the observer’s kawaii feelings remains unclear because previous research mainly used static images. This study determined the optimal head-tilting speed and direction to deepen the observer’s feeling of kawaii toward a robot. Our findings found that participants reported feeling more kawaii toward a robot when it tilted its head to its right within 0.5 or 1 second. Our results also showed that gestures associated with head tilting are critical for robot behavior design.

Motivated by the expectation that robot presence at crime scenes will become increasingly prevalent, the question arises of how they can protect humans in their care or vicinity. The current paper delves into the concept of “robot self-defense” and explores whether a robot should be tele-operated or autonomous, and how humans perceive imperfections in robot performance. To gain insight into how people feel, an online survey was conducted with 180 participants, who watched six videos of a robot defending a victim. The study provides insights into trust in human-robot interactions and sheds light on the complex dynamics involved in robot self-defense. The results indicate that people found a tele-operated robot to be more accepted, and that attempting to help but failing is more acceptable than just observing.

This study investigated the influence of behavior variations in a robot-initiated greeting. Previous studies on the physical interaction between humans and robots typically focused on constructing behaviors to increase the perceived naturalness of the interaction. The specific parameters of the physical contact were of secondary interest. The experiments in our study were designed to mainly focus on the physical aspect of the robot’s interaction with the participants. We varied two parameters of our interaction, which consisted of a shoulder tap to initiate a greeting by the robot. This scenario was selected to withdraw the robot from the participant’s awareness as much as possible. The parameters were the timing of the vocal salutation as well as an optional social cue in the form of a waving motion the robot executed when the participant looked at it after the shoulder tap. The results of the experiments showed, contrary to our predictions about the influence of timing and the performance of greeting motion, that those parameters affect the perceived naturalness of the participants in no significant captivity. Our results showed that in the conducted experiments, the most influential factor on the participants was the shoulder (left or right) on which the robot tapped them.

Such multiple agent interactions as robot-robot interactions are one effective approach to indirectly provide information, unlike direct interactions between people and agents. Several studies reported the effectiveness of this approach and developed conversational mechanisms for robot-robot interaction, although their physical interaction design for robot-robot interaction remains relatively less focused. This study concentrated on the effects of speed in touching behaviors between robots, because people may perceive the relationships shared between robots differently due to various motion speeds even though the motion is identical. Therefore, we conducted two web-survey experiments to investigate the perceptions of a robot's touching motion and how the relationship between two robots was perceived when a robot touched another at different speeds. The first experiment showed two peak speeds where people perceived a robot's touch as patting (friendly touch) or slapping (aggressive touch). The second experiment results showed similar peak speeds where people perceived the relationships between robots as positive or negative to patting or slapping behaviors. We believe that knowledge about the relationships between motion speeds and perceived friendliness between robots will contribute to design physical interaction between robots.

Would it be okay for a robot to hurt a human, if by doing so it could protect someone else? Such ethical questions could be vital to consider, as the market for social robots grows larger and robots become increasingly prevalent in our surroundings. Here we introduce the topic of “robot self-defense”, which involves the use of force by a robot in response to violence, to protect a human in its care. To explore this topic, we conducted a preliminary analysis of the literature, as well as brainstorming sessions, which led us to formulate an idea about how people will perceive robot self-defense based on the perceived risk of loss. Additionally, we propose a study design to investigate how the general public will perceive the acceptability of a robot using self-defense techniques. As part of this, we describe some hypotheses based on the assumption that the perceived acceptability will be affected by both the entities involved in a violent situation and the amount of force that is applied. The proposed scenarios will be used in a future survey to evaluate participants' perception of a social robot using self-defense techniques under varying circumstances, toward stimulating ideation and discussion on how robots will be able to help people to live better lives.

Due to the current worldwide COVID-19 pandemic, such measures as social distancing is being promulgated to reduce community spread. Unfortunately, such steps greatly limit physical touching, and this touch starvation increases people's stress. We address this problem by focusing on touch interactions with oneself: self-touch. We developed a system that consists of a touch sensor and a supportive voice function to encourage self-touch behaviors. We experimentally evaluated whether our developed system increased self-touch behaviors and investigated its stress-buffering effects under stressful settings. The experimental results showed that the system increased self-touch behaviors, although only male participants showed significant stress-buffering effects.

Could a social robot use force to prevent violence directed toward humans in its care?–Might crime be eradicated, or conversely could excessive use of force proliferate and human dignity become trampled beneath cold robotic wheels? Such speculation is one part of a larger, increasingly important question of how social robots will be expected to behave in our societies, as robotic technologies develop and become increasingly widespread. Here, to gain some insight into this topic of "robot self-defense", we proposed a simplified heuristic based on perceived risk of loss to predict acceptability, and conducted a user survey with 304 participants, who watched eight animated videos of robots and humans in a violent altercation. The results indicated that people largely accept the idea that a humanoid robot can use force on attackers to help others. Furthermore, self-defense was perceived as more acceptable when the appearance of the defender was humanoid rather than mechanical, and when the force disparity between attacker and defender was high. The immediate suggestion is that it could be beneficial to re-examine common assumptions that a robot should never harm or risk harming humans, and to discuss and consider the possibilities for robot self-defense.

Assisting patients with dementia is a significant social issue. Currently, to assist patients with dementia, a multimodal care technique called Humanitude is gaining popularity. In Humanitude, the patients are assisted through various techniques to stand up independently by utilizing their motor functions as much as possible. Humanitude care techniques encourage caregivers to increase the area of contact with patients during the sit-to-stand motion. However, Humanitude care techniques are not accurately performed by novice caregivers. Therefore, in this study, a smock-type wearable sensor was developed to measure the proximity between caregivers and care recipients during sit-to-stand motion assistance. A measurement experiment was conducted to evaluate the proximity differences between Humanitude care and simulated novice care. In addition, the effects of different care techniques on the center of mass (CoM) trajectory and muscle activity of the care recipients were investigated. The results showed that the caregivers tend to bring their top and middle trunk closer in Humanitude care compared with novice simulated care. Furthermore, it was observed that the CoM trajectory and muscle activity under Humanitude care were similar to those observed when the care recipient stands up independently. These results validate the effectiveness of Humanitude care and provide useful information for teaching techniques in Humanitude.

We propose a wearable tactile sensor suit, which can be regarded as tactile sensor networks, for monitoring natural body dynamics to be exploited as a computational resource for estimating the posture of a human or robot that wears it. We emulated the periodic motions of a wearer (a human and an android robot) using a novel sensor suit with a 9-channel fabric tactile sensor on the left arm. The emulation was conducted by using a linear regression (LR) model of sensor states as readout modules that predict the next wearer's movement using the current sensor data. Our result shows that the LR performance is comparable with other recurrent neural network approaches, suggesting that a fabric tactile sensor network can monitor the natural body motions, and further, this natural body dynamics itself can be used as an effective computational resource.

Wearable robots are traditionally used as tools for assisting muscular strength and decrease the physical loads of users. In this study, we are interested in the social aspect of wearable robots and investigate the effects of wearing them toward perceived impressions of them. For this purpose, we newly developed a wearable robot, Fylgear, with minimum capabilities to interact with a user. We experimentally investigated whether wearing a robot changes people’s perception of the robot by using different questionnaire items: a set of positive impressions and kawaii (a Japanese word that means "cute" that has positive connotations) feeling. The experiment results showed that participants who wear the robot positively evaluated the robot in the context of likeability and total impression, but there are no significant differences in perceived kawaii feelings.

Kawaii, a Japanese word that means "cute," is an essential design concept in consumer and pop culture in Japan. In this study, we focused on a situation where a social robot describes an object during an information-providing task, which is commonly required for social robots in daily environments. Since past studies reported kawaii feelings are associated with a motivation to approach a target, our robot expressed feelings of kawaii to objects touch behaviors. We also focused on whether touch behaviors that emphasize style increase the feeling of kawaii of the touched object, following a phenomenon where people strongly touch a target when they overwhelmingly feel positive emotion: cute aggression. Our experimental results showed the effectiveness of touch behaviors to express the feelings of kawaii from the robot toward objects and to increase the participants' feelings of kawaii toward the object. We identified fewer effects from the participants to the robot. The emphasized motion style did not show any significant effects for the kawaii feelings.

Although social touch in physical space has been scrutinized for its positive effects on people, the effects of social touch in virtual space has been neglected. In virtual space, two types of social touch can be designed, a touch with only visual stimuli and a touch with both visual and tactile stimuli. This paper investigates the effects of the two types of agent's social touch on users in the context where the agent praises their performance of a task in virtual space. Based on past studies of social touch, we hypothesized that the two types of agent's social touch in virtual space would improve the user's task motivation, task performance, and the agent's likability. We experimentally tested our hypotheses by comparing those variables among no-touch, visual-touch, and visual-tactile touch groups. Since our results showed no significant differences among these groups, our hypotheses were not supported. However, a post-hoc analysis by gender suggests that the agent's social touch with both visual and tactile stimuli while praising male users increased their task motivation in virtual space. This result suggests that the effects of an agent's social touch in virtual space may be different due to genders, and shows a possibility of positive effects for the touch behavior design of the agent in virtual space.

Using multiple robots for information providing tasks is one effective approach to attract people by showing conversational interaction between robots. In this study, we investigate the effectiveness of showing such intimate interaction between robots as touching because it has positive effects on human-robot interactions, even though it has received less attention than robot-robot interaction. We prepared touch behaviors and whispering voices between robots and conducted experiments where robots provided information to participants. Our experiment results showed the effectiveness of both touch behaviors and whispering voices between robots in information providing tasks. On the other hands, we also found that whispering voices decreased their likeability, although touch behavior did not have such negative effects.

We introduce a minimal design approach to achieve a robot for interactive doll therapy. Our approach aims for positive interactions with older adults with dementia by just expressing the most basic elements of human-like features and relying on the user's imagination to supplement the missing information. Based on this approach, we developed HIRO, a baby-sized robot with abstract body representation and without face. The recorded voice of a real human infant emitted by robots enhance human-like features of the robot and then facilitate emotional interaction between older people and the robot. A field study showed that HIRO was accepted by older adults with dementia and facilitated positive interaction by stimulating their imagination.

This study addresses the pre-touch reaction distance effects in human-agent touch interaction in a VR environment. Past studies on human-agent interaction focused on post-touch situations, i.e., pre-touch situations received less attention, and such pre-touch situation was only investigated with a physical agent, i.e., robot. In this study, we conducted a preliminary data collection to investigate the minimum comfortable distance to virtual agent's touch by using VR application. For this purpose, we prepared two kinds of virtual agents (female and male) to investigate gender effects in touch settings. We analyzed the collected data to investigate about people's perceptions towards a touch from virtual agents, and the results showed similar phenomenon with a physical agent.

People engage in social coordination without explicitly communicating when they are conflicting over spatial resources, e.g., a shop clerk who yields to customers the best place to view products. In this study, we proposed a method that achieves such social coordination with a robot. Our idea is that the social coordination between two agents can be represented as utility-maximizing behavior for joint utility rather than just by a single agent utility. That is, given that each agent's reasonable behavior can be represented as utility-maximizing behavior for single agent utility, we model each agent's plans for himself as well as for the partner agent. Moreover, superiority relationships exist in this joint-utility computation. Since each agent knows such superiority relationships, social coordination can be modeled as utility-yielding behavior based on informed superiority. We specifically focus on looking-together situations for which we developed a utility model. With simulations, we investigate whether the above joint-utility-based modeling successfully reproduces social coordination in looking-together situations. We conducted an experiment in a situation where a tele-operated robot and a customer together look at products in a shop environment. Our experimental results show that our proposed method enables the robot to socially coordinate spatial resources, yielding significantly more thoughtful, less-self-centered, and appropriate impressions than the alternate robot.

In human science literature, a hug interaction from a close person buffers the stress of the person being hugged. In this study, we investigated whether audio-visual stimuli during a hug with a robot change such stress buffer effects. For this purpose, we developed the MetaHug system, which consists of a huggable robot and a head-mounted-display that enables a virtual agent to reciprocate hugs. In our experiment, participants did stressful serial subtraction tasks after they experienced hug interactions with either two kinds of virtual agents: female or male appearances. Experiment results with 18 participants showed that participants showed significantly low stress when they did a hug with opposite-gender appearance agent compared to same-gender appearance agent.

This paper addresses the effects of a subtle pause in reactions during human-robot touch interactions. Based on the human scientific literature, people's reaction times to touch stimuli range from 150 to 400 msec. Therefore, we decided to use a subtle pause with a similar length for reactions for more natural human-robot touch interactions. On the other hand, in the human-robot interaction research field, a past study reports that people prefer reactions from a robot in touch interaction that are as quick as possible, i.e., a 0- second reaction time is slightly preferred to 1- or 2- second reaction times. We note that since the resolution of the study's time slices was every second, it remains unknown whether a robot should take a pause of hundreds of milliseconds for a more natural reaction time. To investigate the effects of subtle pauses in touch interaction, we experimentally investigated the effects of reaction time to people's touch with a 200-msec resolution of time slices between 0 second and 1 second: 0 second, 200, 400, 600, and 800 msec. The number of people who preferred the reactions with subtle pauses exceeded the number who preferred the 0- second reactions. However, the questionnaire scores did not show any significant differences because of individual differences, even though the 400-msec pause was slightly preferred to the others from the preference perspective.

Using multiple depth sensors enables us to accurately track pedestrians in real environments. Accurate pedestrian positions are essential for building an effective human-centered cyber world provided by location-based services. In particular, ceiling-mounted depth sensors can robustly track people in such environments. However, one important problem for this approach is the accurate calibration of the absolute sensor positions. This problem remains unsolved due to limited range and sensor distortions from a distance. Manual calibration is complicated and time-consuming, and the existing calibration method still has several limitations since it used a pedestrian as a movable landmark. Instead of a human landmark, we propose a method that uses a mobile robot as a movable and localized landmark to calibrate each sensor. We compared the calibration performance of the proposed and existing methods and showed that the former achieved more accurate calibration for both the absolute sensor and tracked pedestrian positions. Our proposed method with a mobile robot not only increased the accuracy of the calibration processes but also decreased human efforts.

This paper investigates whether a network robot salesclerk system increases sales in real shopping contexts. Our robot system, which consists of an autonomous virtual agent and a semi-autonomous physical agent, enables customers to interact with the virtual agents on their smartphones and reserve special character merchandise. Moreover, their virtual agent is transferred to the physical agent at the shop to physically distribute the reserved merchandise to customers. Through such cyber-physical interaction, we provided rich shopping experiences to customers to increase sales. We collaborated with an animation company, Production I.G Inc., and employed an animation character named Tachikoma from the Ghost in the Shell:Stand Alone Complex (a.k.a S.A.C. seriese) universe to design the appearance and the characteristics of both agents. We conducted field trials to investigate whether the developed system contributed to sales related to the animation merchandise of Ghost in the Shell, and the results showed our system's effectiveness.

This paper investigates the effects of a listener robot that joins a storytelling situation for children as a side-participant. For this purpose, we develop a storytelling-robot system that consists of both reader and listener robots. Our semi-autonomous system involves a human operator who makes correct responses and provides easily understandable answers to the children's questions during storytelling. We develop a gaze model for the natural and autonomous gaze behaviors of a reader robot by considering multiple listeners and a storytelling object (a display that shows images). We conducted an experiment with 16 children to investigate whether they preferred storytelling with/without the listener robot and the changes of speech activities during the storytelling. Children preferred storytelling with the listener robot to storytelling without it. Their speech activities decreased when the listener robot was involved in the storytelling.

We propose a technique to estimate a child's extraversion and agreeableness for social robots that interact with children. The proposed approach observed children's behavior using only the robot's sensors, without any sensor networks in the environment. An RGBD sensor was used to track and identify children's facial expressions. Children's interactions with the robot were observed, such as their distance from the robot and the duration of their eye contact, because such information would provide clues to estimate their personality. Data were collected when a robot, tele-operated by preschool teachers, interacted with kindergarten children individually. The data from 29 children was used to successfully estimate the children's personality compared to chance rates.

This paper presents the effects of being hugged by a robot to encourage prosocial behaviors. In human-human interaction, touches including hugs are essential for communication with others. Touches also show interesting effects, including the “Midas touch,” which encourages prosocial behaviors from the people who have been touched. Previous research demonstrated that people who touched a robot experienced positive impressions of it without clarifying whether being hugged by a robot causes the Midas touch effect, i.e., positively influences engagement in prosocial behaviors. We developed a huge, teddy-bear-like robot that can give reciprocal hugs to people and experimentally investigated its effects on their behaviors. In the experiment, a robot first asked participants to give a hug and then asked them to make charitable donations in two conditions: with or without a reciprocated hug. Our experiment results with 38 participants showed that those who were hugged by a robot donated more money than those who only hugged the robot, i.e., without a reciprocated hug.

Haptic interaction is a key capability for social robots that closely interact with people in daily environments. Such human communication cues as gaze behaviors make haptic interaction look natural. Since the purpose of this study is to increase human-robot touch interaction, we conducted an experiment with 20 participants who interacted with a robot with different combinations of gaze behaviors and touch styles. The experimental results showed that both gaze behaviors and touch styles influence the changes in the perceived feelings of touch interaction with a robot.

The recognition of an indicated object by an interacting person is an essential function for a robot that acts in daily environments. To improve recognition accuracy, clarifying the goal of the indicating behaviors is needed. For this purpose, we experimentally compared two kinds of interaction strategies: a robot that explicitly provides instructions to people about how to refer to objects or a robot that implicitly aligns with the people's indicating behaviors. Even though our results showed that participants evaluated the implicit approach to be more natural than the explicit approach, the recognition performances of the two approaches were not significantly different.

Social robots are used for interacting with children. Their novelty often stimulates the interest of children and encourages such interaction as playing with them. However, some children do not interact with such robots and instead strongly reject interaction with them. Understanding such rejection behaviors of children has important design implications for social robots that are supposed to interact with children. For this purpose, we investigated what kinds of rejection behaviors appeared in different kinds of robots in a play room environment.

This paper reports a tele-presence robot named ChiCaRo, which is designed for interaction with babies and toddlers. ChiCaRo can physically interact with babies and toddlers by moving around and using its small hand. We conducted a field trial at a playroom where babies and toddlers can freely play to investigate ChiCaRo's effectiveness. In the experiment adult participants interacted with their babies and toddlers by ChiCaRo and another robot. The adult participants evaluated ChiCaRo highly in the context of remote interaction with their babies and toddlers.

In human-human interaction, peer pressure is a major social influence on people's thoughts, feelings, and behaviors. The larger the group of people, the more social influence it exerts. In this paper, we investigate whether multiple robots and their synchronized behaviors exert peer pressure on people, as in human groups. We developed a multiple robot controller system that enables robots to perform precise synchronization. In the experiment, we prepared a setting that resembled previous experiments that investigated peer pressure between people and robots. The participants answered questions after hearing the robots' answers, only some of which were incorrect. Our experiment results showed that the influence of the synchronized multiple robots increased the error rates of the participants, but we found no significant effects toward conformity.

This paper reports an interactive approach to improve the recognition performance by robots of objects indicated by humans during human-robot interaction. We developed an approach based on two findings in conversations where a human refers to an object, which is confirmed by a robot. First, humans tend to use the same words or gestures as the robot in a phenomenon called alignment. Second, humans tend to decrease the amount of information in their references when the robot uses excess information in its confirmations: in other words, alignment inhibition. These findings lead to the following design; a robot should use enough information without being excessive to identify objects to improve recognition accuracy because humans will eventually use similar information to refer to those objects by alignment. If humans more frequently use the same information to identify objects, the robot can more easily recognize those being indicated by humans. To verify our design, we developed a robotic system to recognize the objects to which humans referred and conducted a control experiment that had 2 × 3 conditions; one factor was the robot's confirmation way and another was the arrangement of the objects. The first factor had two levels to identify objects: enough information and excess information. The second factor had three levels: congestion, two groups, and a sparse set. We measured the recognition accuracy of the objects humans referred to and the amount of information in their references. The success rate of the recognition and information amount was higher in the adequate information condition than in the excess condition in a particular situation. The results suggested the possibility that our proposed interactive approach improved recognition performance.

This paper investigates people's social acceptance of a childcare support robot system and compares their attitudes to two childcare technologies: anesthesia during labor and baby food (processed food and formula milk), which includes powdered milk and instant food for babies and toddlers. To investigate their social acceptance, we developed scales from three points of view: safety and trustworthy, diligence, and decreasing workload. For this paper, our participants were comprised of 412 people located through a web-based survey and 14 people who experienced the prototype of our childcare support robot system. They answered questionnaires about our three developed scales and an intention to use scale to investigate their social acceptance toward childcare support technologies. The web-based survey results indicate that our system's concept was evaluated lower than current childcare support technologies, but people who experienced our system prototype evaluated it higher than those who filled out web-based surveys.

Social robots working among pedestrians can attract crowds of people around them and consequently become bothersome entities causing congestion in narrow spaces. This in turn can affect the comfort of pedestrians who wish to pass through. To address this problem, our idea is to endow the robot with three capabilities: anticipating pedestrian crowding around the robot, understanding pedestrians' walking comfort, and planning to avoid congestions in advance. Combining several elementary pedestrian behavior models, the robot is able to simulate hypothetical situations where it navigates between pedestrians and anticipate the degree to which this would affect the pedestrians' walking comfort. During planning, the robot determines the best next navigation step based on the results of simulations. We tested the developed system in a real shopping mall and confirmed that it successfully reduces the robot's influence on pedestrian walking comfort due to congestions.

This study developed a fall detection system for elderly people in a nursing home and investigated their acceptance of it. The system obtained their positions and heights from range sensors and used that information to correctly detect 89.5% of the falls based on data where the elderly crouched in a mockup room of a nursing home. We investigated the social acceptance with elderly people by comparing three conditions: (1) only detecting out-of-bed, (2) detecting falls in a room and always showing the human position, and (3) detecting falls in a room and only showing the human position when a fall happened. The results showed that intention to use were significantly higher in the second and third conditions than the first condition.

This study investigates whether a social robot which interacts with children via quiz-style conversations increases their understanding of science classes. We installed a social robot in an elementary school science classroom where children could freely interact with it during their breaks. The robot asks children questions related to their latest science classes to support their understanding of the classes. During interaction, the robot says children's name and distribute its gaze among the group of children by using a face recognition system and a human tracking system. Still, speech recognition is difficult in the noisy elementary school environment; therefore the operator takes over this function during interactions. In this study our result did not show significant effects of the robot for helping children's understanding, but we found several interesting interaction scenes which shows that robot had a certain effect on specific children.

We propose a huggable communication device called Hugvie that encourages children to concentrate on listening by reducing their stress and strengthening the feeling that the storyteller is close. We observed a group of preschool children who listened to a story and conclude that Hugvie has the potential to facilitate attention on stories. This indicates its usefulness to relieve the educational problem where children show disobedient or restless behavior during class. We discuss Hugvie's effect on learning and memory and potential applications to special-needs children.

This study reports a preliminary investigation of a trial to support child-care at an intelligent playroom. We installed a human tracking system and a toy-like robot into the playroom to record children/parents activities. Moreover, we interviewed nurses at nursery schools to study their attitude towards use of robotics technologies for child-care to investigate the social acceptance of robots for supporting child-care.

The importance of assistive robots for elderly people is increasing to match the needs of rapidly aging world societies. This paper develops a wheelchair robot to support the movement of the elderly to decrease caregiver workloads from the perspective of physical load and time consumption. We observed the behaviors of caregivers at a private residential care home when they are wheeling elderly people in wheelchairs. The behavior includes appropriate utterances for reducing the anxieties of senior citizens based on properties of place, e.g., "Here the path is a little narrow" while passing a narrow space or "Now let's go into elevator." They also adjusted to the wheeling speed preference of each individual because their preferred speeds were different. We implemented these two functions for elderly care using the Ubiquitous Network Robot Platform that allows the management of users and environment properties. Experimental results with elderly participants at a pseudo resident home show that the seniors prefer a wheelchair robot on which the above functions have been implemented.

Small robots are being designed to recognize behaviors through playful interaction. Prior work used data from impoverished sensing devices such as inertial sensors to analyze gestures and attitude in playful interaction through time series analysis. However, the prior work did not focus on individual differences required for person identification. This research hypothesizes that person identification can be achieved by determining individual differences in playful interaction by using inertial sensor data. We propose a method that iteratively narrows down the candidates during interaction to achieve accurate person identification. This method calculates the features using a time series of the inertial sensor data. These features identify a candidate who is playfully interacting with the robot using a decision tree classifier that includes combinations of the current candidates. The system stores the results as a dataset for voting, and the voting results are used to reduce the candidates until the number of candidates is winnowed to one. Evaluation results show that our proposed method identifies persons through playful interactions with 99.1% accuracy.

The development of humanlike service robots which interact socially raises a new question: How can we make good interaction content for such robots? Domain experts specializing in the target service have the knowledge for making such content. Yet, while they can easily engage in good face-to-face interactions, we found it difficult for them to prepare conversational content for a robot in written form. Instead, we propose involving experts as teleoperators in an iterative development process in which the expert develops content, teleoperates a robot using that content, and then revises the content based on that interaction. We propose a software system and design guidelines to enable such an iterative design process. To validate these solutions, we conducted a comparison experiment in the field, with elderly volunteer guides teleoperating a robot at a tourist information center in Nara, Japan. The results showed that our system and guidelines enabled domain experts with no robotics background to create better interaction content and conduct better interactions than domain experts without our system.

Interpersonal touch is said to have significant effects on social interaction. We used the ultimatum game to examine whether touch from a robot could inhibit a negative feeling to the robot. We set two experimental conditions: the one was "touch condition" in which unfair proposals were offered to a participant when a robot touched his/her arm and the other was "no touch condition" in which unfair proposals were offered when the same robot did not. We compared Medial Frontal Negativity (MFN) measured by EEG, whose amplitude is correlated with feeling of unfairness, between the two conditions. Result shows that MFN amplitude was larger in the no touch condition than in the touch condition. This indicates that touch from a robot may inhibit a sense of unfairness for the robot. Our finding suggests that touch from a robot could enhance positive feeling to the robot through human-robot interaction.

Assistive robots can be perceived in two main ways: tools or partners. In past research, assistive robots that offer physical assistance for the elderly are often designed in the context of a tool metaphor. This paper investigates the effect of two design considerations for assistive robots in a partner metaphor: conversation and robot-type. The former factor is concerned with whether robots should converse with people even if the conversation is not germane for completing the task. The latter factor is concerned with whether people prefer a communication/function oriented design for assistive robots. To test these design considerations, we selected a shopping assistance situation where a robot carries a shopping basket for elderly people, which is one typical scenario used for assistive robots. A field experiment was conducted in a real supermarket in Japan where 24 elderly participants shopped with robots. The experimental results revealed that they prefer a conversational humanoid as a shopping assistant partner.

This paper presents the effect of a robot's active touch for improving people's motivation. For services in the education and healthcare fields, a robot might be useful for improving the motivation of performing such repetitive and monotonous tasks as exercising or taking medicine. Previous research demonstrated with a robot the effect of user touch on improving its impressions, but they did not clarify whether a robot's touch, especially an active touch, has enough influence on people's motive. We implemented an active touch behavior and experimentally investigated its effect on motivation. In the experiment, a robot requested participants to perform a monotonous task with a robot's active touch, a passive touch, or no touch. The result of experiment showed that an active touch by a robot increased the number of working actions and the amount of working time for the task. This suggests that a robot's active touch can support people to improve their motivation. We believe that a robot's active touch behavior is useful for such robot's services as education and healthcare.

Tour guidance is a common task of social robots. Such a robot must be able to encourage the participation of people who are not directly interacting with it. We are particularly interested in encouraging people to overhear its interaction with others, since it has often been observed that even people who hesitate to interact with a robot are willing to observe its activity. To encourage such participation as bystanders, we developed a robot that walks backwards based on observations of human tour guides. Our developed system uses a robust human tracking system that enables a robot to guide people by walking forward/backward and allows us to scrutinize people's behavior after the experiment. We conducted a field experiment to compare the ratios of overhearing in "walking forward" and "walking backward." The experimental results revealed that in fact people do more often overhear the robot's conversation in the "walking backward" condition.

To provide personal and location-dependent services in public spaces such as shopping malls, it is important to be able to estimate the positions and identities of people in the environment. Sensors in the environment reliably detect their current positions, but it is difficult to identify people using these sensors. On the other hand, signals from wearable sensors can be used to identify people correctly, but precise position estimation remains problematic. In this paper, we describe a novel method of integrating laser range finders (LRFs) in the environment and wearable inertial sensors. Time sequences of angular velocities estimated from both LRFs and wearable sensors are matched to identify people. Examples of tracking individuals in the environment that confirm the effectiveness of this method are shown.

Due to the anticipated future, extensive use of robots, human beings will probably share common spaces with them. The relationships between robots and humans will be conducted at close distances. Predicting people's future positions helps robots understand human behavior and react safely and naturally. In this paper, we propose a method for predicting people's positions in crossing behaviors, i.e. different trajectories people follow when they are crossing each other. We conducted a field experiment to gather various crossing behaviors of pedestrians in a shopping mall environment and analyzed them by focusing on “hot areas” spaces where people modify their trajectories for crossing. We clustered typical crossing behaviors in hot areas and modeled them using Hidden Markov Models for predictions. Our algorithm more accurately predicts the future positions of pedestrians by considering moving direction and speed.

Social robots that provide services to humans in real environments have been developed in recent years. Such a robot should appropriately recognize its users' orders through human-like communications because of user-friendliness. However, their styles of communicating are too diverse to achieve this goal. If the robot could shape their styles, its recognition ability would be improved. An entrainment, which is a phenomenon where human's behavior is synchronized with robot's behavior, can be useful for this shaping. Previous studies have reported the entrainment occurring in the same modality, but they have given little attention to entrainment across different modalities (e.g., speech and gestures). We need to consider this cross-modal effect because human-robot interaction is inherently multi-modal. In this paper, we defined “mutual entrainment” as the entrainment across different modalities and investigated the effect of it through a laboratory experiment. We evaluate how the frequency of human pointing gestures varies with the amount of information in robot speech, and as a result, we found that the gesture frequency increased as the amount of information decreased. The results suggest that smoother human-robot communications can be achieved by shaping human behavior through mutual entrainment.

This paper reports the persuasion effect of a robot's whispering behavior that consists of a whispering gesture and a request made in a small voice. Whispering gestures naturally make close distance and create warmth feelings with subjects, and requests in quiet voices with whispering gestures also create familiar impressions, which are effective factors of persuasion. We believe that such physical behavior as whispering is one persuasion advantage held by real robots over computers. We conducted a between-subjects experiment to investigate the effectiveness of these two factors on persuasion. In the experiment, the robot requests an annoying task of the subjects; writing as many multiplication table equations as possible. As a result, whispering gestures significantly increased the working time and the number of equations. On the other hand, the loudness of the voice in the request had no effect. We believe the results indicate the effectiveness of physical behavior for persuasion in human-robot interaction.

We present a study on people's acceptance of robots performing tasks in a city. Three different entities (a human, a human in the costume of a mascot, and a robot) performed tasks in three different scenarios: endlessly guiding, responding to irrational complaints, and picking an accidentally discarded key out of a trash. All of these tasks involve entities interacting with visitors in situations that are troublesome, i.e. dull, stressful, and dirty, respectively. For this paper, 24 participants watched 9 videos (three tasks performed by three entities) and evaluated the appropriateness of each entity for the task and the human-likeness of the entity. The results indicate that people prefer to have a robot rather than a human perform these troublesome tasks, even though these tasks require much interaction with people. In addition, the comparison with a human in costume enables us to further speculate that people's expectations of an entity deserving human rights rather than being human-like in appearance or cognitive capability would be a reason for their judgments regarding appropriateness.

This paper reports our development of a communication robot for use in a shopping mall to provide shopping information, offer route guidance, and build rapport. In the development, the major difficulties included sensing human behaviors, conversation in a noisy daily environment, and the needs of unexpected miscellaneous knowledge in the conversation. We chose a network-robot system approach, where a single robot's poor sensing capability and knowledge are supplemented by ubiquitous sensors and a human operator. The developed robot system detects a person with floor sensors to initiate interaction, identifies individuals with radio-frequency identification (RFID) tags, gives shopping information while chatting, and provides route guidance with deictic gestures. The robot was partially teleoperated to avoid the difficulty of speech recognition as well as to furnish a new kind of knowledge that only humans can flexibly provide. The information supplied by a human operator was later used to increase the robot's autonomy. For 25 days in a shopping mall, we conducted a field trial and gathered 2642 interactions. A total of 235 participants signed up to use RFID tags and, later, provided questionnaire responses. The questionnaire results are promising in terms of the visitors' perceived acceptability as well as the encouragement of their shopping activities. The results of the teleoperation analysis revealed that the amount of teleoperation gradually decreased, which is also promising.

To explore possible robot tasks in daily life, we developed a guide robot for a shopping mall and conducted a field trial with it. The robot was designed to interact naturally with customers and to affectively provide shopping information. It was also designed to repeatedly interact with people to build a rapport; since a shopping mall is a place people repeatedly visit, it provides the chance to explicitly design a robot for multiple interactions. For this capability, we used RFID tags for person identification. The robot was semi-autonomous, partially controlled by a human operator, to cope with the difficulty of speech recognition in a real environment and to handle unexpected situations.

Social interactions unfold over time, at multiple time scales, and can be observed through multiple sensory modalities. In this paper, we propose a machine learning framework for selecting and combining low-level sensory features from different modalities to produce high-level characterizations of human-robot social interactions in real-time.

A communication robot must recognize a referred-to object to support us in daily life. However, using our wide human vocabulary, we often refer to objects in terms that are incomprehensible to the robot. This paper focuses on lexical entrainment to solve this problem. Lexical entrainment is the phenomenon of people tending to adopt the terms of their interlocutor. While this has been well studied in human-computer interaction, few published papers have approached it in human-robot interaction. To investigate how lexical entrainment occurs in human-robot interaction, we conduct experiments where people instruct the robot to move objects. Our results show that two types of lexical entrainment occur in human-robot interaction. We also discuss the effects of the state of objects on lexical entrainment. Finally, we developed a test bed system for recognizing a referred-to object on the basis of knowledge from our experiments.

This paper reports the challenges of developing multiple social robots that operate in a shopping mall. We developed a networked robot system that coordinates multiple social robots and sensors to provide efficient service to customers. It directs the tasks of robots based on their positions and people's walking behavior, manages the paths of robots, and coordinates the conversation-performance between two robots. Laser range finders were distributed in the environment to estimate people's positions. The system estimates such human walking behaviors as ¿stopping¿ or ¿idle walking¿ to direct robots to provide appropriate tasks to appropriate people. Each robot interacts with people to provide recommendation information and route information about shops. The system sometimes simultaneously uses two robots to lead people from one place to another. The field trial, which was conducted in a shopping mall where four robots interacted with 414 people, revealed the effectiveness of the network robot system for guiding people around a shopping mall as well as increasing their interest.

For a robot providing services to people in a public space such as a shopping mall, it is important to distinguish potential customers, such as window shoppers, from other people, such as busy commuters. In this paper, we present a series of abstraction techniques for people's trajectories and a service framework for using these techniques in a social robot, which enables a designer to make the robot proactively approach customers by only providing information about target local behavior. We placed a ubiquitous sensor network consisting of six laser range finders in a shopping arcade. The system tracks people's positions as well as their local behaviors, such as fast walking, idle walking, wandering, or stopping. We accumulated people's trajectories for a week, applying a clustering technique to the accumulated trajectories to extract information about the use of space and people's typical global behaviors. This information enables the robot to target its services to people who are walking idly or stopping. The robot anticipates both the areas in which people are likely to perform these behaviors as well as the probable local behaviors of individuals a few seconds in the future. In a field experiment, we demonstrate that this service framework enables the robot to serve people efficiently.

This paper reports an initial field trial with a prototype of a semiautonomous communication robot at a train station. We developed an operator-requesting mechanism to achieve semiautonomous operation for a communication robot functioning in real environments. The operator-requesting mechanism autonomously detects situations that the robot cannot handle by itself; a human operator helps by assuming control of the robot.

For a robot providing services to people in a public space such as a train station or a shopping mall, it is important to distinguish potential customers, such as window-shoppers, from other people, such as busy commuters. In this paper, we present a series of techniques for anticipating people's behavior in a public space, mainly based on the analysis of accumulated trajectories, and we demonstrate the use of these techniques in a social robot. We placed a ubiquitous sensor network consisting of six laser range finders in a shopping arcade. The system tracks people's positions as well as their local behaviors such as fast walking, idle walking, or stopping. We accumulated people's trajectories for a week, applying a clustering technique to the accumulated trajectories to extract information about the use of space and people's typical global behaviors. This information enables the robot to target its services to people who are walking idly or stopping. The robot anticipates both the areas in which people are likely to perform these behaviors, and also the probable local behaviors of individuals a few seconds in the future. In a field experiment we demonstrate that this system enables the robot to serve people efficiently.

This paper proposes a new server platform for supporting various robotic services that interact with people. Every robot serving people in public spaces need to know: where the person is, what the person is doing, and what is happening at the place. The proposed platform is the first integrated system that can robustly measure, recognize and provide information on human activities and spatial nature in the environment, in a standard way. In the shopping mall experiment, the system is able to track and capture activities of more than 20 people simultaneously in real time. We also describe the result of robotic service experiment utilizing the power of this platform.

This paper reports a method in which a communicative robot simultaneously interacts with two persons and identifies them with passive-type RFID and floor sensors. The difficulty emanates from the association of these inputs. A passive-type RFID reader provides very accurate person identification. However, the robot cannot specify who actually used the RFID if surrounded by many people. A floor sensor provides robust tracking of people’s positions, but it does not identify any person by itself. Thus, combining these sensors into one device that provides robust tracking of position with accurate person identification would be very beneficial. Toward this problem, in our key idea, the robot keeps multiple hypotheses about the interpretation of sensor information and gradually improves their accuracy through interaction with the people. The multimodal inputs from sensors are integrated with a Bayesian network that allows us to exploit people’s behavior patterns as parameters to estimate the accuracy of our hypotheses. Our method also considers the vagueness of current hypotheses when choosing appropriate interactive behaviors. When the robot has reservations about the situation, it prefers non-conclusive behavior and tries to solve the ambiguity by performing verification behaviors. Once the robot is certain, it prefers conclusive behavior. The developed system was tested in a real daily environment, a shopping center, where we gathered interaction data between the robot and multiple people as teaching data for the Bayesian network. The experimental results revealed that the system successfully identified 79.2% of the visitors. In particular, the hypothesis-based system for behavior control improved the successful rate by 16.7%.

This paper reports a method that uses humanoid robots as a communication medium. There are many interactive robots under development, but due to their limited perception, their interactivity is still far poorer than that of humans. Our approach in this paper is to limit robots' purpose to a non-interactive medium and to look for a way to attract people's interest in the information that robots convey. We propose using robots as a passive-social medium, in which multiple robots converse with each other. We conducted a field experiment at a train station for eight days to investigate the effects of a passive-social medium.

This paper describes a group attention control (GAC) system that enables a communication robot to simultaneously interact with many people. GAC is based on controlling social situations and indicating explicit control to unify all purposes of attention. We implemented a semi-autonomous GAC system into a communication robot that guides visitors to exhibits in a science museum and engages in free-play interactions with them. The GAC system's effectiveness was demonstrated in a two-week experiment in the museum. We believe these results will allow us to develope interactive humanoid robots that can interact effectively with groups of people.

This paper reports a study that estimated visitor positions, visiting patterns, and inter-human relationships at a science museum using information from RFID readers. In the science museum, we exhibited humanoid robots. Visitors were invited to wear RFID tags to interact with the robots. Visitor behavior was simultaneously observed using 20 RFID readers, distributed throughout the entire floor, that roughly measured the distances of nearby tags. We integrated the outputs from all RFID readers to estimate visitor trajectories that were used to analyze three perspectives: space, visiting patterns, and relationships. Regarding space, we identified crowded and uncrowded areas. We found several typical visiting patterns, such as visited at every exhibit and directly going to robot area. We also identified atypical visiting behavior. Regarding relationships, for example, we estimated 68% coverage of group-member relationships with 91% reliability.

This paper reports on a field trial with interactive humanoid robots at a science museum where visitors are supposed to study and develop an interest in science. In the trial, each visitor wore an RFID tag while looking around the museum's exhibits. Information obtained from the RFID tags was used to direct the robots' interaction with the visitors. The robots autonomously interacted with visitors via gestures and utterances resembling the free play of children [1]. In addition, they performed exhibit-guiding by moving around several exhibits and explaining the exhibits based on sensor information. The robots were highly evaluated by visitors during the two-month trial. Moreover, we conducted an experiment in the field trial to compare the detailed effects of exhibit-guiding and free-play interaction under three operating conditions. This revealed that the combination of the free-play interaction and exhibit-guiding positively affected visitors' experiences at the science museum.

This paper introduces a "teleoperated communication robot" whose unique point is that its language component is performed by an operator to avoid the automatic recognition difficulty of spoken language. On the other hand, conceptually, its nonverbal behavior will be autonomously controlled. One important point is to identify whether ordinary people will accept this type of communication robot. Therefore, we conducted a preliminary field trial at a shopping center, which revealed positive perspectives as well as technical problems to be solved

We propose a method for promoting human-robot interaction based on emotion recognition with particular focus on tension emotion. There are two types of emotions expressed in a short time. One is autonomic emotion caused by a stimulus, such as joy and fear. The other is self-reported emotion, such as tension, that is relatively independent of a single stimulus. In our preliminary experiment, we observed that tension emotion (self-reported emotion) obstructs the expression of autonomic emotion, which has demerits on speech recognition and interaction. Our method is based on detection and moderation of tension emotion. If a robot detects tension emotion, it tries to ease it so that a person will interact with it more comfortably and express autonomic emotions. It also retrieves nuances from expressed emotions for supplementing insufficient speech recognition, which will also promote interaction.

This paper reports progress in the development of a humanoid robot designed for real-time face-to-face interaction with humans. An essential component for face-to-face interaction with humans is being able to find faces, tracking them, and smoothly moving back and forth between gazing to a face and other objects of interest. In this paper we propose a system that integrates peripheral vision and foveal vision in a principled manner using particle filters. The developed system generates hypotheses about face position by using peripheral vision and verifies them by integrating peripheral vision and foveal vision. Even though a face may not be present in foveal vision, while the robot is gazing at another object, it keeps plausible hypotheses about the location of the human face with peripheral vision and restarts the face-following by verifying the hypothesis later. Moreover, this fundamental function provides more rich sensory information for human-robot interaction, such as a human's facial expression and lip motions during utterances.