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.

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.

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.

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.

In this paper, we report that how much the presence of a robot increases the rate of valid responses in a questionnaire survey. We conducted a field trial in a university campus based on a single-case design method that is used in applied behavior analysis. We made two conditions: One was a condition that a robot interacting with people was installed (robot condition), and another was a condition that the robot was not installed (no-robot condition). We measured once a day for eight days, each day lasting 125 minutes from 11:25 to 13:30. The robot condition and the no-robot condition were assigned alternately to each day. The robot was controlled by an operator. The results showed that the valid response rates of the robot condition were 8.9%, 3.8%, 6.1%, and 1.9%, and those of the no-robot condition were 4.9%, 3.7%, 3.1%, and 1.8%. Considering both these results and complete answer rates, we found that although a robot can attract people’s attention and increase the response rates a little and short-term, the valid response rates do not increase so much as we expect because people who are attracted by the robot are likely to quit answering the questionnaire halfway. In order to increase the valid response rates, we will need to consider a new interaction design for preventing people from quitting their answer.

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.

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.

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 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.

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.

Since a talking robot cannot escape from errors in recognizing user's speech in daily environment, its verbal responses are sometimes felt as incoherent with the context of conversation. This paper presents a solution to this problem that generates a social context where a user is guided to find coherency of the robot's utterances, even though its response is produced according to incorrect recognition of user's speech. We designed a novel turn-taking pattern in which two robots behave according to a pre-scheduled scenario to generate such a social context. Two experiments proved that participants who talked to two robots using that turn-taking pattern felt robot's responses to be more coherent than those who talked to one robot not using it; therefore, our proposed turn-taking pattern generated a social context for user's flexible interpretation of robot's responses. This result implies a potential of a multiple robots approach for improving the quality of human-robot conversation.

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.

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 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.

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.

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.

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.