We developed an autonomous human-like guide robot for a science museum. Its identifies individuals, estimates the exhibits at which visitors are looking, and proactively approaches them to provide explanations with gaze autonomously, using our new approach called speak-and-retreat interaction. The robot also performs such relation-building behaviors as greeting visitors by their names and expressing a friendlier attitude to repeat visitors. We conducted a field study in a science museum at which our system basically operated autonomously and the visitors responded quite positively. First-time visitors on average interacted with the robot for about 9 min, and 94.74% expressed a desire to interact with it again in the future. Repeat visitors noticed its relation-building capability and perceived a closer relationship with it.

As agents, social robots are expected to increase opportunities for dialogue with the elderly. However, it is difficult to sustain a dialogue with an elderly user because speech recognition frequently fails during the dialogue. Here, to overcome this problem, regardless of speech recognition failure, we developed a question–answer–response dialogue model. In this model, a robot took initiative in the dialogue by asking the user various questions. Moreover, to improve user experience during dialogue, we extended the model such that two robots could participate in the dialogue. Implementing these features, we conducted a field trial in a nursing home to evaluate the twin-robot dialogue system. The average word error rate of speech recognition was 0.778. Despite the frequently high number of errors, participants talked for 14 min in a dialogue with two robots and felt slightly strange during the dialogue. Although we found no significant difference between a dialogue with one robot and that with two robots, the effect size of the difference in the dialogue time with one robot and that with two robots was medium (Cohen’s d = −0.519). The results suggested that the presence of two robots might likely encourage elderly people to sustain the talk. Our results will contribute to the design of social robots to engage in dialogues with the elderly.

This paper reports how robot‐assisted language learning (RALL) impacts Japanese adults' English speaking skills. With existing research on RALL focusing on children, there is little evidence indicating RALL's effects on adults. We developed a RALL system comprising a robot, a tablet, and designed learning materials for speaking practice. To explore the effects of this system on the speaking skills of adults, we conducted a pretest and posttest designed pilot experiment. The participants in this study were nine female Japanese university students. The participants used the system for speaking practice for a total of 30 min per day for 7 days. The results of comparing the pretests with the posttests demonstrated that the adults' English speaking skills were enhanced; in particular, their speaking accuracy, fluency, and pronunciation improved greatly. This study contributes to providing clear evidence of the effects of RALL on Japanese adults' English speaking skills.

Many social robots have emerged in public places to serve people. For these services, the robots are assumed to be able to present internal aspects (i.e., mind, sociability) to engage and interact with people over the long term. In this paper, we propose a novel dialogue structure called experience-based dialogue to help a robot present and maintain a good interaction over the long term. This dialogue structure contains a piece of knowledge and a story about how the robot gained this knowledge, which are used to compose the robot’s experience-related utterances for sharing experiences of interacting with previous users other than just the current user and help it present its internal aspects. We conducted an experiment to test the effects of our proposed dialogue structure and measure them with some published subjective scales. The results showed that experience-based dialogue can help a robot obtain better evaluations in terms of perceived intelligence, sociability, mind, anthropomorphism, animacy, likability, level of acceptance, and positive user reaction.

This paper reports the effects of praise from two robots on children’s learning time, their performance, and their acceptance of the robots. Since praise from a single robot provides positive effects for children as educational support, we hypothesized that praise from two robots will increase such effects because two robots will enhance the social influences. To verify this hypothesis, we developed an English learning system that consisted of an e-learning application, a depth sensor for human-tracking, a tabletop humanoid robot, a doll-type robot, and an operator. We designed the robots to interactively praise children during their learning situations and experimented with a with-in participant design to compare the effects of praise from two robots and just one. 22 children participated in an experiment whose results showed that children learned more when two robots praised them than just one, even with an identical amount of praise. These results suggest the effectiveness and empirical evidence of using two robots for educational support. GRAPHICAL ABSTRACT

Social connectedness is vital for developing group cohesion and strengthening belongingness. However, with the accelerating pace of modern life, people have fewer opportunities to participate in group-building activities. Furthermore, owing to the teleworking and quarantine requirements necessitated by the Covid-19 pandemic, the social connectedness of group members may become weak. To address this issue, in this study, we used an android robot to conduct daily conversations, and as an intermediary to increase intra-group connectedness. Specifically, we constructed an android robot system for collecting and sharing recent member-related experiences. The system has a chatbot function based on BERT and a memory function with a neural-network-based dialog action analysis model. We conducted a 3-day human-robot conversation experiment to verify the effectiveness of the proposed system. The results of a questionnaire-based evaluation and empirical analysis demonstrate that the proposed system can increase the familiarity and closeness of group members. This suggests that the proposed method is useful for enhancing social connectedness. Moreover, it can improve the closeness of the user-robot relation, as well as the performance of robots in conducting conversations with people.