Recent research has focused on developing non-task-oriented dialogue agents with high anthropomorphism, aiming to sustain users’ dialogue motivation over prolonged periods. Previous studies have introduced chatbots that deliberately incorporate short response delays to enhance anthropomorphism and user satisfaction. However, in the context of long-term casual conversations, the behavior of these chatbots, which reply within a few seconds, may lead to a perception of the chatbot as always on standby, lacking its own schedule or personal life, thus appearing less humanlike. To address this issue, we propose a non-real-time chatbot that interacts with users while introducing deliberate response delays on a larger time scale, ranging from a few minutes to several hours. A five-day dialogue experiment confirmed that the non-real-time chatbot was perceived as more humanlike and encouraged users to engage in extensive dialogue. These insights are invaluable for designing chatbots that can maintain casual conversations over prolonged periods.

Improving motor performance is essential for sustaining long-term sports participation. In this study, we proposed a novel observational learning scheme for full-body sports movements, aiming to enhance its effectiveness by inducing a kinesthetic illusion. To implement this scheme, we developed a virtual reality (VR) system which can apply synchronized visuo-motor and visuo-tactile stimuli. In an experiment, participants who experienced the proposed scheme reported significantly higher perceived synchrony and subjectively rated their ball trajectory accuracy as improved after they performed actual golf driver swings. These findings suggest that our proposed observational learning scheme has the potential to support performance improvement in full-body sports movements.

Complaint sharing is a common communicative strategy in human–human interaction, known to foster intimacy and strengthen social bonds. Building on this insight, we proposed a robot that shares complaints with users to promote intimate relationship. Through an online experiment with 210 participants, we found that moderate complaint sharing fosters intimacy similar to not complaining, while also enhancing conversational engagement. In contrast, aggressive complaint sharing evokes discomfort, is perceived as ethically inappropriate, and fails to build intimacy. These findings highlight the potential and risks of complaint sharing, and provide valuable insights for designing robots that build intimate relationships with users.

The use of dialogue agents with a strategy that focuses beyond a static depth level and gradually deepens the conversation over time can nurture relationships with users. Thus, this study aimed to develop a dialogue agent capable of gradually deepening conversations over time and consequently examine its effectiveness. First, we constructed a topic depth estimation model capable of estimating the depth of any given topic. Subsequently, we developed a dialogue agent designed to gradually deepen conversation topics as interactions progress, tailored for operations on users’ smartphones. To evaluate the effectiveness of the developed agent, we conducted a 10-day dialogue experiment with 26 participants. The results indicated that the proposed agent elicited deep self-disclosure from users without significantly increasing psychological reluctance compared to the control condition. Moreover, the participants perceived a deeper relationship with the agent and were less likely to consider it as a non-human entity. These findings suggest that the proposed dialogue agent was capable of effectively engaging in deeper conversations over time, thereby enhancing human–agent interactions.

With recent technological developments, such as machine learning, data collection has become increasingly important. However, due to privacy issues, there is an ethical problem in collecting data on everyday activities. In this study, we aimed to develop a mechanism for acquiring consent interactively by using a robot and, as a first step, focus on a dialogue strategy in which users feel safe to provide their data. Specifically, we investigated the effects of reminder and rationale provided by the robot in situations where consent is obtained. We conducted two experiments in which users conversed with a virtual robot agent in a crowd setting. We found that the robot reminder improved the degree of understanding regarding the use of data. In addition, the permission rate was improved by robot notifications. However, no effect of rationale was observed. These results contribute to the discussion on the ethical aspects of data collection by robots.

In recent years, a substantial amount of research has been aimed at realizing a social robot that can maintain long-term user interest. One approach is using a dialogue strategy in which the robot makes a remark based on previous dialogues with users. However, privacy problems may occur owing to private information of the user being mentioned. We propose a novel dialogue strategy whereby a robot mentions another robot in the form of gossiping. This dialogue strategy can improve the sense of conversation, which results in increased interest while avoiding the privacy issue. We examined our proposal by conducting a conversation experiment evaluated by subject impressions. The results demonstrated that the proposed method could help the robot to obtain higher evaluations. In particular, the perceived mind was improved in the Likert scale evaluation, whereas the robot empathy and intention to use were improved in the binary comparison evaluation. Our dialogue strategy may contribute to understanding the factors regarding the sense of conversation, thereby adding value to the field of human-robot interaction.