Civic technology platforms are increasingly developed through collaborative processes involving diverse stakeholders. Yet existing participatory design literature offers limited theoretical attention to collaborative trajectories that deviate from a canonical model in which co-design is structured and intentional from the outset. This paper presents a reflective account of the development of “dédédé”, an online platform that invites citizens to informally document and share observations about their neighborhoods. The platform was initially conceived and developed by a team of technologists, then evolved through two co-design phases involving progressively broader stakeholder communities: first with urban designers and other practitioners experienced in facilitating machiaruki (“town-walking”) workshops, then with city officials and real estate developers interested in leveraging the platform for machizukuri (“town-building”) efforts. We show that these phases produced qualitatively distinct collaborative dynamics: the first characterized by receptive redesign, in which domain expertise corrected the implicit assumptions of the technical team; the second by active negotiation and resistance, in which the design knowledge accumulated in the first phase became a resource (design anchor) for mediating competing stakeholder visions. We argue that such staged co-design processes represent a recognizable and undertheorized mode of civic technology development, with distinct benefits and risks.

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.

Teleoperated customer service robots have attracted attention to improve customer service efficiency. However, operators experience boredom during long-duration operation due to monotony and idle time, leading to decreased task motivation. This study proposes and evaluates a method to reduce operator boredom through dialogue with the robot to be operated. Field experiments demonstrated that operator-robot dialogue significantly reduced boredom and contributed to maintaining task engagement during long-duration operation.

In this paper, we aim to understand how user motivation shapes human-robot interaction (HRI) in the wild. To explore this, we conducted a field study by deploying a fully autonomous conversational robot in a shopping mall over two days. Through sequential video analysis, we identified five patterns of interaction fluency (Smooth, Awkward, Active, Messy, and Quiet), four types of user motivation for interacting with the robot (Function, Experiment, Curiosity, and Education), and user positioning towards the robot. We further analyzed how these motivations and positioning influence interaction fluency. Our findings suggest that incorporating users' motivation types into the design of robot behavior can enhance interaction fluency, engagement, and user satisfaction in real-world HRI scenarios.

In recent years, numerous studies have been conducted on dialogue robots powered by large language models,enabling sophisticated interactions such as providing guidance and engaging in small talk. However, the interaction performance remains imperfect, and the robots sometimes cause problems during interactions. In this study, we aim to automatically detect such anomalies in human-robot interactions by creating a dataset and developing anomaly detection models. To this end, we created a dataset by manually annotating videos of in-the-wild interactions collected from our field experiment designed to test a framework of parallel conversations in which a human intervenes when a problem occurs in the interaction. Using this dataset, we trained classification models to construct anomaly detection models. We then conducted another field experiment in which the model’s detection results were presented as alerts to operators within the parallel conversation framework. The results confirmed that providing alerts on the basis of the anomaly detection model was useful for facilitating operator intervention.

Effective teleoperated avatar communication requires expressing social behaviors. Gaze behavior is one of the crucial social behaviors and includes reflexive reactions to the avatar’s surroundings and intentional responses to the operator’s speech and actions. Teleoperated avatars must have their gaze behavior controlled according to situational changes in both the avatar’s and operator’s contexts. However, it is not clear how to adjust the avatar’s gaze in response to changes in both situations. In this paper, we collect a dataset of gazing positions that the avatar is instructed to face, taking into account both avatar and operator situations, and annotation labels that represent both situations in detail. We then exploratorily analyze the ratio of gazing positions per situation through dynamic area-of-interest (AOI) analysis. Our analysis provides insights into determining the gaze behavior of teleoperated avatars.

In this study, we developed POP Cart that uses a shopping cart and an agent to recommend products. The agent on POP Cart is designed to call the customer by name, chatting and making recommendations to the customer casually like a friend. POP Cart has two advantages: first, the agent can form a positive relationship with the customer is in the store, which could be beneficial for a high recommendation success rate, as found in previous studies. Second, the agent can make multiple recommendations for different products according to the position of the customer while they are shopping in a large store. To evaluate the effectiveness of the recommendations made by the POP Cart agent, we conducted a field experiment in a real supermarket in Japan, where 49 participants shopped under three cart conditions. The results revealed that having an agent on a shopping cart is an effective way to recommend and sell products.