This study investigates the role of robot bowing in trust repair, focusing on how human-like movement impacts trust in human-robot interaction. We manipulated the movement quality across four conditions: human-like, constant-speed, abrupt, and no-movement. Specifically for the human-like movement, we analyzed the Japanese hospitality (called “omotenashi”) gesture, which expresses attentiveness and respect to others and was translated into robot bowing movement. The experimental results indicated that human-like movement did not significantly affect trust repair, while constant-speed and abrupt movements showed improvements in subjective trust. The study highlighted the need for movement designs with considerations of the robot's appearance to facilitate effective trust repair.

Trust, the cornerstone of human-robot interaction, is a key element in fostering a synergistic human-robot relationship. Trust facilitates the appropriate utilization of these systems, thereby optimizing their potential benefits. A failure to appropriately gauge the level of trust in a robot can have grave consequences, including potential misuse and accidents, underscoring the critical importance of accurate trust assessment in fostering a harmonious and safe human-robot collaboration. To avert such issues, it is imperative to calibrate trust levels accurately. To address this need, we have developed a novel estimation model for trust, leveraging the capabilities of structural equation modeling (SEM) to address the challenges posed by latent variables. The proposed model demonstrated a 70% accuracy in estimating trust during a manipulator’s successful and failed behaviors with uncertainty. The outcomes demonstrate the efficacy of the proposed method in surpassing conventional approaches.

Service robots are now common in restaurant food delivery. However, machines aren’t always stable and may encounter errors, causing a loss of trust. Typically, when people make an error, they apologize and are forgiven. We were curious about how people react when a service robot makes an error and apologizes. Before checking trust in a participant, we aimed to check for manipulative behavior and investigate which motions are suitable for apologetic gesture such as bowing, using non-anthropomorphic robot. We conducted a web-based experiment with two-way ANOVA using a 2 x 5 (End-effector: Robot hand, Human hand; Motions: 5 different motions) within-participant design. Participants indicated a willingness to accept machine apologies, but we explored how trust could change. This study contributes insights into human-robot interactions, probing the acceptance of service robots in various roles and the impact of error and apology on trust.

Today, people are enjoying using teleconference systems like Zoom or Skype for social communication or for having drinks through a screen with others via the internet. In addition, some people have started using embodied systems called telepresence robots, such as the Beam robot. Some schools have started using telepresence robots so that students can attend school. However, in previous studies, systems have not been compared in terms of social presence and anthropomorphism, for example, robots compared with humans. Therefore, we wondered how the presence and anthropomorphism of such systems affect people. Therefore, we carried out a web-based experiment and conducted a one-way ANOVA (smartphone vs. telepresence robot with motion vs. without motion). Some people feel that telepresence robots bring a feeling of presence to remote places. Ironically, from the results, a video teleconference system using a smartphone and a telepresence robot did not create a feeling of presence, but regarding anthropomorphism, participants felt more of a human-likeness in the video teleconference system.