International Journal of Social Robotics最新文献

Avatar robots enable the teleoperation and telepresence of an operator with a rich and meaningful sense of existence in another location. Robotic avatar systems rely on intuitive interactions to afford operators comfortable and accurate robot control to perform various tasks. The ability of operators to feel immersed within a robot has drawn interest in multiple research fields to explore the future capabilities of such systems. This paper presents a robotic avatar system based on a custom humanoid robot, TOCABI, with a mobile base. Its teleoperation system was developed in response to the ANA Avatar XPRIZE. Combining the life-size humanoid robot and the mobile base allows for improved mobility and dexterous manipulation. The robotic avatar system comprises the robot/base and an operator station that incorporates haptic feedback devices, trackers, a head-mounted display, gloves, and pedals. These devices connect the robot-environment interaction and operator-avatar robot experience through visual, auditory, tactile, haptic, and kinesthetic feedback. Combining the untethered battery-operated and Wi-Fi-enabled robot with these sensory experiences enables intuitive control through the operator’s body movement. The performance of the robotic avatar system was evaluated through user studies and demonstrated in the ANA Avatar XPRIZE Finals, represented by Team SNU, where it completed 8 of the 10 missions, placing the team eighth among the 17 finalists.

Currently, collaborative robots (cobots) are mostly programmed to do one task repetitively. They can be programmed at different speeds and work near human operators. The goal of our research was to investigate the effect of robot speed on acceptance, subjective and objective stress, and cognitive workload of individuals. Therefore, we organized a repeated measures experiment in which participants (N = 25) conducted an assembly task with the YuMi cobot from ABB at a low and at a high speed. Subjective and physiological responses were collected, and participants were subjected to a standardized stress test. Our results indicate that when working with a cobot at a high speed, people believe they can work faster and be more productive but also experience a higher workload and higher perceived stress. We also found that tonic EDA is a significant physiological predictor for monitoring perceived stress in humans. We observed a greater relative increase in tonic EDA from baseline to task execution during high-speed mode compared to low-speed mode. Additionally, this increase in tonic EDA significantly correlated with participants’ perceived stress levels. However, workload could not be predicted by any of the physiological measures. Future research should explore the effect of higher cobot working speeds and the use of physiological measures (such as stress) as input to guide the collaboration between individuals and cobots.

Intergenerational interactions between children and older adults are gaining broader recognition because of their mutual benefits. However, such interactions face practical limitations owing to potential disease transmission and the poor health of older adults for face-to-face interactions. This study explores robot-mediated interactions as a potential solution to address these issues. In this study, older adults remotely controlled a social robot to perform a health-screening task for nursery school children, thereby overcoming the problems associated with face-to-face interactions while engaging in physical interactions. The results of this study suggested that the children responded favorably to the robot, and the rate of positive response increased over time. Older adults also found the task generally manageable and experienced a significant positive shift in their attitude toward children. These findings suggest that robot-mediated interactions can effectively facilitate intergenerational engagement and provide psychosocial benefits to both the parties to the engagement. This study provides valuable insights into the potential of robot-mediated interactions in childcare and other similar settings.

Research on children’s anthropomorphism of social robots is mostly cross-sectional and based on a single measurement. However, because social robots are new type of technology with which children have little experience, children’s initial responses to social robots may be biased by a novelty effect. Accordingly, a single measurement of anthropomorphism may not accurately reflect how children anthropomorphize social robots over time. Thus, we used data from a six-wave panel study to investigate longitudinal changes in 8- to 9-year-old children’s anthropomorphism of a social robot. Latent class growth analyses revealed that anthropomorphism peaked after the first interaction with the social robot, remained stable for a brief period of time, and then decreased. Moreover, two distinct longitudinal trajectories of anthropomorphism could be identified: one with moderate to high anthropomorphism and one with low to moderate anthropomorphism. Previous media exposure to non-fictional robots increased the probability that children experienced higher levels of anthropomorphism.

In recent years, the research of humanoid robots that can change users’ opinions has been conducted extensively. In particular, two robots have been found to be able to improve their persuasiveness by cooperating with each other in a sophisticated manner. Previous studies have evaluated the changes in opinions when robots showed consensus building. However, users did not participate in the conversations, and the optimal strategy may change depending on their prior opinions. Therefore, in this study, we developed a system that adaptively changes conversations between robots based on user opinions. We investigate the effect on the change in opinions when the discussion converges to the same position as the user and when it converges to a different position. We conducted two subject experiments in which a user and virtual robotic agents talked to each other using buttons in a crowded setting. The results showed that users with confidence in their opinions increased their confidence when the robot agents’ opinions converged to the same position and decreased their confidence when the robot agents’ opinions converged to a different position. This will significantly contribute to persuasion research using multiple robots and the development of advanced dialogue coordination between robots.

In recent years, explanations have become a pressing matter in AI research. This development was caused by the increased use of black-box models and a realization of the importance of trustworthy AI. In particular, explanations are necessary for human–agent interactions to ensure that the user can trust the agent and that collaborations are effective. Human–agent interactions are complex social scenarios involving a user, an autonomous agent, and an environment or task with its own distinct properties. Thus, such interactions require a wide variety of explanations, which are not covered by the methods of a single AI discipline, such as computer vision or natural language processing. In this paper, we map out what types of explanations are important for human–agent interactions, surveying the field via a scoping review. In addition to the typical introspective explanation tackled by explainability researchers, we look at assistive explanations, aiming to support the user with their task. Secondly, we survey what causes the need for an explanation in the first place. We identify a variety of human–agent interaction-specific causes and categorize them by whether they are centered on the agent’s behavior, the user’s mental state, or an external entity. Our overview aims to guide robotics practitioners in designing agents with more comprehensive explanation-related capacities, considering different explanation types and the concrete times when explanations should be given.

The need for safe, predictable, and reliable robot navigation is fundamental for mobile robots to move around in home and office environments. Shortest-path navigation is a popular robot navigation method that uses the most efficient path to get to the desired goal. This behaviour is not always easy to interpret, understand, and avoid in a congested hallway. Instead, more predictable navigation methods, such as a robot following a wall, can help increase social acceptance and help avoid the robot crossing the pedestrian path. If a robot follows along a wall, a key variable to consider is the preferred driving side of the robot (left or right) in areas such as in narrow passages, and its perceived impact on social acceptance. This international user study (n = 143) involved an online video-based test to compare robot evaluation and social acceptance for two types of mobile navigation (Wall-Following and Shortest Path), including the preferred driving side for Wall-Following. A Fetch robot navigated from start to goal position in a series of indoor scenarios with a pedestrian. Select scenarios included a hallway, doorway, and intersection. Independent Sample T-Tests results found that Wall-Following was rated significantly higher than Shortest Path for being perceived as more comfortable and predictable, regardless of robot driving side. The preference for the driver side of the robot did not match the country of residence, nor did it have a significant impact on robot ratings. There were significant interaction effects for comfort, safety and predictable scores across two timepoints. Given the popularity of Shortest Path navigation, the findings indicate that this approach might not be the most appropriate for human settings. Additional investigation into Wall-Following behaviours is recommended for social acceptance, even if the method compromises the efficiency of the robot to acheive its objective.

The concept of engagement is widely adopted in the human–robot interaction (HRI) field, as a core social phenomenon in the interaction. Despite the wide usage of the term, the meaning of this concept is still characterized by great vagueness. A common approach is to evaluate it through self-reports and observational grids. While the former solution suffers from a time-discrepancy problem, since the perceived engagement is evaluated at the end of the interaction, the latter solution may be affected by the subjectivity of the observers. From the perspective of developing socially intelligent robots that autonomously adapt their behaviors during the interaction, replicating the ability to properly detect engagement represents a challenge in the social robotics community. This systematic review investigates the conceptualization of engagement, starting with the works that attempted to automatically detect it in interactions involving robots and real users (i.e., online surveys are excluded). The goal is to describe the most worthwhile research efforts and to outline the commonly adopted definitions (which define the authors’ perspective on the topic) and their connection with the methodology used for the assessment (if any). The research was conducted within two databases (Web of Science and Scopus) between November 2009 and January 2023. A total of 590 articles were found in the initial search. Thanks to an accurate definition of the exclusion criteria, the most relevant papers on automatic engagement detection and assessment in HRI were identified. Finally, 28 papers were fully evaluated and included in this review. The analysis illustrates that the engagement detection task is mostly addressed as a binary or multi-class classification problem, considering user behavioral cues and context-based features extracted from recorded data. One outcome of this review is the identification of current research barriers and future challenges on the topic, which could be clustered in the following fields: engagement components, annotation procedures, engagement features, prediction techniques, and experimental sessions.

This article introduces the design and assessment of a robotic assistant aimed at generating therapeutic interventions for individuals with ASD. A uniform oval-shaped structure was considered for the design, featuring two arms, leg-like wheels, and a facial screen for displaying facial expressions. Six basic emotional states were generated, enriched with facial expressions, colors, sounds, and movements, aiming to emulate human nonverbal language to the greatest extent possible. The construction process was executed using additive manufacturing technology, specifically 3D printing. Once the functional prototype was developed, its appearance, therapeutic usability, generation of emotional states, and social skill development were evaluated through structured surveys on a Likert Scale. The evaluation took place in two stages: (a) expert peer evaluation, involving 5 experts in ASD, with consensus levels determined using Kendall’s Coefficient of Concordance ((omega )), that show a susbtantial agreement ((omega =0.709)) regarding the robot appearance and slight agreement ((omega =0.183)) in mood generation; (b) perception assessment with individuals who work daily with people with ASD, with 36 participants, and survey validation through Cronbach’s Alpha ((alpha = 0.94)), followed by results analysis using descriptive statistics, which indicates that the robot appearance is suitable for the majority of evaluators, but they differ in the robot dimensions. Outcomes highlighted the robotic assistant’s specific characteristics that warrant adjustments before piloting with ASD individuals. This study presents a replicable protocol for the preliminary evaluation of any technological support geared towards therapeutic interventions, preceding experimental processes involving the target audience.

Social, anthropomorphic robots are increasingly used in professional work environments to collaborate with humans. However, little is known about how these robots affect human workers in performance-critical aspects, such as feedback. The present study investigates differences between the effects of a robot and a human feedback giver on self-esteem, intrinsic motivation, and psychophysiological reactions. Using a mixed model design for subjective data and a between-subject design for psychophysiological data, we tested 72 participants who performed a cognitive task on working memory, namely the 3-back task. The results indicate that people are more motivated to perform the task when receiving feedback from a robot, but their electrodermal activity and heart rate are higher after receiving positive feedback from a human. There is no difference in electrodermal activity following negative feedback from a human or a robot. Additional analyses show that individuals report feeling less comfortable and perceiving less social warmth when receiving feedback from a robot compared to a human. Furthermore, individuals exhibit higher skin conductance responses when perceiving greater social warmth in their interactions, regardless of whether their interaction partner is a human or a robot. The results suggest that social robots may serve as surrogates for social interaction. However, they seem to have less social presence, which leads to reduced psychophysiological reactions. This knowledge may be used to calibrate arousal in feedback situations.