International Journal of Social Robotics最新文献

Communication among some older adults is affected by cognitive and mobility impairments. This increases isolation, particularly for those residing in care homes, and leads to accelerated cognitive decline. Previous research has leveraged assistive robots to promote recreational routines and communication among older adults, with the robot leading the interaction. However, older adults could have more agency in the interaction, as robots could extend elders’ intentions and needs. Therefore, we explored an approach whereby the robot’s agency is shifted to the older adults who lead the interaction by commanding a robot’s actions using interactive physical blocks (tangible blocks). We conducted sessions with 22 care home dwellers where they could exchange messages and objects using the robot. Based on older adults’ observed behaviors during the sessions and perspectives gathered from interviews with geriatric professionals, we reflect on the opportunities and challenges for increased user agency and the asymmetries that emerged from differing abilities and personality traits. Our qualitative results highlight the potential of robotic approaches to extend the agency and communication of older adults, anchored on human values, such as the exchange of affection, collaboration, and competition.

Examining personality traits can enhance the likelihood of a successful interaction between humans and robots in forthcoming work settings. Employing the emic/etic approach stands out as a crucial method for investigating personality types in the context of future environments. Currently, no study has explored the impact of this approach on individuals’ willingness to engage with a robot. In the present study, our aim is to determine whether emic characteristics can influence the connection between etic traits and the willingness to use a robot. In the current study, 367 male workers participated. All data were collected using valid and reliable questionnaires. The Five-Factor model of personality was regarded as etic personality characteristics, while the moderating roles of technology affinity and STARA were assessed as emic personality characteristics. The analytical process followed the method presented by Hayes et al. for analyzing moderators. Technology affinity, as a primary emic factor, exerts a moderating influence on the association between neuroticism, openness, agreeableness, conscientiousness, and the willingness to use robots. Conversely, STARA serves as a mediator exclusively in the relationship with neuroticism among workers. Notably, extroversion does not exhibit mediation with any of the emic factors. Both emic and etic personality characteristics were recognized as significant facilitators of the inclination to use robots. In addition to technology affinity and STARA, it is advisable to explore new emic traits and their interactive effects with etic personality characteristics.

This study explores how much current mainstream Robot-Assisted Language Learning (RALL) systems produce outcomes compared to human tutors instructing a typical English conversation lesson. To this end, an experiment was conducted with 26 participants divided in RALL (14 participants) and human tutor (12 participants) groups. All participants took a pre-test on the first day, followed by 30 min of study per day for 7 days, and 3 post-tests on the last day. The test results indicated that the RALL group considerably improved lexical/grammatical error rates and fluency of speech compared to that for the human tutor group. The other characteristics, such as rhythm, pronunciation, complexity, and task achievement of speech did not indicate any differences between the groups. The results suggested that exercises with the RALL system enabled participants to commit the learned expressions to memory, whereas those with human tutors emphasized on communication with the participants. This study demonstrated the benefits of using RALL systems that can work well in lessons that human tutors find hard to teach.

In recent years, applications of social robots as the operator’s avatar have been widely studied for remote conversation with rich nonverbal information. Having another side-participant robot beside the avatar robot of the operator was found to be effective for providing long-lasting backchannels to the interlocutor. The side-participant robot is also expected to play a role in assisting human participation in multiparty conversations. However, such a focus has not been applied to remote conversations with multiple robots. Here, we propose a multiple-robot telecommunication system with which the operator can use a side-participant robot to assist conversation that is developed by the operator through the main speaker robot to verify its effectiveness. In the laboratory experiment where the subjects were made to feel stressed by being forced to provide rude questions to the interlocutor, the proposed system was shown to reduce guilt and to improve the overall mood of operators. The result encourages the application of a multi robot remote conversation system to allow the user to participate in remote conversations with less anxiety of potential failure in maintaining the conversation.

As robots become more common, people interact with them individually, with strangers, and with friends. For example, when coming across a robot in a mall, a family might ask it for instructions. An individual person might hesitate to interact with the robot until they see another person interacting, and then explore the robot together. Although human–robot interaction (HRI) research has recently uncovered the importance of examining differences in group behavior toward robots versus individuals’ behavior, thus far, most HRI research has not distinguished behavior based on group type (e.g., stranger, companion). In this online lab-based study, we explore how individuals, strangers, and companions collaborate with robot teammates. We test competing hypotheses: (1) More cohesive companion groups will form a human subgroup and exclude the robots more than strangers or individuals, vs. (2) More cohesive companion groups will provide social support to interact better with the novel robotic technology than strangers or individuals. In this cooperative context in which participants were required to interact with the robot, results supported H1: the subgroup hypothesis. Based on these findings, people deploying robots should note that if people are required to interact with the robots, the interactions may not go as smoothly for companion groups compared to stranger groups or individuals.

This study examined the effects of explanation strategy (global explanation vs. deductive explanation vs. contrastive explanation) and autonomy level (high vs. low) of explainable agents on human–AI collaborative decision-making. A 3 × 2 mixed-design experiment was conducted. The decision-making task was a modified Mahjong game. Forty-eight participants were divided into three groups, each collaborating with an agent with a different explanation strategy. Each agent had two autonomy levels. The results indicated that global explanation incurred the lowest mental workload and highest understandability. Contrastive explanation required the highest mental workload but incurred the highest perceived competence, affect-based trust, and social presence. Deductive explanation was found to be the worst in terms of social presence. The high-autonomy agents incurred lower mental workload and interaction fluency but higher faith and social presence than the low-autonomy agents. The findings of this study can help practitioners in designing user-centered explainable decision-support agents and choosing appropriate explanation strategies for different situations.

Robots in multi-user environments require adaptation to produce personalized interactions. In these scenarios, the user’s feedback leads the robots to learn from experiences and use this knowledge to generate adapted activities to the user’s preferences. However, preferences are user-specific and may suffer variations, so learning is required to personalize the robot’s actions to each user. Robots can obtain feedback in Human–Robot Interaction by asking users their opinion about the activity (explicit feedback) or estimating it from the interaction (implicit feedback). This paper presents a Reinforcement Learning framework for social robots to personalize activity selection using the preferences and feedback obtained from the users. This paper also studies the role of user feedback in learning, and it asks whether combining explicit and implicit user feedback produces better robot adaptive behavior than considering them separately. We evaluated the system with 24 participants in a long-term experiment where they were divided into three conditions: (i) adapting the activity selection using the explicit feedback that was obtained from asking the user how much they liked the activities; (ii) using the implicit feedback obtained from interaction metrics of each activity generated from the user’s actions; and (iii) combining explicit and implicit feedback. As we hypothesized, the results show that combining both feedback produces better adaptive values when correlating initial and final activity scores, overcoming the use of individual explicit and implicit feedback. We also found that the kind of user feedback does not affect the user’s engagement or the number of activities carried out during the experiment.

Annotation tools serve a critical role in the generation of datasets that fuel machine learning applications. With the advent of Foundation Models, particularly those based on Transformer architectures and expansive language models, the capacity for training on comprehensive, multimodal datasets has been substantially enhanced. This not only facilitates robust generalization across diverse data categories and knowledge domains but also necessitates a novel form of annotation—prompt engineering—for qualitative model fine-tuning. This advancement creates new avenues for machine intelligence to more precisely identify, forecast, and replicate human behavior, addressing historical limitations that contribute to algorithmic inequities. Nevertheless, the voluminous and intricate nature of the data essential for training multimodal models poses significant engineering challenges, particularly with regard to bias. No consensus has yet emerged on optimal procedures for conducting this annotation work in a manner that is ethically responsible, secure, and efficient. This historical literature review traces advancements in these technologies from 2018 onward, underscores significant contributions, and identifies existing knowledge gaps and avenues for future research pertinent to the development of Transformer-based multimodal Foundation Models. An initial survey of over 724 articles yielded 156 studies that met the criteria for historical analysis; these were further narrowed down to 46 key papers spanning the years 2018–2022. The review offers valuable perspectives on the evolution of best practices, pinpoints current knowledge deficiencies, and suggests potential directions for future research. The paper includes six figures and delves into the transformation of research landscapes in the realm of machine-assisted behavioral annotation, focusing on critical issues such as bias.

Abstract

As China has become the largest user of industrial robots, the need to understand how workers perceive robot-human substitution and how their perceptions influence their job behaviors is becoming increasingly crucial. This paper examined whether workers’ fear of being replaced by robots (FRR) is correlated with one aspect of job behavior: turnover intention, which refers to the extent to which an individual intends to change their job within a specific time period. Using a dataset covering 1512 manufacturing workers in Guangdong province of China, we found that workers who fear losing their jobs to robots report significantly higher turnover intention. We also found that the positive effect of FRR on turnover intention increased when robots were already utilised in the workplace. This effect was also found to be increase when workers perceived that their wages did not increase with the rise in productivity due to robotisation. Based on these findings, we provide practical recommendations to organizations on effectively addressing the turnover intention arising from the FRR.