International Journal of Social Robotics最新文献

As robotic agents become increasingly pervasive in our lives, recent works have shown promising results on the use of emotions on social drones. However, further research is needed to determine the appropriateness of these emotions in various contexts. We here investigate the appropriateness of seven drone emotional states. In a series of six workshops ((N{=}30)) consisting of both individual and group activities, we studied emotion appropriateness for six drone metaphors across the two most popular and radically different domains for Human–Drone Interaction namely: Emergency and Entertainment. Within diverse situations of interactions, participants were able to identify the appropriateness of each emotion. Our results describe how each emotion was found both appropriate and inappropriate depending on context. We provide insights into unique opportunities generated by the perceived emotion appropriateness, depending on different roles of drone emotions in interactions with people. We conclude with design considerations for future social robotic agents, including the importance of using a broad range of emotions, the use of a neutral expression, the temporality of emotions, and novel applications to interaction design. This work contributes to the understanding of the inner workings of emotion appropriateness in drones, providing researchers with a starting point for future work on social flying robots.

Social learning is an evolutionarily important ability increasingly attributed also to invertebrate species. Interfacing robots with animals represents a promising strategy to investigate social learning. Herein, we studied if the gregarious form of Locusta migratoria, a particularly suited model to examine social learning, can use social information provided by robotic demonstrators to optimize their predator avoidance. Robotic demonstrators with different silhouettes and colours (biomimetic or neutral) were used to investigate if their rotation on a rod (e.g. hiding behaviour) elicited the same behaviour in neighbouring locusts. Locusts’ responses were affected by different robotic demonstrators, observing a significant impact of the biomimetic silhouette in reducing the latency duration, and in promoting social learning (e.g. locusts displaying hiding behaviour after observing it in robotic demonstrators). A significant impact of colour patterns in triggering socially induced hiding behaviour was also recorded, especially when the biomimetic silhouette was coloured with the gregarious-like pattern. This research indicates gregarious locusts exploit social information in specific ecological contexts, providing basic knowledge on the complex behavioural ecology and social biology in invertebrates. The proposed animal-robot interaction paradigm shows the role of robots as carrier of social information to living organisms, suggesting social biorobotics as advanced and sustainable approach for socio-biology investigation, and environmental management.

This research seeks to identify the factors that affect people’s decision to purchase, or to not purchase, social robots for their homes. To this aim, we focus on a specific technological use case: behavior and habit change. As consumer behavior research suggests that preferred designs and price sensitivity will vary between those who are technology early adopters and those who are mainstream adopters, we look at how self-classification influences the aforementioned areas. To this end, we interview 18 individuals to identify behavioral change goals and note reactions to three videos of technology for habit change. In addition to assessing willingness-to-pay (WTP) by using established methods in market research, holistic product design cards are also created to aid this process and to support user design. Additionally, we compare how people’s purchase-based designs differ from their ideal designs. We find that although early adopters prefer domestic robots to be human-like in form and behavior, they exhibit significant downgrading, especially to a more device-like form, due to price. Alternatively, we find that those in the mainstream prefer technology that is not human-like in form or behavior, and that privacy concerns and a desire for control have significant impacts on their designs.

Immersive robotic avatars have the potential to aid and replace humans in a variety of applications such as telemedicine and search-and-rescue operations, reducing the need for travel and the risk to people working in dangerous environments. Many challenges, such as kinematic differences between people and robots, reduced perceptual feedback, and communication latency, currently limit how well robot avatars can achieve full immersion. This paper presents AVATRINA, a teleoperated robot designed to address some of these concerns and maximize the operator’s capabilities while using a commodity light-weight human–machine interface. Team AVATRINA took 4th place at the recent $10 million ANA Avatar XPRIZE competition, which required contestants to design avatar systems that could be controlled by novice operators to complete various manipulation, navigation, and social interaction tasks. This paper details the components of AVATRINA and the design process that contributed to our success at the competition. We highlight a novel study on one of these components, namely the effects of baseline-interpupillary distance matching and head mobility for immersive stereo vision and hand-eye coordination.

In a human–robot team, robots may play a manager, helping to maintain the behavioural norms of its team. Robots as managers have the power to reward and punish. Currently, a small number of previous relevant studies have mainly focused on the impact of robot punishment behaviour on human beings, but managers without reward behaviour have difficulty gaining the trust of members. Therefore, this study investigates the effects of robot managers' reward and punishment behaviours on human–robot trust and job performance and explores the mediating effect of emotion and the moderating effect of group relations. The study recruited 76 participants using a 2 (independent variable robot managers' reward and punishment behaviours: reward behaviour, punishment behaviour) × 2 (moderator variable human–robot group relations: ingroup and outgroup) experimental design, and each participant and a robot manager worked together to complete the task of sorting items. It was found that the robot managers' reward-punishment behaviours have an impact on human emotions. Emotions play a mediating role in the effect of robot managers' reward-punishment behaviours on human trust but do not play a mediating role in the effect on job performance. The human–robot group relation plays a moderating role in the effects of emotions on human–robot trust. The research results help more preferably understand the interaction mechanism of the human–robot team and more preferably serve the management and cooperation of the human–robot team by appropriately adjusting the robot managers' reward and punishment behaviours in the human–robot team and the human–robot group relation.

Here, using a cooperative reaction game, we compared human interaction with an anthropomorphic robot and a computer program. The assistant (either the robot or the program) took over half of the tasks that the participant faced in the game. In half of the game sessions, the assistant reacted slowly, and in the other half, it reacted faster than naive participants. Together with the fast-paced robot, participants scored significantly more points, than with the fast-paced program. This difference was made by the players, as there was no difference in performance between the robot and the program. In addition, human reaction time varied with the pace of the robot, but not the program. We believe that the participants played better with the robot and adopted its pace because they perceived it as a real co-agent. According to the survey, the participants preferred to play with the robot. Together, these results suggest promising prospects for joint human–robot agency and the use of anthropomorphic robots.

Abstract

Increasingly, roboticists have to pay attention to cultural norms and expectations. But roboticists have generally worked with a relatively narrow understanding of culture, based on nationality. This contrasts with the rich and diverse understandings of culture from disciplines as diverse as sociology, philosophy, and anthropology. Here we draw on the philosophy of science literature on scientific terminology to argue that culture is a conceptually fragmented concept: the concept has no unified definition, and alternative definitions of culture are useful for different areas within robotics. We argue that this has important implications for robotics. We consider two possible reactions to this situation. One claims that, despite the lack of a unified definition, the concept of culture still fulfils useful roles within robotics, and ought to be preserved. The other argues that the problems with the concept are so great that the concept ought to be eliminated from discussions in robotics. We argue in favour of the former option.

Abstract

Due to demographic change and transformations in domestic structures as well as working environments, the need for formal care continues to increase. This process leads to a significantly greater number of care workers that will be needed in the future. Concurrently, the demands on caregivers concerning the amount of care and quality of care are increasing. Socially-assistive robots (SARs) are a promising resource in this regard. At the same time, research that directly addresses the target group of elderly persons with cognitive impairments is underrepresented. This study reports observations regarding the interaction between a SAR and elderly people with cognitive impairments. Seven observational and behavioural measures were conducted during the interaction between residents of a nursing home and the SAR named “James”. Twelve participants from two residential homes took part in the study. Data were analysed by using content analysis and interpreted along a technology acceptance model. Verbal comments and observable emotions of the SAR were predominantly positive, only a few participants reacted negatively to the SAR “James”. There was also hardly any shyness to touch the robot. The participants made eye contact and responded adequately to the robot’s requests. Tasks which were set by the SAR in group settings led to a higher communication between the participants than tasks in single-user settings. The mainly upbeat emotions and interaction with the SAR indicates a positive attitude towards the system. Long-term studies are needed to investigate the sustainability of robot acceptance.

Prolonged sedentary behavior in the vast population of office and remote workers leads to increased cardiovascular and musculoskeletal health challenges, and existing solutions for encouraging breaks are either costly health coaches or notification systems that are easily ignored. A socially assistive robot (SAR) for promoting healthy workplace practices could provide the physical presence of a health coach along with the scalability of a notification system. To investigate the impact of such a system, we implemented a SAR as an alternative break-taking support solution and examined its impact on individual users’ break-taking habits over relatively long-term deployments. We conducted an initial two-month-long study (N = 7) to begin to understand the robot’s influence beyond the point of novelty, and we followed up with a week-long data collection (N = 14) to augment the dataset size. The resulting data was used to inform a robot behavior model and formulate possible methods of personalizing robot behaviors. We found that uninterrupted sitting time tended to decrease with our SAR intervention. During model formulation, we found participant responsiveness to the break-taking prompts could be classified into three archetypes and that archetype-specific adjustments to the general model led to improved system success. These results indicate that break-taking prompts are not a one-size-fits-all problem, and that even a small dataset can support model personalization for improving the success of assistive robotic systems.