International Journal of Social Robotics最新文献

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.

Increasingly often robots are deployed in human environments, where they will encounter people. An example of a challenge robots encounter is crossing paths with a human. Based on human-robot proxemics research one would expect that people would keep a certain distance to maintain an appropriate comfort level. However it is unclear whether this also holds for crossing scenarios between a robot and a person. In the first experiment presented in this paper, a humanoid robot crossed paths with a person in which the crossing angle and acceleration of the robot were manipulated. Results showed that participants deviated more from a straight path when the robot arrived earlier at the crossing point compared to the other trials and when it accelerated or when the robot itself deviated from a straight path. If participants had to deviate from their path, it was regarded as less comfortable and it required more effort. In the second experiment, an autonomous guided vehicle was used, and we tested the moving speed of the robot. Similar to the first experiment, when the robot kept a straight path or stopped, it was regarded as the most comfortable. The results show that it is more comfortable if a robot does not change its direction while crossing paths with the robot. These findings indicate that perceived comfort is not merely determined by distance, but is more strongly affected by how predictable the robot is.

Co-speech gestures have significant impacts on conveying information. For social agents, producing realistic and smooth gestures are crucial to enable natural interactions with humans, which is a challenging task depending on many impact factors (e.g., speech audio, content, and the interacting person). In this paper, we tackle the cross-modal fusion problem through a novel fusion mechanism for end-to-end learning-based co-speech gesture generation. In particular, we facilitate parallel directional cross-modal transformers, and an interactive and cascaded 2D attention module, to achieve selective fusion of the gesture-related cues. Besides, we propose new metrics to evaluate gesture diversity and speech-gesture correspondence, without 3D pose annotation requirements. Experiments on a public dataset indicate that the proposed method can successfully produce diverse human-like poses, which outperform the other competitive state-of-the-art methods, with the evaluations conducted both objectively and subjectively.

This paper contributes to the understanding of child-robot interaction through the investigation of child interactions with and anthropomorphization of humanoid robots when manipulating robot-related variables such as behavior and gender. In this study, children observe a robot demonstration in a classroom setting, during which the robot showcases either assertive or submissive behavior and is attributed a gender, either robot-female or robot-male. Afterwards, participant anthropomorphization is measured using the Attributed Mental States Questionnaire (AMS-Q). Results suggest that when prompted to select a response directed at the robot, children used significantly more commanding phrases when addressing the assertively behaving robot when compared to the submissively behaving robot. Further, younger children ages 7–9 anthropomorphize robots at a higher degree than older children 10–12 and assertive behavior from the robot lead to higher rates of anthropomorphization. Results also suggest that children are more likely to respond to female robots in an imperative way than male robots. This widened understanding of child perception of and interaction with humanoid robots can contribute to the design of acceptable robot interaction patterns in various settings.

As social robots are projected to become an integral part of human life in the coming decades, their ability to adapt movement and trajectory when in proximity to people is essential for ensuring social acceptance during human-robot interaction. A key aspect of this adaptability involves predicting and anticipating human intents during robot navigation. Despite significant strides in the social navigation of autonomous robots within human environments, opportunities for advancements in related algorithms persist. This paper presents a novel real-time path trajectory optimization algorithm for socially aware robot navigation, grounded in the social elastic band concept, incorporating prediction and anticipation of human movements to adapt its forward velocity. Building upon the elastic band framework introduced in the 1990s for adapting robot trajectories in dynamic environments, our proposal of social elastic band differentiates between objects and human presence. This distinction allows for the definition of social interaction spaces and their relationship to the elastic band, facilitating the generation of socially accepted paths that rapidly adapt to environmental changes without causing a disturbance. Integrated into the SNAPE social navigation framework, the algorithm has been tested and validated through simulations and real-world experiments in various environments.

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.