International Journal of Social Robotics最新文献

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.

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.