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This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertainty. In Experiment 1, we compared trust in the advice of a physically embodied versus screen-mediated anthropomorphic robot, observing no effects of embodiment; in Experiment 2, we manipulated the relative anthropomorphism of virtual robots, observing modestly greater trust in the most anthropomorphic agent relative to the least. Across studies, when any version of the agent randomly disagreed, participants reversed their threat-identifications and decisions to kill in the majority of cases, substantially degrading their initial performance. Participants subjective confidence in their decisions tracked whether the agent (dis)agreed, while both decision-reversals and confidence were moderated by appraisals of the agents intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

The deployment of robots in human‐centric environments has significantly increased in recent years. It is crucial for robots to navigate human environments while understanding social norms and personal boundaries to ensure a harmonious coexistence between humans and robots. A socially aware robot should be capable of interpreting and responding appropriately to human cues, expressions, and intentions, thereby fostering trust and confidence among humans. However, prior studies were insufficient or unable to address the navigation challenges in human‐populated environments, as they perceive people as obstacles rather than social agents. Recent studies have utilized proxemic areas that are present in interpersonal interactions for human‐robot interaction scenarios, but they have enforced consistent proxemic areas for social robot navigation. This approach fails to fully capture the highly sophisticated behaviour and preferences of humans. Therefore, we propose a psychologically‐based adaptive proxemic area that fluctuates based on the human's emotional state. Furthermore, we integrate this feature into a reinforcement learning‐based social navigation framework, making our navigation framework robust to the unpredictable affections of humans. Additionally, our navigation framework includes human intention prediction to approximate the future proxemic area, thereby avoiding interference with the path to be taken by individuals. We have named our framework the Human Emotion and Intention Aware Path Planner (EmoiPlanner). Our framework has been subjected to case studies involving realistic crowd navigation scenarios, and the results indicate that it enables robots to navigate through crowds without causing discomfort to pedestrians who exhibit stochastic behaviours and emotional states, while also ensuring efficient path planning. [ABSTRACT FROM AUTHOR]

Most studies of social robot interventions for children with autism spectrum disorder have been laboratory experiments focusing on component skills. There is insufficient evidence documenting the qualitative impact of such programmes on social development and participation of children with autism spectrum disorder. This study aimed to identify the qualitative outcomes of a robot-mediated social skills training programme for children with autism spectrum disorder, examine the impact of such programmes on children's social participation and identify the essential elements of robotic interventions that are conducive to children's social development. A case study approach with a multiple case study design was adopted. Sixteen children with autism spectrum disorder, aged 5–11 years, were included. Participants received 12 weekly sessions of robot-mediated social skills training. The successful outcomes relating to social participation were identified as enhanced verbal expression, social awareness and emotional reciprocity. The impacts of the programme on personal development were identified as enhanced self-esteem, self-confidence and emotional expression. Robot friendship, the role of the robot as a facilitator and the presence of a human instructor capable of leading the programme were identified as essential elements of the positive changes. The encounter with a social robot was regarded as meaningful and important to the children with autism spectrum disorder. [ABSTRACT FROM AUTHOR]

The integration of robots into social environments necessitates their ability to interpret human intentions and anticipate potential outcomes accurately. This capability is particularly crucial for social robots designed for human care, as they may encounter situations that pose significant risks to individuals, such as undetected obstacles in their path. These hazards must be identified and mitigated promptly to ensure human safety. This paper delves into the artificial theory of mind (ATM) approach to inferring and interpreting human intentions within human–robot interaction. We propose a novel algorithm that detects potentially hazardous situations for humans and selects appropriate robotic actions to eliminate these dangers in real time. Our methodology employs a simulation-based approach to ATM, incorporating a "like-me" policy to assign intentions and actions to human subjects. This strategy enables the robot to detect risks and act with a high success rate, even under time-constrained circumstances. The algorithm was seamlessly integrated into an existing robotics cognitive architecture, enhancing its social interaction and risk mitigation capabilities. To evaluate the robustness, precision, and real-time responsiveness of our implementation, we conducted a series of three experiments: (i) A fully simulated scenario to assess the algorithm's performance in a controlled environment; (ii) A human-in-the-loop hybrid configuration to test the system's adaptability to real-time human input; and (iii) A real-world scenario to validate the algorithm's effectiveness in practical applications. These experiments provided comprehensive insights into the algorithm's performance across various conditions, demonstrating its potential for improving the safety and efficacy of social robots in human care settings. Our findings contribute to the growing research on social robotics and artificial intelligence, offering a promising approach to enhancing human–robot interaction in potentially hazardous environments. Future work may explore the scalability of this algorithm to more complex scenarios and its integration with other advanced robotic systems. [ABSTRACT FROM AUTHOR]

As robots become increasingly common in human-populated environments, they must be perceived as social beings and behave socially. People try to preserve their own space during social interactions with others, and this space depends on a variety of factors, such as individual characteristics or their age. In real-world social spaces, there are many different types of people, and robots need to be more sensitive, especially when interacting with vulnerable subjects such as children. However, the current navigation methods do not consider these differences and apply the same avoidance strategies to everyone. Thus, we propose a new navigation framework that considers different social types and defines appropriate personal spaces for each, allowing robots to respect them. To this end, the robot needs to classify people in a real environment into social types and define the personal space for each type as a Gaussian asymmetric function to respect them. The proposed framework is validated through simulations and real-world experiments, demonstrating that the robot can improve the quality of interactions with people by providing each individual with an adaptive personal space. The proposed costmap layer is available on GitHub. [ABSTRACT FROM AUTHOR]

AI-based technologies are increasingly pervasive in a number of contexts. Our affective and emotional life makes no exception. In this article, we analyze one way in which AI-based technologies can affect them. In particular, our investigation will focus on affective artificial agents, namely AI-powered software or robotic agents designed to interact with us in affectively salient ways. We build upon the existing literature on affective artifacts with the aim of providing an original analysis of affective artificial agents and their distinctive features. We argue that, unlike comparatively low-tech affective artifacts, affective artificial agents display a specific form of agency, which prevents them from being perceived by their users as extensions of their selves. In addition to this, we claim that their functioning crucially depends on the simulation of human-like emotion-driven behavior and requires a distinctive form of transparency—we call it emotional transparency—that might give rise to ethical and normative tensions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertainty. In Experiment 1, we compared trust in the advice of a physically embodied versus screen-mediated anthropomorphic robot, observing no effects of embodiment; in Experiment 2, we manipulated the relative anthropomorphism of virtual robots, observing modestly greater trust in the most anthropomorphic agent relative to the least. Across studies, when any version of the agent randomly disagreed, participants reversed their threat-identifications and decisions to kill in the majority of cases, substantially degrading their initial performance. Participants’ subjective confidence in their decisions tracked whether the agent (dis)agreed, while both decision-reversals and confidence were moderated by appraisals of the agent’s intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

Abstract Emotionally expressive vocalizations can elicit approach-avoidance responses in humans and non-human animals. We investigated whether artificially generated sounds have similar effects on humans. We assessed whether subjects' reactions were linked to acoustic properties, and associated valence and intensity. We generated 343 artificial sounds with differing call lengths, fundamental frequencies and added acoustic features across 7 categories and 3 levels of biological complexity. We assessed the hypothetical behavioural response using an online questionnaire with a manikin task, in which 172 participants indicated whether they would approach or withdraw from an object emitting the sound. (1) Quieter sounds elicited approach, while loud sounds were associated with avoidance. (2) The effect of pitch was modulated by category, call length and loudness. (2a) Low-pitched sounds in complex sound categories prompted avoidance, while in other categories they elicited approach. (2b) Higher pitch in loud sounds had a distancing effect, while higher pitch in quieter sounds prompted approach. (2c) Longer sounds promoted avoidance, especially at high frequencies. (3) Sounds with higher intensity and negative valence elicited avoidance. We conclude that biologically based acoustic signals can be used to regulate the distance between social robots and humans, which can provide an advantage in interactive scenarios.

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. [ABSTRACT FROM AUTHOR]

This paper introduces DAC-HRC, a novel cognitive architecture designed to optimize human-robot collaboration (HRC) in industrial settings, particularly within the context of Industry 4.0. The architecture is grounded in the Distributed Adaptive Control theory and the principles of joint intentionality and interdependence, which are key to effective HRC. Joint intentionality refers to the shared goals and mutual understanding between a human and a robot, while interdependence emphasizes the reliance on each other's capabilities to complete tasks. DAC-HRC is applied to a hybrid recycling plant for the disassembly and recycling of Waste Electrical and Electronic Equipment (WEEE) devices. The architecture incorporates several cognitive modules operating at different timescales and abstraction levels, fostering adaptive collaboration that is personalized to each human user. The effectiveness of DAC-HRC is demonstrated through several pilot studies, showcasing functionalities such as turn-taking interaction, personalized error-handling mechanisms, adaptive safety measures, and gesture-based communication. These features enhance human-robot collaboration in the recycling plant by promoting real-time robot adaptation to human needs and preferences. The DAC-HRC architecture aims to contribute to the development of a new HRC paradigm by paving the way for more seamless and efficient collaboration in Industry 4.0 by relying on socially adept cognitive architectures. [ABSTRACT FROM AUTHOR]

The development of university students’ skills to successfully produce scientific documents has been a recurring topic of study in academia. This paper analyzes the implementation of a training experience using a digital environment mediated by video content materials starring humanoid robots. The research aimed to scale complex thinking and its sub-competencies as a hinge to strengthen basic academic research skills. Students from Colombia, Ecuador, and Mexico committed to preparing a scientific document as part of their professional training participated. A pretest to know their initial level of perception, a posttest to evaluate if there was a change, and a scientific document the students delivered at the end of the training experience comprised the methodology to demonstrate the improvement of their skills. The results indicated students’ perceived improvement in the sub-competencies of systemic, creative, scientific, and innovative thinking; however, their perceptions did not align with that of the tutor who reviewed the delivered scientific product. The conclusion was that although the training experience helped strengthen the students’ skills, variables that are determinants for a student to develop the knowledge necessary to prepare scientific documents and their derived products remain to be analyzed. [ABSTRACT FROM AUTHOR]

Little is known about children's long-term acceptance of social robots; whether different types of users exist; and what reasons children have not to use a robot. Moreover, the literature is inconclusive about how the measurement of children's robot acceptance (i.e., self-report or observational) affects the findings. We relied on both self-report and observational data from a six-wave panel study among 321 children aged eight to nine, who were given a Cozmo robot to play with at home over the course of 8 weeks. Children's robot acceptance decreased over time, with the strongest drop after 2–4 weeks. Children rarely rejected the robot (i.e., they did not stop using it already prior to actual adoption). They rather discontinued its use after initial adoption or alternated between using and not using the robot. The competition of other toys and lacking motivation to play with Cozmo emerged as the strongest reasons for not using the robot. Self-report measures captured patterns of robot acceptance well but seemed suboptimal for precise assessments of robot use. [ABSTRACT FROM AUTHOR]

As the population of older adults increases, there is an anticipated rise in the utilization of hospice and palliative care. Many significant advancements in technology have been used to address the unique needs of this demographic; however, an unexplored area of research is the use of robotic animals as part of end-of-life care. The purpose of this scoping review was to examine the state of the literature on robotic animal use among older adults enrolled in palliative or hospice care and to offer a framework for future research. Following a guide for scoping reviews, we identified relevant studies and then charted, collated, summarized, and reported the data. Two articles were selected for final review. The results found that decreased medication use, behavior change, and emotional benefits were potential outcomes of robotic animal use in hospice and palliative care. Perceptions of the robot and ethical considerations were also discussed. Overall, the study findings point toward the potential uses of robotic animals as part of end-of-life care, however, more empirical research is critically needed. [ABSTRACT FROM AUTHOR]

For a period of 3 weeks in June 2021, we embedded a social robot (Softbank Pepper) in a Special Educational Needs (SEN) school for autistic children. The robot's behaviours and integration into the school were co-designed with the children and teachers, with a focus on improving the well-being of the pupils. Using a mix-method approach, we studied the robot's adoption over the course of the study, and the impact of the robot's presence on the children well-being and the school ecosystem. We found that the robot successfully integrated within the school; it fostered and maintained a steady level of interactions (330 interactions, 16 h of continuous use over 3 weeks) with a small yet meaningful group of children with a positive impact on their well-being; and it led to a nuanced conversation with the students and school staff about the role and impact of such a social technology in a SEN school. [ABSTRACT FROM AUTHOR]

This study investigates the influence of gender and age on the acceptance of emerging technologies such as robotics and artificial intelligence (AI), Using the Spanish Sociological Research Centre's Science and Technology Perception Survey 2023. This is a representative sample of the Spanish population, comprising 2,384 responses. The results obtained using Ordinary Least Squares Regression reveal highly significant differences in acceptance and interest in technology, with women and older people showing less enthusiasm towards AI and robotics. The study also uncovers a nuanced perception of the socio-cultural impact of technology. While concerns about dehumanization were evident, fears related to elitism and inequality were not significantly supported. These findings highlight the need for inclusive and sensitive strategies in the development and implementation of new technologies by both corporate CSR policy and public policy. [ABSTRACT FROM AUTHOR]

Theory of Mind (ToM) and Metacognition constitute two superior mental mechanisms that promote the smooth integration and adaptation of the individual in society. In particular, the ability to read minds introduces the individual into the social world, contributing to understanding oneself and others. Metacognition focuses on individual knowledge, control, regulation, and readjustment regarding the cognitive mechanism and its influence on cognitive performance and the mental and social development of the individual. At the basis of the development of the two mechanisms is the activation of social interaction, which determines their levels of development. The innovative approaches and great expectations of technology and Artificial Intelligence for improving the artificial mind brought social robots to the fore. Robots with social action are gradually entering human life. Their interaction with the human factor is anticipated to become more and more frequent, expanded, and specialized. Hence, the investigation of equipping artificial systems with integrated social-cognitive and metacognitive capabilities was necessary, constituting the subject of study of the current narrative review. Research findings show that intelligent systems with introspection, self-evaluation, and perception-understanding of emotions, intentions, and beliefs can develop safe and satisfactory communication with humans as long as their design and operation conform to the code of ethics.

Tele-operated social robots (telerobots) offer an innovative means of allowing children who are medically restricted to their homes (MRH) to return to their local schools and physical communities. Most commercially available telerobots have three foundational features that facilitate child-robot interaction: remote mobility, synchronous two-way vision capabilities, and synchronous two-way audio capabilities. We conducted a comparative analysis between the Toyota Human Support Robot (HSR) and commercially available telerobots, focusing on these foundational features. Children who used these robots and these features on a daily basis to attend school were asked to pilot the HSR in a simulated classroom for learning activities. As the HSR has three additional features that are not available on commercial telerobots: (1) pan-tilt camera, (2) mapping and autonomous navigation, and (3) robot arm and gripper for children to "reach" into remote environments, participants were also asked to evaluate the use of these features for learning experiences. To expand on earlier work on the use of telerobots by remote children, this study provides novel empirical findings on (1) the capabilities of the Toyota HSR for robot-mediated learning similar to commercially available telerobots and (2) the efficacy of novel HSR features (i.e., pan-tilt camera, autonomous navigation, robot arm/hand hardware) for future learning experiences. We found that among our participants, autonomous navigation and arm/gripper hardware were rated as highly valuable for social and learning activities.

Safe and socially compliant navigation in a crowded environment is essential for social robots. Numerous research efforts have shown the advantages of deep reinforcement learning techniques in training efficient policies, while most of them ignore fast-moving pedestrians in the crowd. In this paper, we present a novel design of safety measure, named Risk-Area, considering collision theory and motion characteristics of different robots and humans. The geometry of Risk-Area is formed based on the real-time relative positions and velocities of the agents in the environment. Our approach perceives risk in the environment and encourages the robot to take safe and socially compliant navigation behaviors. The proposed method is verified with three existing well-known deep reinforcement learning models in densely populated environments. Experiment results demonstrate that our approach combined with the reinforcement learning techniques can efficiently perceive risk in the environment and navigate the robot with high safety in the crowds with fast-moving pedestrians. [ABSTRACT FROM AUTHOR]

The complex relationships between humans and AI-empowered machines have created and inspired new products and services as well as controversial debates, fiction and entertainment, and last but not least, a striving and vital field of research. The (theoretical) convergence between the two categories of entities has created stimulating concepts and theories in the past, such as the uncanny valley, machinization of humans through datafication, or humanization of machines, known as anthropomorphization. In this article, we identify a new gap in the relational interaction between humans and AI triggered by commercial interests, making use of AI through advertisement, marketing, and corporate communications. Our scope is to broaden the field of AI and society by adding the business-society-nexus. Thus, we build on existing research streams of machinewashing and the analogous phenomenon of greenwashing to theorize about the humanwashing of AI-enabled machines as a specific anthropomorphization notion. In this way, the article offers a contribution to the anthropomorphization literature conceptualizing humanwashing as a deceptive use of AI-enabled machines (AIEMs) aimed at intentionally or unintentionally misleading organizational stakeholders and the broader public about the true capabilities that AIEMs possess. [ABSTRACT FROM AUTHOR]