Exploring Interactive Services by Hotel Room Social Robots

The decisions made by social robots while they fulfill their tasks have a strong influence on their performance. In these contexts, autonomous social robots must exhibit adaptive and social-based behavior to make appropriate decisions and operate correctly in complex and dynamic scenarios. This paper presents a Decision-Making System for social robots working on long-term interactions like cognitive stimulation or entertainment. The Decision-making System employs the robot’s sensors, user information, and a biologically inspired module to replicate how human behavior emerges in the robot. Besides, the system personalizes the interaction to maintain the users’ engagement while adapting to their features and preferences, overcoming possible interaction limitations. The system evaluation was in terms of usability, performance metrics, and user perceptions. We used the Mini social robot as the device where we integrated the architecture and carried out the experimentation. The usability evaluation consisted of 30 participants interacting with the autonomous robot in 30 min sessions. Then, 19 participants evaluated their perceptions of robot attributes of the Godspeed questionnaire by playing with the robot in 30 min sessions. The participants rated the Decision-making System with excellent usability (81.08 out of 100 points), perceiving the robot as intelligent (4.28 out of 5), animated (4.07 out of 5), and likable (4.16 out of 5). However, they also rated Mini as unsafe (security perceived as 3.15 out of 5), probably because users could not influence the robot’s decisions.

Robots are a combination of mechatronics, computer science, and artificial intelligence. Robotics is a branch of engineering that involves the conception, design, manufacture, and operation of actions. Whenever a robot has to interact with the human-society, it has to adopt a special skill called Human-Robot Interaction and thus the term Social-Robot comes into account. A social robot has to be able to express emotions, communicate with high-level dialogue, use natural cues, and learn to recognize models of other agents. An autonomous social robot cannot follow orders instead of doing something on its own. To make the robot more interactive and communicative, lots of sensors and modules have to be used along with its moving mechanism. Therefore, a social robot becomes complex and expensive. To overcome the issue of the complexity and costliness, in this paper, a design of social robot using a combination of embedded systems, the Internet of Robotic Things (IORT) and Android operating system has been introduced to be interactive and communicative to human, be intelligent enough to solve complex mathematics and be able to follow the operator’s command simultaneously. By using the Internet as the robot’s source of information and the Android phone as the robot’s sensory and control system partially, and adding them all to the robot’s embedded system wirelessly, we have not only become able to make the robot more advanced and intelligent, but also reduce the cost of construction by a significant amount.

The United States Department of Defense (DoD) designs, constructs, and deploys social and autonomous robots and robotic weapons systems. Military robots are designed to follow the rules and conduct of the professions or roles they emulate, and it is expected that ethical principles are applied and aligned with such roles. The application of these principles appear paramount during the COVID-19 global pandemic, wherein substitute technologies are crucial in carrying out duties as humans are more restrained due to safety restrictions. This article seeks to examine the ethical implications of the utilization of military robots. The research assesses ethical challenges faced by the United States DoD regarding the use of social and autonomous robots in the military. The authors provide a summary of the current status of these lethal autonomous and social military robots, ethical and moral issues related to their design and deployment, a discussion of policies, and the call for an international discourse on appropriate governance of such systems.

The success of social robots, even (or, especially) in use cases as simple as “manning” a booth to promote a product in a shopping mall, depends on their ability of interacting with humans in a timely, effective and enjoyable way. In this paper we present an attention system and a gesture system for use by an autonomous social robot in applications related to product promotion. Our attention system employs a modular approach and attention functions, to allow for rich run-time behaviours to arise from simple rules, while the proposed gesture system allows for tuning the gestures to convey different emotions and robot personalities. The two systems were tested in an experiment involving 790 participants, aiming to explore the attractive power of different robot behaviours.

A survey on the design and evolution of social robots — Past, present and future

As a technical application in artificial intelligence, a social robot is one of the branches of robotic studies that emphasizes socially communicating and interacting with human beings. Although both robot and behavior research have realized the significance of social robot design for its market success and related emotional benefit to users, the specific design of the eye and mouth shape of a social robot in eliciting trustworthiness has received only limited attention. In order to address this research gap, our study conducted a 2 (eye shape) × 3 (mouth shape) full factorial between-subject experiment. A total of 211 participants were recruited and randomly assigned to the six scenarios in the study. After exposure to the stimuli, perceived trustworthiness and robot attitude were measured accordingly. The results showed that round eyes (vs. narrow eyes) and an upturned-shape mouth or neutral mouth (vs. downturned-shape mouth) for social robots could significantly improve people's trustworthiness and attitude towards social robots. The effect of eye and mouth shape on robot attitude are all mediated by the perceived trustworthiness. Trustworthy human facial features could be applied to the robot's face, eliciting a similar trustworthiness perception and attitude. In addition to empirical contributions to HRI, this finding could shed light on the design practice for a trustworthy-looking social robot.

Background: There are 4.9 million English Language Learners (ELLs) in the United States. Only 2% of educators are trained to support these vulnerable students. Social robots show promise for language acquisition and may provide valuable support for students, especially as we return to needing smaller classes due to COVID-19. While cultural responsiveness increases gains for ELLs, little is known about the design of culturally responsive child–robot interactions. Method: Therefore, using a participatory design approach, we conducted an exploratory study with 24 Spanish-speaking ELLs at a Pacific Northwest elementary school. As cultural informants, students participated in a 15-min, robot-led, small group story discussion followed by a post-interaction feedback session. We then conducted reflexive critiques with six ELL teachers who reviewed the group interactions to provide further interpretation on design feature possibilities and potential interactions with the robot. Results: Students found the social robot engaging, but many were hesitant to converse with the robot. During post-interaction dialogue students articulated the specific ways in which the social robot appearance and behavior could be modified to help them feel more comfortable. Teachers postulated that the social robot could be designed to engage students in peer-to-peer conversations. Teachers also recognized the ELLs verbosity when discussing their experiences with the robot and suggested such interactions could stimulate responsiveness from students. Conclusion: Cultural responsiveness is a key component to successful education in ELLs. However, integrating appropriate, cultural responsiveness into robot interactions may require participants as cultural informants to ensure the robot behaviors and interactions are situated in that educational community. Utilizing a participatory approach to engage ELLs in design decisions for social robots is a promising way to gather culturally responsive requirements to inform successful child–robot interactions.

HintergrundSoziale Beziehungen sind bedeutsame Ressourcen für psychisches Wohlbefinden und physische Gesundheit. Im höheren Lebensalter treffen zunehmende Vulnerabilität und Funktionsverluste häufig auf reduzierte soziale Netzwerke. Mangelnde soziale Kontakte und fehlende Netzwerke bergen dabei psychische und physische Risiken für die Betroffenen, die durch den Einsatz sozialer Roboter möglicherweise abgemildert werden können.FragestellungWelche Potenziale und Herausforderungen ergeben sich für ältere Menschen aus ihrer Interaktion mit sozialen Robotern?Material und MethodenDie Forschungsfrage wird mittels eines „rapid review“ beantwortet. Eine systematische Literatursuche ergab 433 unikale Treffer, aus denen n = 11 Artikel in die Analysen eingingen.ErgebnissePotenziale sozialer Roboter bestehen in der Reduktion von Einsamkeit, Stärkung der (zwischenmenschlichen) Kommunikation und Stimmungsaufhellung bei gleichzeitiger Stressreduktion. Herausforderungen bestehen in der sozialen Einbettung der Roboter. Diese sei durch Aspekte wie Wohltätigkeit, Autonomie und Privatheit als Grundsätze zu gestalten, an denen sich Design und Einsatz von sozialen Robotern orientieren können, um einem Verlust von sozialen Beziehungen vorzubeugen.DiskussionDie Ergebnisse zeigen einen Korridor auf, der die potenzialausschöpfende Anwendung sozialer Roboter für ältere Menschen ermöglicht. Im Vordergrund steht die Analyse der Herausforderungen für den Einzelfall, da soziale Beziehungen älterer Menschen positiv sowie negativ beeinflusst werden können. Dabei orientieren sich die eingeschlossenen Artikel größtenteils am Setting Pflege. Forschung zum Einsatz sozialer Roboter bei nicht oder wenig funktionseingeschränkten Personen sollte die bestehende Literatur ergänzen.Zusatzmaterial onlineZusätzliche Informationen sind in der Online-Version dieses Artikels (10.1007/s00391-021-01932-5) enthalten.

A wider incorporation of robots into classrooms is hampered by current technological limitations on full autonomy in social robots. Automated speech recognition, for example, a key enabler for vocal communication, is still unable to perform with sufficient accuracy. Past studies have shown that humans adjust their speech patterns to accommodate less skilled interlocutors. If such a response holds in human-robot interactions as well, we may be able to exploit it to lessen the burden on social robots and enable rich, autonomous vocal communication. In this paper we explore whether a robot's speaking ability could have an impact on children's speech patterns, learning, and engagement by designing an interaction where a child and a robot collaborate on a Tower of Hanoi puzzle. Sixteen children aged 7-14 completed this collaborative task partnered with a social robot that communicated with either high verbal (full sentences), low verbal (short phrases or single words), or nonverbal (sound-based utterances) vocalization. While we found no significant impact on children's speech patterns or learning due to the robot's method of vocalization, children in the non-verbal condition had a significantly lower perception of the robot's intelligence along with higher rates of providing feedback and more instances of undoing its moves. This suggests that a link may exist between a robot's perceived speaking ability and children's confidence in that robot's overall intelligence and capability in a collaborative task, as well as their empathy towards a peer they perceive as less skilled in the task.

Worldwide, approximately 50 million people live with Alzheimer’s disease or other dementias and there are nearly 10 million new cases every year. Social robots have been a promising approach to supplement human caregivers in dementia care. In this narrative review, we reviewed 62 articles to gain insight into the attitudes and perceptions among people with dementia and other stakeholders worldwide towards using social robots to assist dementia care. Then, we discussed some critical factors and challenges found in these studies influencing people’s perceptions as well as future directions in this field. The primary influencing challenges include cultural factors, users’ limited experiences with technologies, methodological challenges underlying qualitative studies as well as technological malfunctions in current robot system. We further suggested several aspects to be taken into more consideration in future research, including collaborations with other stakeholders, design of individual or group use for dementia care, an adaptive level of autonomy in a social robot and long-term human-robot interaction.

3D visual recognition is a prerequisite for most autonomous robotic systems operating in the real world. It empowers robots to perform a variety of tasks, such as tracking, understanding the environment, and human–robot interaction. Autonomous robots equipped with 3D recognition capability can better perform their social roles through supportive task assistance in professional jobs and effective domestic services. For active assistance, social robots must recognize their surroundings, including objects and places to perform the task more efficiently. This article first highlights the value-centric role of social robots in society by presenting recently developed robots and describes their main features. Instigated by the recognition capability of social robots, we present the analysis of data representation methods based on sensor modalities for 3D object and place recognition using deep learning models. In this direction, we delineate the research gaps that need to be addressed, summarize 3D recognition datasets, and present performance comparisons. Finally, a discussion of future research directions concludes the article. This survey is intended to show how recent developments in 3D visual recognition based on sensor modalities using deep-learning-based approaches can lay the groundwork to inspire further research and serves as a guide to those who are interested in vision-based robotics applications.

How do typically developing children and children with autism perceive different social robots?

Robots have recently gained popularity in customer service. Especially social robots are nowadays utilized in healthcare, elderly homes and schools. Although it is crucial to design social robots according to well-defined user experience goals, research related to experience-driven design of social robots is still scarce. Experience-Driven Design (EDD) is a framework to design interaction for technology based on certain goals, known as experience goals. In this paper, we present the design and evaluation of the university guidance robot based on the user experience goals defined in previous research. The experience goals are nurture, fellowship and recreation. We designed applications, interaction, and robot's behavior to support the fulfillment of the experience goals. The social robot Pepper served as a platform for the university guidance robot. The evaluation was conducted as a field study in a university campus with 32 university students during the orientation week. According to our findings, the university guide robot successfully evoked nurture, fellowship and recreation among participants.

Social robots are demonstrating to have potential in several healthcare applications, especially in rehabilitation areas. This paper presents an architecture for a socially assistive robot system for cardiac rehabilitation, based on a model-controller structure through a finite-state machine and a behaviour module. The platform has been designed to provide social support and assistance during the therapy, aiming to improve the quality of the provided service, as well as the engagement and performance of the patients. This architecture has been tested under clinical conditions with a patient during a typical therapy session. The results show that the proposed architecture is able to adapt to the various situations present during a session, providing a robust framework to further develop the robot's behaviour towards a more natural and intuitive interaction with the patients.

Social robots, both when acting individually and in groups, need to manage their own energy use effectively. An uneven energy distribution in a swarm of robots performing a mission may prevent completion of the group's mission or reduce its tolerance for adverse events that occur during mission execution. The goal of effectively managing energy use in a swarm is known as collective energy homeostasis. While previous works have mainly focused on achieving this goal by direct energy exchange methods (e.g., using battery charging mechanisms), this paper presents a novel bio-inspired approach for maintaining collective energy homeostasis in social robot swarms. The approach extends Particle Swarm Optimization (PSO) techniques for task selection and motion planning for individual robots by making them sensitive to the robot's energy state. The overall effect of this Energy-based PSO (EPSO) algorithm is to shift energy-intensive tasks towards robots in the swarm that have higher energy levels, i.e. energy load-leveling, which improves energy self-sufficiency and its homogeneity across the swarm. This can be considered a form of indirect energy exchange by task shifting. Experimental results show that the EPSO algorithm enables social robot swarms to maintain collective energy homeostasis more effectively than previous approaches, reducing the variance of energy between individuals by 49%, and extending the number of missions that a swarm can achieve, given a fixed initial energy budget.