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This paper presents an overview of our work towards building socially intelligent, cooperative humanoid robots that can work and learn in partnership with people. People understand each other in social terms, allowing them to engage others in a variety of complex social interactions including communication, social learning, and cooperation. We present our theoretical framework that is a novel combination of Joint Intention Theory and Situated Learning Theory and demonstrate how this framework can be applied to develop our sociable humanoid robot, Leonardo. We demonstrate the robot's ability to learn quickly and effectively from natural human instruction using gesture and dialog, and then cooperate to perform a learned task jointly with a person. Such issues must be addressed to enable many new and exciting applications for robots that require them to play a long-term role in people's daily lives.

Learning by imitation is a natural and intuitive way to teach social robots new behaviors. While these learning systems can use different sensory inputs, vision is often their main or even their only source of input data. However, while many vision-based robot learning by imitation (RLbI) architectures have been proposed in the last decade, they may be difficult to compare due to the absence of a common, structured description. The first contribution of this survey is the definition of a set of standard components that can be used to describe any RLbI architecture. Once these components have been defined, the second contribution of the survey is an analysis of how different vision-based architectures implement and connect them. This bottom–up, structural analysis of architectures allows to compare different solutions, highlighting their main advantages and drawbacks, from a more flexible perspective than the comparison of monolithic systems.

Computational systems for human–robot interaction (HRI) could benefit from visual perceptions of social cues that are commonly employed in human–human interactions. However, existing systems focus on one or two cues for attention or intention estimation. This research investigates how social robots may exploit a wide spectrum of visual cues for multiparty interactions. It is proposed that the vision system for social cue perception should be supported by two dimensions of functionality, namely, vision functionality and cognitive functionality. A vision-based system is proposed for a robot receptionist to embrace both functionalities for multiparty interactions. The module of vision functionality consists of a suite of methods that computationally recognize potential visual cues related to social behavior understanding. The performance of the models is validated by the ground truth annotation dataset. The module of cognitive functionality consists of two computational models that (1) quantify users’ attention saliency and engagement intentions, and (2) facilitate engagement-aware behaviors for the robot to adjust its direction of attention and manage the conversational floor. The performance of the robot’s engagement-aware behaviors is evaluated in a multiparty dialog scenario. The results show that the robot’s engagement-aware behavior based on visual perceptions significantly improve the effectiveness of communication and positively affect user experience.

As the influence of social robots in people’s daily lives grows, research on understanding people’s perception of robots including sociability, trust, acceptance, and preference becomes more pervasive. Research has considered visual, vocal, or tactile cues to express robots’ emotions, whereas little research has provided a holistic view in examining the interactions among different factors influencing emotion perception. We investigated multiple facets of user perception on robots during a conversational task by varying the robots’ voice types, appearances, and emotions. In our experiment, 20 participants interacted with two robots having four different voice types. While participants were reading fairy tales to the robot, the robot gave vocal feedback with seven emotions and the participants evaluated the robot’s profiles through post surveys. The results indicate that (1) the accuracy of emotion perception differed depending on presented emotions, (2) a regular human voice showed higher user preferences and naturalness, (3) but a characterized voice was more appropriate for expressing emotions with significantly higher accuracy in emotion perception, and (4) participants showed significantly higher emotion recognition accuracy with the animal robot than the humanoid robot. A follow-up study ($N=10$) with voice-only conditions confirmed that the importance of embodiment. The results from this study could provide the guidelines needed to design social robots that consider emotional aspects in conversations between robots and users.

Despite the social robotics’ potential to revolutionize human life, the field of social robotics has been less appealing and less popular than other robotics fields in both industrial and research domains. Therefore, many students are unfamiliar with this field. To expedite the development of the social robotics sector, numerous efforts must be made, including introducing students to the topic. In this study, we developed teaching materials and a procedure for introducing university students to the topic of social robotics. A robot demonstration was also utilized to provide participants with a more engaging experience and a deeper knowledge of the importance of social robotics. The ultimate objective of this paper is to provide a reference for lecturers and instructors in introducing social robots to students in the hope that they will become interested in the field, whether for the purposes of research or simply to learn more about this topic. We evaluated the proposed teaching materials and procedures for a small class size based on behavioral and subjective measurements. We also compared the outcome of presentations with and without a robot demonstration. In addition, we performed semi-structured interviews with 10 random participants who wanted to expand our qualitative analysis. Both presentations, with and without robot demonstration, successfully delivered the material. The experimental findings indicated that the robot demonstration had a significant impact on students’ satisfaction with the presentation and their desire to participate in similar presentations in the future.

To look into the assumed difference between East and West in acceptance and use of robots, we performed a content analysis on 120 papers about social robots in two Asian-English (China Daily and The Japan Times) and two Western-English newspapers (The Guardian and New York Times) written between 2009 and 2018. From these papers, we drew a number of statements ($N=118$). We analyzed tone of voice (TOV) as well as the positive or negative framing of the consequences of the implementation of social robots in society, economy, health, and safety. Intercoder reliability $\mathrm{\text{was}}>0.7$, according to Krippendorff’s $\alpha$-reliability. Western newspapers presented significantly more negative social frames, negative fairness-and-equality frames, and negative safety-and-health frames than did Eastern papers, which presented significantly more positive economic frames than did Western papers. Western newspapers expected more negative social, health, safety, and equality issues than did the East. The West anticipated little economic benefit. The East expected little harm to society, safety, health, and equality but rather foresaw beneficial economic outcomes.