Human-Robot Interaction in Groups: Methodological and Research Practices

Oliveira, Raquel; Arriaga, Patrí­cia; Paiva, Ana

doi:10.3390/mti5100059

Cited by 20 publications

(6 citation statements)

References 129 publications

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“…On the other hand, when we broaden the scope of our enquiry, we might be able to extend the notion of sociomorphing: we propose to apply it to the analysis of socio-technical hybrid interactional systems. Since now, we mostly lack a theoretical framework able to capture the complexity of interactional networks where not all the actors are humans (Oliveira et al 2021). This is one of the main key differences between social robots and other "social technologies" as social networks.…”

Section: Sociomorph Interactional Network and Antmentioning

confidence: 99%

“…In our opinion, in order to observe the phenomenon of sociomorphing at the level of interactional networks, we should dismiss 1-to-1 HRI experiments, and we should focus on robots interacting with groups of humans. Some experiments have been carried out, but the research on this field still lacks methodologies (Oliveira 2021).…”

Section: Processes Of Negotiation and Robots Spokespersonsmentioning

confidence: 99%

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Sociomorphing and an Actor-Network Approach to Social Robotics

Bisconti

Possati

2023

Social Robots in Social Institutions

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Most of human-robot interaction (HRI) research relies on an implicit assumption that seems to drive experimental work in interaction studies: the more anthropomorphism we can reach in robots, the more effective the robot will be in “being social.” The notion of “sociomorphing” was developed in order to challenge the assumption of ubiquitous anthropomorphizing. This paper aims to explore the notion of sociomorphing by analysing the possibilities offered by actor-network theory (ANT). We claim that ANT is a valid framework to re-think the conceptual couple anthropomorphizing / sociomorphing and answer the following question: What kind of negotiation process and social practices can be developed in HRI, given the notion of sociomorph interactional networks?

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Section: Sociomorph Interactional Network and Antmentioning

confidence: 99%

Section: Processes Of Negotiation and Robots Spokespersonsmentioning

confidence: 99%

Sociomorphing and an Actor-Network Approach to Social Robotics

Bisconti

Possati

2023

Social Robots in Social Institutions

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“…Furthermore, from individual facial expressions, in [ 62 ], a system to recognise group emotion for an entertainment robot was described. In [ 63 ], research on HRI in small groups was carried out, concluding that groups are complex, adaptable, and dynamic systems. The authors recommended developing suitable robots for group interactions and improving the methodologies used in the process of measuring human and robotic behaviour in situations involving HRI.…”

Section: Related Workmentioning

confidence: 99%

Group Emotion Detection Based on Social Robot Perception

Quiroz

Patiño

Díaz-Amado

et al. 2022

Sensors

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Social robotics is an emerging area that is becoming present in social spaces, by introducing autonomous social robots. Social robots offer services, perform tasks, and interact with people in such social environments, demanding more efficient and complex Human–Robot Interaction (HRI) designs. A strategy to improve HRI is to provide robots with the capacity of detecting the emotions of the people around them to plan a trajectory, modify their behaviour, and generate an appropriate interaction with people based on the analysed information. However, in social environments in which it is common to find a group of persons, new approaches are needed in order to make robots able to recognise groups of people and the emotion of the groups, which can be also associated with a scene in which the group is participating. Some existing studies are focused on detecting group cohesion and the recognition of group emotions; nevertheless, these works do not focus on performing the recognition tasks from a robocentric perspective, considering the sensory capacity of robots. In this context, a system to recognise scenes in terms of groups of people, to then detect global (prevailing) emotions in a scene, is presented. The approach proposed to visualise and recognise emotions in typical HRI is based on the face size of people recognised by the robot during its navigation (face sizes decrease when the robot moves away from a group of people). On each frame of the video stream of the visual sensor, individual emotions are recognised based on the Visual Geometry Group (VGG) neural network pre-trained to recognise faces (VGGFace); then, to detect the emotion of the frame, individual emotions are aggregated with a fusion method, and consequently, to detect global (prevalent) emotion in the scene (group of people), the emotions of its constituent frames are also aggregated. Additionally, this work proposes a strategy to create datasets with images/videos in order to validate the estimation of emotions in scenes and personal emotions. Both datasets are generated in a simulated environment based on the Robot Operating System (ROS) from videos captured by robots through their sensory capabilities. Tests are performed in two simulated environments in ROS/Gazebo: a museum and a cafeteria. Results show that the accuracy in the detection of individual emotions is 99.79% and the detection of group emotion (scene emotion) in each frame is 90.84% and 89.78% in the cafeteria and the museum scenarios, respectively.

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“…How should the interaction interface of social robots be most appropriately designed so that the interaction can proceed in the best possible way? Likewise, one difficulty is successfully inserting social robots into group interactions, as well as how they should or can be best used and analyzed in group interactions as Oliveira et al (2021) states. Not to be neglected are also the psychological implications.…”

Section: Introductionmentioning

confidence: 99%

A bibliometric analysis on reciprocal human-machine-interactions

Erdmann,

Sauer,

Suárez Ruz

et al. 2023

Libro De Actas.

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Research into artificial intelligence is not a very young field; its precursors can be traced back as far as the 16th century. Today's technical development, however, is virtually leaping forward, with new intelligent chat systems and social robots playing no small part in this. This is revolutionizing a wide range of scientific and social fields. The very large publication numbers in this field illustrate this as well. In order for academics and scientists to keep track of the discourse in the field, the representatives of the field, the publications as well as the topics and their future development, it is indispensable to prepare these in a bibliometric analysis. Only in this way is it possible to uncover thematic gaps as well as further points of contact and to drive research forward in a targeted and stringent manner. It is precisely this sorting and processing of the research discourse, the topics, and the authors, which is necessary for further research, that is carried out in this paper. For this purpose, using bibliometric analysis tools, an overview of the past, present and future of the research field is created, and the general relevant topics are uncovered. The analysis includes as performance analysis a) the total number of publications and b) the total number of citations, and for science mapping c) a co-citation analysis (past), d) a bibliographic coupling (present) and e) a co-word analysis (future). The data needed for the analysis are identified and extracted from the SCOPUS or Web of Science (ISI) databases.

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Human-Robot Interaction in Groups: Methodological and Research Practices

Cited by 20 publications

References 129 publications

Sociomorphing and an Actor-Network Approach to Social Robotics

Sociomorphing and an Actor-Network Approach to Social Robotics

Group Emotion Detection Based on Social Robot Perception

A bibliometric analysis on reciprocal human-machine-interactions

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