I-C-E Framework: Concepts for Group Dynamics Research in Human-Robot Interaction

Abrams, Anna M. H.; der Pütten, Astrid Rosenthal-von

doi:10.31234/osf.io/jyue6

Cited by 3 publications

(3 citation statements)

References 60 publications

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“…Lastly, we also acknowledge that this paper considers cohesion as a broader umbrella of satisfaction and a sense unity of among the team members, which holds two implicit limitations. First, we are blending many specific constructs together (Abrams, & Rosenthal‐von der Pütten, 2020) that require further scrutiny. And second, we are directly transferring constructs from social sciences to human–robot interaction.…”

Section: Discussion and Future Challengesmentioning

confidence: 99%

“…Second, we recognize that the concept itself of cohesive alliances between humans and robots can change over time, considering the presence of robots in our society is increasing every day. For instance, Abrams & Rosenthal‐von der Pütten raised the question of what it means to identify with a human–robot group considering this type of mixed group is not yet established in our daily lives (Abrams & Rosenthal‐von der Pütten, 2020). As a result, we suggest future research to embrace and account for how the mere presence of robotic technologies can influence the way we establish teamwork with robots .…”

Section: Discussion and Future Challengesmentioning

confidence: 99%

“…However, Abrams & Rosenthal‐von der Pütten have recently proposed the Identification‐Cohesion‐Entitativity (I‐C‐E) framework, which highlights important considerations to study cohesion in interactions with robots. Their paper reviews the theoretical foundation to address the concepts of ingroup identification, cohesion, and entitativity in HRI (Abrams, & Rosenthal‐von der Pütten, 2020). The authors clarify the similarities, differences, and relations between these constructs and point out that they have been used interchangeably in the literature.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

When a Robot Is Your Teammate

Correia

Melo

Paiva

2022

Topics in Cognitive Science

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Creating effective teamwork between humans and robots involves not only addressing their performance as a team but also sustaining the quality and sense of unity among teammates, also known as cohesion. This paper explores the research problem of: how can we endow robotic teammates with social capabilities to improve the cohesive alliance with humans? By defining the concept of a human–robot cohesive alliance in the light of the multidimensional construct of cohesion from the social sciences, we propose to address this problem through the idea of multifaceted human–robot cohesion. We present our preliminary effort from previous works to examine each of the five dimensions of cohesion: social, collective, emotional, structural, and task. We finish the paper with a discussion on how human–robot cohesion contributes to the key questions and ongoing challenges of creating robotic teammates. Overall, cohesion in human–robot teams might be a key factor to propel team performance and it should be considered in the design, development, and evaluation of robotic teammates.

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Section: Discussion and Future Challengesmentioning

confidence: 99%

Section: Discussion and Future Challengesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

When a Robot Is Your Teammate

Correia

Melo

Paiva

2022

Topics in Cognitive Science

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Social Dynamics in Human-Robot Groups – Possible Consequences of Unequal Adaptation to Group Members Through Machine Learning in Human-Robot Groups

Pütten

Abrams

2020

Artificial Intelligence in HCI

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MoTiS Parameters for Expressive Multi-Robot Systems: Relative Motion, Timing, and Spacing

Bacula

Knight

2022

Int J of Soc Robotics

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Multi-robot systems are moving into human spaces, such as working with people in factories (Bacula et al., in: Companion of the 2020 ACM/IEEE international conference on human–robot interaction, pp 119–121, 2020) or in emergency support (Wagner in Front Robot AI 8, 2021; Baxter et al., in: Autonomous robots and agents, Springer, pp 9–16, 2007) and it is crucial to consider how robots can communicate with the humans in the space. Our work evaluates a parameter framework to allow multi-robot groups of x, y, robots to effectively communicate using expressive motion. While expressive motion has been extensively studied in single robots (Knight et al., in: 2016 IEEE international conference on intelligent robots and systems (IROS), IEEE, 2016; Bacula and LaViers in Int J Soc Robot, 1–16, 2020; Dragan et al., in: 2013 8th ACM/IEEE international conference on human–robot interaction (HRI), IEEE, pp 301–308, 2013; Kirby et al., in: The 18th IEEE international symposium on robot and human interactive communication, 2009, RO-MAN 2009, IEEE, pp 607–612, 2009), moving to multi-robots creates new challenges as the state space expands and becomes more complex. We evaluate a hierarchical framework of six parameters to generate multi-robot expressive motion consisting of: (1) relative direction, (2) coherence, (3) relative speed, (4) relative start time, (5) proximity, and (6) geometry. We conducted six independent online studies to explore each parameter, finding that four out of six of the parameters had significant impact on people’s perception of the multi-robot group. Additional takeaways of our studies clarify what humans interpret as a robot group, when the group is perceived positively versus negatively, and the critical role of architectural floor plan in interpreting robot intent.

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I-C-E Framework: Concepts for Group Dynamics Research in Human-Robot Interaction

Cited by 3 publications

References 60 publications

When a Robot Is Your Teammate

When a Robot Is Your Teammate

Social Dynamics in Human-Robot Groups – Possible Consequences of Unequal Adaptation to Group Members Through Machine Learning in Human-Robot Groups

MoTiS Parameters for Expressive Multi-Robot Systems: Relative Motion, Timing, and Spacing

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