Groups of humans and robots: Understanding membership preferences and team formation

Correia, Filipa; Petisca, Sofia; Alves‐Oliveira, Patrícia; Ribeiro, Tiago; Melo, Francisco S.; Paiva, Ana

doi:10.15607/rss.2017.xiii.024

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…Robots and humans have inevitably come together as teams to complete tasks and share resources (Correia et al, 2019). The appearance and behavior of robots can be very similar to humans, and we treat robots as if they possess human attributes, including morality (Coeckelbergh, 2014).…”

Section: Purpose and Hypothesesmentioning

confidence: 99%

Effects of morality and reputation on sharing behaviors in human-robot teams

Chen,

Hu,

Zhai

2023

Front. Psychol.

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IntroductionThe relationship between robots and humans is becoming increasingly close and will become an inseparable part of work and life with humans and robots working together. Sharing, which involves distributing goods between individuals and others, involves individuals as potential beneficiaries and the possibility of giving up the interests of others. In human teams, individual sharing behaviors are influenced by morality and reputation. However, the impact on individuals’ sharing behaviors in human-robot collaborative teams remains unclear-individuals may consider morality and reputation differently when sharing with robot or human partners. In this study, three experiments were conducted using the dictator game paradigm, aiming to compare the effects and mechanisms of morality and reputation on sharing behaviors in human and human-robot teams.MethodsExperiment 1 involving 18 participants was conducted. Experiment 2 involving 74 participants was conducted. Experiment 3 involving 128 participants was conducted.ResultsExperiment 1 validated the differences in human sharing behaviors when the agents were robots and humans. Experiment 2 verifies that moral constraints and reputation constraints affect sharing behaviors in human-robot teams. Experiment 3 further reveals the mechanism of differences in sharing behaviors in human-robot teams, where reputation concern plays a mediating role in the impact of moral constraint on sharing behaviors, and the agent type plays a moderating role in the impact of moral constraint on reputation concern and sharing behaviors.DiscussionThe results of this study contribute to a better understanding of the interaction mechanism of human-robot teams. In the future, the formulation of human-robot collaborative team rules and the setting of interaction environments can consider the potential motivation of human behavior from both morality and reputation perspectives and achieve better work performance.

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Section: Purpose and Hypothesesmentioning

confidence: 99%

Effects of morality and reputation on sharing behaviors in human-robot teams

Chen,

Hu,

Zhai

2023

Front. Psychol.

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“…Lastly, we performed a behavioral analysis on the data collected in the aforementioned (Raque et al, 2018;Oliveira, Arriaga, Correia, & Paiva, 2020), using a coding scheme based on Bales Interaction Process Analysis (Bales, 1950). We found that the structure of the group's communication is different according to the goal orientation of the robot and according to 538 F. Correia, F. S. Melo, A. Paiva / Topics in Cognitive Science 16 (2024) Table 2 Analysis of the first use case (Correia et al, 2017a;Correia et al, 2017bCorreia et al, , 2018b -Choice of robot for hypothetical future game their roles. Overall, the occurrence of different behavioral patterns in competitive and collaborative interactions with robots suggests that there might be a relationship between social cohesion and structural cohesion in human-robot interactions.…”

Section: A Use Case On Social Cohesionmentioning

confidence: 99%

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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“…For instance, some researchers explored how robots can help mitigate conflicts in teams and how a robot's gaze influences its teammates' perception of decision making [65,110]. Additionally, Correia et al [29] explored how human group members in a two-human, two-robot group generate their membership preferences to these robots based on the robot's behaviors.…”

Section: Groups In Hrimentioning

confidence: 99%

“…In these environments, robots are often tasked with interacting with groups of people. Thus, it is 15:2 A. Taylor et al important that robots have an adequate understanding of social groups [22,30,44,59,61,62,66,77,79,83,84,90,96,129,132,133].…”

Section: Introductionmentioning

confidence: 99%

Robot-Centric Perception of Human Groups

Taylor

Chan

Riek

2020

J. Hum.-Robot Interact.

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The robotics community continually strives to create robots that are deployable in real-world environments. Often, robots are expected to interact with human groups. To achieve this goal, we introduce a new method, the Robot-Centric Group Estimation Model (RoboGEM), which enables robots to detect groups of people. Much of the work reported in the literature focuses on dyadic interactions, leaving a gap in our understanding of how to build robots that can effectively team with larger groups of people. Moreover, many current methods rely on exocentric vision, where cameras and sensors are placed externally in the environment, rather than onboard the robot. Consequently, these methods are impractical for robots in unstructured, human-centric environments, which are novel and unpredictable. Furthermore, the majority of work on group perception is supervised, which can inhibit performance in real-world settings. RoboGEM addresses these gaps by being able to predict social groups solely from an egocentric perspective using color and depth (RGB-D) data. To achieve group predictions, RoboGEM leverages joint motion and proximity estimations. We evaluated RoboGEM against a challenging, egocentric, real-world dataset where both pedestrians and the robot are in motion simultaneously, and show RoboGEM outperformed two state-of-the-art supervised methods in detection accuracy by up to 30%, with a lower miss rate. Our work will be helpful to the robotics community, and serve as a milestone to building unsupervised systems that will enable robots to work with human groups in real-world environments.

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Groups of humans and robots: Understanding membership preferences and team formation

Cited by 8 publications

References 28 publications

Effects of morality and reputation on sharing behaviors in human-robot teams

Effects of morality and reputation on sharing behaviors in human-robot teams

When a Robot Is Your Teammate

Robot-Centric Perception of Human Groups

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