Dynamics of social positioning patterns in group-robot interactions

Vroon, Jered; Joosse, Michiel; Lohse, Manja; Kolkmeier, Jan; Kim, Jaebok; Truong, Khiet P.; Englebienne, Gwenn; Heylen, Dirk; Evers, Vanessa

doi:10.1109/roman.2015.7333633

Cited by 18 publications

(9 citation statements)

References 17 publications

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“…In the literature, many works have studied F-formation patterns either to investigate the different F-formation arrangements that emerged during an interaction between a robot and human [61][62][63] or to investigate the quality of interaction between the robot and humans [64][65][66]. However, only a handful of works have proposed approaches to recognise the spatial patterns [28,29], which are further used to interact with groups of people.…”

Section: Estimating F-formationsmentioning

confidence: 99%

Detecting Groups and Estimating F-Formations for Social Human–Robot Interactions

Pathi

Kiselev

Loutfi

2022

MTI

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The ability of a robot to detect and join groups of people is of increasing importance in social contexts, and for the collaboration between teams of humans and robots. In this paper, we propose a framework, autonomous group interactions for robots (AGIR), that endows a robot with the ability to detect such groups while following the principles of F-formations. Using on-board sensors, this method accounts for a wide spectrum of different robot systems, ranging from autonomous service robots to telepresence robots. The presented framework detects individuals, estimates their position and orientation, detects groups, determines their F-formations, and is able to suggest a position for the robot to enter the social group. For evaluation, two simulation scenes were developed based on the standard real-world datasets. The 1st scene is built with 20 virtual agents (VAs) interacting in 7 different groups of varying sizes and 3 different formations. The 2nd scene is built with 36 VAs, positioned in 13 different groups of varying sizes and 6 different formations. A model of a Pepper robot is used in both simulated scenes in randomly generated different positions. The ability for the robot to estimate orientation, detect groups, and estimate F-formations at various locations is used to determine the validation of the approaches. The obtained results show a high accuracy within each of the simulated scenarios and demonstrates that the framework is able to work from an egocentric view with a robot in real time.

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Section: Estimating F-formationsmentioning

confidence: 99%

Detecting Groups and Estimating F-Formations for Social Human–Robot Interactions

Pathi

Kiselev

Loutfi

2022

MTI

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“…Social space formed within static group formations should be respected by mobile robots. Vázquez et al [243] and Vroon et al [247] have studied how a robot may affect the spatial behavior of static groups by performing certain actions. A common tool used to analyse static groups is the F-formation Kendon [113], which was used by Kuzuoka et al [128] to also analyse how a robot's action can affect static group spatial arrangements.…”

Section: Formations and Social Spacesmentioning

confidence: 99%

Core Challenges of Social Robot Navigation: A Survey

Mavrogiannis¹,

Baldini²,

Wang³

et al. 2021

Preprint

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Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progress and the massive recent interest, we observe a number of significant remaining challenges that prohibit the seamless deployment of autonomous robots in public pedestrian environments. In this survey article, we organize existing challenges into a set of categories related to broader open problems in motion planning, behavior design, and evaluation methodologies. Within these categories, we review past work, and offer directions for future research. Our work builds upon and extends earlier survey efforts by a) taking a critical perspective and diagnosing fundamental limitations of adopted practices in the field and b) offering constructive feedback and ideas that we aspire will drive research in the field over the coming decade.

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“…For example, Vazquez et al [134] investigated how a robot's role during a game impacts its human group partner's spatial behaviors. Vroon et al [136] designed a reactive system that generates hypotheses for social positioning of approach, retreat, and converse behaviors while solving a group task.…”

Section: Groups In Hrimentioning

confidence: 99%

“…First, it can be used in group tracking pipelines to improve their performance. Unlike previous approaches in the literature that only work in controlled environments and employ stationary cameras, our method is designed for ego-centric, color and depth (RGB-D) perception, encompassing the challenging problem of moving people from a mobile platform [65,85,136]. We collected an RGB-D dataset that was recorded in a crowded, sunny, outdoor environment, which caused many computer vision challenges such as occlusion, shadow, and varying lighting illuminations.…”

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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Dynamics of social positioning patterns in group-robot interactions

Cited by 18 publications

References 17 publications

Detecting Groups and Estimating F-Formations for Social Human–Robot Interactions

Detecting Groups and Estimating F-Formations for Social Human–Robot Interactions

Core Challenges of Social Robot Navigation: A Survey

Robot-Centric Perception of Human Groups

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