Estimating F-Formations for Mobile Robotic Telepresence

Pathi, Sai Krishna; Kiselev, Andrey; Loutfi, Amy

doi:10.1145/3029798.3038304

Cited by 8 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a free-standing conversational group, Kendon [10] proposed the F-formation system to define the positions and orientations of individuals within a group. F-formations and other group formation models have been studied computationally [11][12][13][14][15] with potential applications to mobile robots [16] and wheelchairs [17], and have been used as a basis for joining group behaviors of a mobile robot [18][19][20][21]. Truong et al [18] proposed a framework that enables a robot to approach a group safely and socially.…”

Section: Group Interaction Researchmentioning

confidence: 99%

A dataset of human and robot approach behaviors into small free-standing conversational groups

Yang

Gao

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

The analysis and simulation of the interactions that occur in group situations is important when humans and artificial agents, physical or virtual, must coordinate when inhabiting similar spaces or even collaborate, as in the case of human-robot teams. Artificial systems should adapt to the natural interfaces of humans rather than the other way around. Such systems should be sensitive to human behaviors, which are often social in nature, and account for human capabilities when planning their own behaviors. A limiting factor relates to our understanding of how humans behave with respect to each other and with artificial embodiments, such as robots. To this end, we present CongreG8 (pronounced ‘con-gre-gate’), a novel dataset containing the full-body motions of free-standing conversational groups of three humans and a newcomer that approaches the groups with the intent of joining them. The aim has been to collect an accurate and detailed set of positioning, orienting and full-body behaviors when a newcomer approaches and joins a small group. The dataset contains trials from human and robot newcomers. Additionally, it includes questionnaires about the personality of participants (BFI-10), their perception of robots (Godspeed), and custom human/robot interaction questions. An overview and analysis of the dataset is also provided, which suggests that human groups are more likely to alter their configuration to accommodate a human newcomer than a robot newcomer. We conclude by providing three use cases that the dataset has already been applied to in the domains of behavior detection and generation in real and virtual environments. A sample of the CongreG8 dataset is available at https://zenodo.org/record/4537811.

show abstract

Section: Group Interaction Researchmentioning

confidence: 99%

A dataset of human and robot approach behaviors into small free-standing conversational groups

Yang

Gao

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Studies of formations have been considered in the HRI community [26][27][28][29]. The influence of F-formations during a task in which a child taught a robot was explored in [26].…”

Section: Background and Related Workmentioning

confidence: 99%

“…The challenge of detecting the F-formations using a mobile robot based on lower body estimation, which is obtained by tracking the position and orientation of the people in the scene using an exocentric camera (overhead video data set), was explored in [27]. In our previous work [28], we explored F-formations and detected the F-formations based on the face orientation, and in [29] we proposed an optimal placement for a robot in social group interactions. There are also many works such as [3,16,[30][31][32][33] that deal with proxemics, F-formations, and MRP systems, but not many works have studied the teleoperator's placement in the group and whether they would place themselves according to F-formations.…”

Section: Background and Related Workmentioning

confidence: 99%

F-Formations for Social Interaction in Simulation Using Virtual Agents and Mobile Robotic Telepresence Systems

Pathi

Kristoffersson

Kiselev

et al. 2019

MTI

Self Cite

View full text Add to dashboard Cite

F-formations are a set of possible patterns in which groups of people tend to spatially organize themselves while engaging in social interactions. In this paper, we study the behavior of teleoperators of mobile robotic telepresence systems to determine whether they adhere to spatial formations when navigating to groups. This work uses a simulated environment in which teleoperators are requested to navigate to different groups of virtual agents. The simulated environment represents a conference lobby scenario where multiple groups of Virtual Agents with varying group sizes are placed in different spatial formations. The task requires teleoperators to navigate a robot to join each group using an egocentric-perspective camera. In a second phase, teleoperators are allowed to evaluate their own performance by reviewing how they navigated the robot from an exocentric perspective. The two important outcomes from this study are, firstly, teleoperators inherently respect F-formations even when operating a mobile robotic telepresence system. Secondly, teleoperators prefer additional support in order to correctly navigate the robot into a preferred position that adheres to F-formations.

show abstract

“…Regarding group patterns estimation, not many functional models are proposed. Some of our works [26,27] are proposed to estimate F-formations, but study small groups sizes, i.e., two or three people per group. Recently, few works are proposed in regards to mobile robot using on-board sensors to interact with group.…”

Section: Introductionmentioning

confidence: 99%

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

Pathi

Kiselev

Loutfi

2022

MTI

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Estimating F-Formations for Mobile Robotic Telepresence

Cited by 8 publications

References 5 publications

A dataset of human and robot approach behaviors into small free-standing conversational groups

A dataset of human and robot approach behaviors into small free-standing conversational groups

F-Formations for Social Interaction in Simulation Using Virtual Agents and Mobile Robotic Telepresence Systems

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

Contact Info

Product

Resources

About