A spatial model of engagement for a social robot

Michalowski, Marek P.; Šabanović, Selma; Simmons, Reid

doi:10.1109/amc.2006.1631755

Cited by 154 publications

(116 citation statements)

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“…Bayesian inference algorithms and Hidden Markov Models have also successfully been applied to modelling and for predicting spatial user information Govea (2007 Hanajima et al (2005). In Michalowski et al (2006) models are reviewed that describe social engagement based on the spatial relationships between a robot and a person, with emphasis on the movement of the person. Although the robot is not perceived as a human when encountering people, the hypothesis is that robot behavioural reactions with respect to motion should resemble human-human scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…Several sensors have been used, including 2D and 3D vision (Dornaika & Raducanu, 2008;Munoz-Salinas et al, 2005), thermal tracking (Cielniak et al, 2005) or range scans (Fod et al, 2002;Kirby et al, 2007;Rodgers et al, 2006;Xavier et al, 2005). Laser scans are typically used for person detection, whereas the combination with cameras also produces pose estimates Feil-Seifer & Mataric (2005); Michalowski et al (2006). Using face detection requires the person to always face the robot, and that person to be close enough to be able to obtain a sufficiently high resolution image of the face Kleinehagenbrock et al (2002), limiting the use in environments where people are moving and turning frequently.…”

Section: Related Workmentioning

confidence: 99%

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Navigation Among Humans

Svenstrup¹

2011

Advances in Robot Navigation

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Navigation Among Humans

Svenstrup¹

2011

Advances in Robot Navigation

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“…In [6] the problem is relaxed by only inquiring if the person is looking at the system or not. In [13] detecting a frontal face at a suitable spatial location is enough to adjust the classification to a higher level of engagement. In other works such as it is not clear how they solve the task.…”

Section: Related Workmentioning

confidence: 99%

Recognizing the Visual Focus of Attention for Human Robot Interaction

Sheikhi

Odobez

2012

Human Behavior Understanding

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Abstract. We address the recognition of people's visual focus of attention (VFOA), the discrete version of gaze that indicates who is looking at whom or what. As a good indicator of addressee-hood (who speaks to whom, and in particular is a person speaking to the robot) and of people's interest, VFOA is an important cue for supporting dialog modelling in Human-Robot interactions involving multiple persons. In absence of high definition images, we rely on people's head pose to recognize the VFOA. Rather than assuming a fixed mapping between head pose directions and gaze target directions, we investigate models that perform a dynamic (temporal) mapping implicitly accounting for varying body/shoulder orientations of a person over time, as well as unsupervised adaptation. Evaluated on a public dataset and on data recorded with the humanoid robot Nao, the method exhibit better adaptivity and versatility producing equal or better performance than a state-of-the-art approach, while the proposed unsupervised adaptation does not improve results.

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“…How using this information to plan an appropriate interaction initiation is still a challenging issue, although some work was proposed in the literature. Michalowski et al [6], for example, proposed an approach based on social space to categorise different stages of user engagement with a robot, such as present, attending, engaged and interacting. Peters proposed a perceptually-based theory of mind model for interaction initiation applied to virtual agents [7] and evaluated user perception of attention behaviours for interaction initiation in virtual environments [8].…”

Section: Introductionmentioning

confidence: 99%

Ubiquitous social perception abilities for interaction initiation in human-robot interaction

Deshmukh

Castellano

Lim

et al. 2010

Proceedings of the 3rd International Workshop on Affective Interaction in Natural Environments

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Robots acting as assistants or companions in a social environment must be capable of sensing information about the location of the users and analysing and interpreting their social, affective signals in order to be able to plan and generate an appropriate response. Social perception abilities are thereby very important for the robot to evaluate whether it is appropriate or not to initiate an interaction with the user. In this paper we present the initial steps of the design of a ubiquitous social perception system for interaction initiation: users' social signals and expressive behaviour are analysed at different spatial locations and temporal instants. We propose an approach to evaluate whether it is appropriate for a robot to initiate an interaction with the user. We describe an autonomous algorithm to regulate the inter-entity distance between the robot and a person using visual face detection which can be used during interaction initiation and also discuss the role of memory abilities to remember what has happened throughout the interaction. * This work is supported by European Community (EC) and is currently funded by the EU FP7 ICT-215554 project LIREC (LIving with Robots and IntEractive Companions). The authors are solely responsible for the content of this publication. It does not represent the opinion of the EC, and the EC is not responsible for any use that might be made of data appearing therein.

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A spatial model of engagement for a social robot

Cited by 154 publications

References 12 publications

Navigation Among Humans

Navigation Among Humans

Recognizing the Visual Focus of Attention for Human Robot Interaction

Ubiquitous social perception abilities for interaction initiation in human-robot interaction

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