An interdisciplinary taxonomy of social cues and signals in the service of engineering robotic social intelligence

Wiltshire, Travis J.; Lobato, Emilio J. C.; Velez, Jonathan; Jentsch, Florian; Fiore, Stephen M.

doi:10.1117/12.2049933

Cited by 7 publications

(18 citation statements)

References 89 publications

(105 reference statements)

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“…This is a simple example where the social cues involved are relatively few in number and displayed solely by the other agent. The reality is that the range and complexity of the relationship between social cues and signals is complex and still a challenge for researchers to appropriately understand, yet alone attempt to model (see Vinciarelli et al, 2012;Wiltshire, Lobato, Velez, Jentsch, & Fiore, 2014). Nonetheless, SJT and the Lens Model offer reasonable steps forward to help address this problem (see Cooksey, 1996 for details of the methodology of SJT and for more complex iterations of the Lens Model).…”

mentioning

confidence: 99%

Leveraging Social Judgment Theory to Examine the Relationship between Social Cues and Signals in Human-Robot Interactions

Wiltshire

Snow

Lobato

et al. 2014

Proceedings of the Human Factors and Ergonomics Society Annual

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Human-robot interaction (HRI) research requires new techniques for understanding the social dynamics that occur at the interface between humans and robots. Prior work has focused on incorporating the social cues and social signals distinction from the field of social signal processing and complementing this with recent advances in understanding human social cognition that specify two primary types of cognitive processes. A related account, stemming from Social Judgment Theory (SJT), specifies a Lens Model for which cues can be interpreted as well as the task conditions that would induce either of the types of cognitive processes. Surprisingly, SJT-based research has not yet examined the social cue and signal relationship. We argue it provides an ideal path forward for such research and we integrate these related disciplines of study to provide a theoretically derived account that can be useful for both the design of humanhuman and HRI experiments focused on social interaction dynamics.

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mentioning

confidence: 99%

Leveraging Social Judgment Theory to Examine the Relationship between Social Cues and Signals in Human-Robot Interactions

Wiltshire

Snow

Lobato

et al. 2014

Proceedings of the Human Factors and Ergonomics Society Annual

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“…According to ref. [20], proxemic behavior falls under interdisciplinary taxonomy of social cues and signals in the service of engineered social intelligence in robots.…”

Section: Related Workmentioning

confidence: 99%

A probabilistic evaluation of human activity space for proactive approach behavior of a social robot

Sirithunge

Bandara

Jayasekara

et al. 2020

Paladyn, Journal of Behavioral Robotics

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Intelligent robot companions contribute significantly to improve the living standards of people in the modern society. Therefore, humanlike decision-making skills are sought after during the design of such robots. On the one hand, such features enable the robot to be easily handled by its human user. On the other hand, the robot will have the capability of dealing with humans without disturbing them by its behavior. Perception of Behavioral Ontology prior to an interaction is an important aspect in this regard. Furthermore, humans make an instant evaluation of task-related movements of others before approaching them. In this article, we present a mechanism to monitor how the activity space is utilized by a particular user on a temporal basis as an ontological assessment of the situation and then determine an appropriate approach behavior for a proactive robot to initiate an interaction with its user. This evaluation was then used to determine appropriate proxemic behavior to approach that person. The usage of activity space varies depending on the task of an individual. We used a probabilistic approach to find the areas that are the most and least likely to be occupied within the activity space of a particular individual during various tasks. As the robot approaches its subject after analyzing the spatial behavior of the subject within his/her activity space, spatial constraints occurred as a result of which robot’s movement could be demolished. Hence, a more socially acceptable spatial behavior could be observed from the robot. In other words, an etiquette based on approach behavior is derived considering the user’s activity space. Experiment results used to validate the system are presented, and critical observations during the study and implications are discussed.

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“…Shim and Arkin [19] define a taxonomy of robot deception for HRI contexts. Wiltshire et al [20] propose a taxonomy of social signals from an interdisciplinary point of view. They categorize five social cues that can be extracted to predict social signals.…”

Section: Related Workmentioning

confidence: 99%

A taxonomy of preferences for physically assistive robots

Canal

Alenyà

Torras

2017

2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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Abstract-Assistive devices and technologies are getting common and some commercial products are starting to be available. However, the deployment of robots able to physically interact with a person in an assistive manner is still a challenging problem. Apart from the design and control, the robot must be able to adapt to the user it is attending in order to become a useful tool for caregivers. This robot behavior adaptation comes through the definition of user preferences for the task such that the robot can act in the user's desired way. This article presents a taxonomy of user preferences for assistive scenarios, including physical interactions, that may be used to improve robot decision-making algorithms. The taxonomy categorizes the preferences based on their semantics and possible uses. We propose the categorization in two levels of application (global and specific) as well as two types (primary and modifier). Examples of real preference classifications are presented in three assistive tasks: feeding, shoe fitting and coat dressing.

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An interdisciplinary taxonomy of social cues and signals in the service of engineering robotic social intelligence

Cited by 7 publications

References 89 publications

Leveraging Social Judgment Theory to Examine the Relationship between Social Cues and Signals in Human-Robot Interactions

Leveraging Social Judgment Theory to Examine the Relationship between Social Cues and Signals in Human-Robot Interactions

A probabilistic evaluation of human activity space for proactive approach behavior of a social robot

A taxonomy of preferences for physically assistive robots

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