Break the Ice: a Survey on Socially Aware Engagement for Human–Robot First Encounters

Avelino, João; Garcia­‐Marques, Leonel; Ventura, Rodrigo; Bernardino, Alexandre

doi:10.1007/s12369-020-00720-2

Cited by 22 publications

(7 citation statements)

References 123 publications

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“…However, none has been transdisciplinary in nature and taken a service focus. Most reviews have restrictive foci: non‐verbal robotic communication (Saunderson & Nejat, 2019), emotions in HRI (Stock‐Homburg, 2021), service failure (Honig & Oron‐Gilad, 2018), first encounters (Avelino et al., 2021), ethical considerations related to HRI (Boada et al., 2021; Tan et al., 2021), social acceptance of robots in different occupational fields (Savela et al., 2018), social robots to combat loneliness (Gasteiger et al., 2021), or quantifiable evidence of human attitudes toward social robots (Naneva et al., 2020). Others have been restricted to a specific social robot model (i.e., NAO; Robaczewski et al., 2021) or context, such as elderly care (i.e., socially assistive robots [SAR]; Kachouie et al., 2014; Vandemeulebroucke et al., 2021; Wang et al., 2022), education (Woo et al., 2021), or hospitality (Ivanov et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

A transdisciplinary review and framework of consumer interactions with embodied social robots: Design, delegate, and deploy

Blaurock

Čaić

Okan

et al. 2022

Int J Consumer Studies

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Social robots are increasingly being deployed in a wide variety of consumer-facing services, where they co-create value with and for the benefit of the consumers they interact with (Lu et al., 2020;Wirtz et al., 2018). Robots welcome customers to restaurants and hotels, entertain children, read cooking recipes at home, give additional information about products in stores, or assist the elderly with walking to support their health (Henschel et al., 2021;KPMG, 2016). What all these robots delivering services to consumers have in common is that they represent an "information technology in a physical embodiment, providing customized services by performing physical as well as nonphysical tasks with a high degree of autonomy" (Jörling et al., 2019, p. 405). This integration of robots into the marketplace reshapes service interactions and also challenges some fundamental principles of consumer-firm interactions (Kaartemo & Helkkula, 2018;Subramony et al., 2018). While service robots come with different levels of intelligence (Huang & Rust, 2018) and in various manifestations (Wirtz et al., 2018), embodied robots engaging in social interactions with consumers are expected to ignite what could be the most dramatic transformation of the consumer service landscape in the age of ser-

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Section: Introductionmentioning

confidence: 99%

A transdisciplinary review and framework of consumer interactions with embodied social robots: Design, delegate, and deploy

Blaurock

Čaić

Okan

et al. 2022

Int J Consumer Studies

View full text Add to dashboard Cite

show abstract

“…Nonverbal communication cues [21], such as body motion and language, play a central role in HRI, from both users' and robots' perspective [22,23]. However, the perception of social nonverbal behaviors is a challenging task to solve in HRI [24], especially for the first phases of the interactions [8]. Nonetheless, it is important to be able to predict the intention to interact with the robot so that an effective reaction strategy can be well accommodated to the users' needs.…”

Section: Related Workmentioning

confidence: 99%

“…The very initial phase of these interactions plays an important role in establishing an effective Human-Robot Interaction (HRI), in which the user first sees the robot and decides to approach and engage with it. When users are unfamiliar with the situation, e.g., because they enter a new environment and are unsure about the right action to take, the robot's behavior is crucial to determine if this approach phase yields a successful interaction and a good user experience [8].…”

Section: Introductionmentioning

confidence: 99%

Self-supervised prediction of the intention to interact with a service robot

Abbate,

Giusti,

Schmuck

et al. 2024

Robotics and Autonomous Systems

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“…For robots to operate effectively in society, they need to understand various social norms and individual user-behaviour as inaccurate situational behaviour can decrease trust in social robots [14]. This is particularly challenging while learning appropriate behaviours during the first interactions with a user [15] as first impressions may impact competence ratings subsequently [16]. A key factor to consider while designing robots for human-centric environments is how they position themselves while interacting with humans.…”

Section: B Learning Socially Appropriate Behaviours In Hrimentioning

confidence: 99%

Learning Socially Appropriate Robo-waiter Behaviours through Real-time User Feedback

McQuillin

Churamani

Güneş

2022

2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

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Current Humanoid Service Robot (HSR) behaviours mainly rely on static models that cannot adapt dynamically to meet individual customer attitudes and preferences. In this work, we focus on empowering HSRs with adaptive feedback mechanisms driven by either implicit reward, by estimating facial affect, or explicit reward, by incorporating verbal responses of the human 'customer'. To achieve this, we first create a custom dataset, annotated using crowd-sourced labels, to learn appropriate approach (positioning and movement) behaviours for a Robowaiter. This dataset is used to pre-train a Reinforcement Learning (RL) agent to learn behaviours deemed socially appropriate for the robo-waiter. This model is later extended to include separate implicit and explicit reward mechanisms to allow for interactive learning and adaptation from user social feedback. We present a within-subjects Human-Robot Interaction (HRI) study with 21 participants implementing interactions between the robowaiter and human customers implementing the above-mentioned model variations. Our results show that both explicit and implicit adaptation mechanisms enabled the adaptive robo-waiter to be rated as more enjoyable and sociable, and its positioning relative to the participants as more appropriate compared to using the pre-trained model or a randomised control implementation.

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Break the Ice: a Survey on Socially Aware Engagement for Human–Robot First Encounters

Cited by 22 publications

References 123 publications

A transdisciplinary review and framework of consumer interactions with embodied social robots: Design, delegate, and deploy

A transdisciplinary review and framework of consumer interactions with embodied social robots: Design, delegate, and deploy

Self-supervised prediction of the intention to interact with a service robot

Learning Socially Appropriate Robo-waiter Behaviours through Real-time User Feedback

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