Long-term assessment of a service robot in a hotel environment

Pinillos, Roberto; Marcos, Samuel; Feliz, Raúl; Zalama, Eduardo; Gómez-García-Bermejo, Jaime

doi:10.1016/j.robot.2016.01.014

Cited by 134 publications

(91 citation statements)

References 18 publications

(17 reference statements)

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“…Moreover, many studies have tested the performance of various social robots in service encounters (e.g. at information desks [24,32,40], information broadcasting [26,27], bellboy tasks [28], and customer feedback gathering [7]).…”

Section: Background and Issuementioning

confidence: 99%

“…Various robots have already been employed in service encounters in hotels [15,38], and researchers have investigated using social robots for more complex tasks involving human-robot interactions in hotel environments. For instance, a long-term assessment with a humanoid robot was conducted to test and improve robots' ability to perform bellboy tasks [28]. In the experiment, bellboy tasks involved guiding customers, providing useful information, and providing other hotel-related services.…”

Section: Social Robots In a Hotelmentioning

confidence: 99%

“…The first form of collaboration involves using robots as a replacement for human labour. Actually, most studies have developed social robots capable of engaging in tasks typically performed by humans [24,28,32,40], implying an intention to replace human labour with social robots. Hennna hotel advocates replacement of human labour with robots for ultimate efficiency [15,25].…”

Section: Collaborative Service Design For Social Robots In a Hotelmentioning

confidence: 99%

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Continuous Hospitality with Social Robots at a hotel

et al. 2020

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Social robots are being increasingly employed in service encounters at hotels. This study explored the possibility that social robots can engage in heartwarming interactions with hotel customers. A collaboration design known as 'Continuous Hospitality with Social Robots' , in which social robots compensate for gaps in hospitality through heartwarming interaction, was evaluated. A field test was conducted in which social robots engaged in heartwarming interaction with customers in a public area of a hotel and then collected customers' impressions of the social robots and overall service via a questionnaire and an interview. The results demonstrate social robots' potential for engaging in heartwarming interactions that enhance overall customer satisfaction through the use of the 'Continuous Hospitality with Social Robots' collaboration design. An exploratory analysis suggests that the perceived impressions of the interaction with social robots are influenced by customer gender and the duration of interactions. Furthermore, the results suggest that social robots could be utilized in other roles at hotels, namely effective advertisement through heartwarming interaction and mental support for employees who do not interact with customers.

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Section: Background and Issuementioning

confidence: 99%

Section: Social Robots In a Hotelmentioning

confidence: 99%

Section: Collaborative Service Design For Social Robots In a Hotelmentioning

confidence: 99%

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Continuous Hospitality with Social Robots at a hotel

et al. 2020

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“…One of the most explored applications in human-populated environments is using mobile robots to provide information services. The reason this application is so popular is that mobile robots in contrast to fixed information terminals, can move around their environments to provide information and actively approach potential users [1], [6] where needed.…”

Section: Scientific Contextmentioning

confidence: 99%

Getting to Know Your Robot Customers: Automated Analysis of User Identity and Demographics for Robots in the Wild

Herrero¹,

Fentanes

Hanheide

2018

IEEE Robot. Autom. Lett.

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Long-term studies with autonomous robots "in the wild" (deployed in real-world human-inhabited environments) are among the most laborious and resource-intensive endeavours in Human-Robot Interaction. Even if a robot system itself is robust and well-working, the analysis of the vast amounts of user data one aims to collect and analyse poses a significant challenge. This paper proposes an automated processing pipeline, using state-of-the-art computer-vision technology to estimate demographic factors from users' faces and re-identify them to establish usage patterns. It overcomes the problem of explicitly recruiting participants and having them fill questionnaires about their demographic background, and allows to study completely unsolicited and non-primed interactions over long periods of time. The paper offers a comprehensive assessment of the performance of the automated analysis with data from 68 days of continuous deployment of a robot in a care home, and also presents a set of findings obtained through the analysis, underpinning the viability of the approach.

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“…Natural interaction with humans is enabled only by combining research on topics such as robot control, object recognition and dialog engines [1][2][3]. Recently, a hotel in Japan introduced an android to confirm the reservation information of clients and guide them to their rooms [4,5]. This system combines interactive technology that responds to clients, vision/recognition technology that can recognize an approaching person and memorize their appearance, voice recognition and dialog technology that can understand what people say and have a conversation and robot control technology that can inform people of their room location via hand gestures.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Android Facial Expressions Using Genetic Algorithms

et al. 2019

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Because the internal structure, degree of freedom, skin control position and range of the android face are different, it is very difficult to generate facial expressions by applying existing facial expression generation methods. In addition, facial expressions differ among robots because they are designed subjectively. To address these problems, we developed a system that can automatically generate robot facial expressions by combining an android, a recognizer capable of classifying facial expressions and a genetic algorithm. We have developed two types (older men and young women) of android face robots that can simulate human skin movements. We selected 16 control positions to generate the facial expressions of these robots. The expressions were generated by combining the displacements of 16 motors. A chromosome comprising 16 genes (motor displacements) was generated by applying real-coded genetic algorithms; subsequently, it was used to generate robot facial expressions. To determine the fitness of the generated facial expressions, expression intensity was evaluated through a facial expression recognizer. The proposed system was used to generate six facial expressions (angry, disgust, fear, happy, sad, surprised); the results confirmed that they were more appropriate than manually generated facial expressions.

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Long-term assessment of a service robot in a hotel environment

Cited by 134 publications

References 18 publications

Continuous Hospitality with Social Robots at a hotel

Continuous Hospitality with Social Robots at a hotel

Getting to Know Your Robot Customers: Automated Analysis of User Identity and Demographics for Robots in the Wild

Optimizing Android Facial Expressions Using Genetic Algorithms

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