Demonstration of a Robot Receptionist with Multi-party Situated Interaction

Moujahid, Meriam; Wilson, B.; Hastie, Helen; Lemon, Oliver

doi:10.1109/hri53351.2022.9889314

Cited by 4 publications

(2 citation statements)

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“…The system seamlessly combines open and closed-domain dialogues with emotive facial expressions, resulting in an engaging and personalised interaction for users. The system was initially designed and developed to serve as a recep- tionist for the National Robotarium, in continuation of the multi-party interactive model developed by Moujahid et al (2022b), and its deployment shows promise in other areas due to the LLMs versatile capabilities. As a result, the system is not limited to the designated receptionist role, but can also engage in open-domain conversations, thereby enhancing its potential as a multifunctional conversational agent.…”

Section: Introductionmentioning

confidence: 99%

FurChat: An Embodied Conversational Agent using LLMs, Combining Open and Closed-Domain Dialogue with Facial Expressions

Cherakara,

Varghese,

Shabana

et al. 2023

Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue

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We demonstrate an embodied conversational agent that can function as a receptionist and generate a mixture of open and closed-domain dialogue along with facial expressions, by using a large language model (LLM) to develop an engaging conversation. We deployed the system onto a Furhat robot, which is highly expressive and capable of using both verbal and nonverbal cues during interaction. The system was designed specifically for the National Robotarium to interact with visitors through natural conversations, providing them with information about the facilities, research, news, upcoming events, etc. The system utilises the state-of-the-art GPT-3.5 model to generate such information along with domain-general conversations and facial expressions based on prompt engineering.

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Section: Introductionmentioning

confidence: 99%

FurChat: An Embodied Conversational Agent using LLMs, Combining Open and Closed-Domain Dialogue with Facial Expressions

Cherakara,

Varghese,

Shabana

et al. 2023

Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue

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“…Social robots are entering our public and social spaces, where they need to be human-aware and follow social etiquette. Social robots can be valuable in the service industry, such as receptionists [1], baristas [2], and bartenders [3]. If we observe how people interact with each other in physically situated face-to-face settings, the interaction goes well beyond the spoken words.…”

Section: Introductionmentioning

confidence: 99%

Multi-party Interaction with a Robot Receptionist

Moujahid

Hastie

Lemon

2022

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

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Social Robots in human environments need to be able to reason about their physical surroundings while interacting with people. Furthermore, human proxemics behaviours around robots can indicate how people perceive the robots and can inform robot personality and interaction design. Here, we introduce Charlie, a situated robot receptionist that can interact with people using verbal and non-verbal communication in a dynamic environment, where users might enter or leave the scene at any time. The robot receptionist is stationary and cannot navigate. Therefore, people have full control over their personal space as they are the ones approaching the robot. We investigated the influence of different apparent robot personalities on the proxemics behaviours of the humans. The results indicate that different types of robot personalities, specifically introversion and extroversion, can influence human proxemics behaviours. Participants maintained shorter distances with the introvert robot receptionist, compared to the extrovert robot. Interestingly, we observed that human-robot proxemics were not the same as typical human-human interpersonal distances, as defined in the literature. We therefore propose new proxemics zones for human-robot interaction.

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