Multi-party Interaction with a Robot Receptionist

Abstract

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.

We are all Individuals: The Role of Robot Personality and Human Traits in Trustworthy Interaction

“…As robots become more capable of meaningful interaction, we will start to see them in settings that require social etiquette, such as in the service industry, including as baristas [1], receptionists [2] and bartenders [3]. As these are everyday settings, they provide convenient platforms for experiments that do not require subjects to have specialist training, such as in the first responder or healthcare domains.…”

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

“…The proposed system uses a one-to-one interaction method of communication with the visitors. We plan on extending the system to handle multi-party interaction (Moujahid et al, 2022a;Addlesee et al, 2023;Lemon, 2022;Gunson et al, 2022), which is an active research topic in developing receptionist robots. It is also crucial to address the issue of hallucination from the large language model and this problem can be mitigated by fine-tuning the language model and directly generating conversations from it without relying on any NLU components which we plan to implement in the future.…”

Between Reality and Delusion: Challenges of Applying Large Language Models to Companion Robots for Open-Domain Dialogues with Older Adults

“…However, overuse of them might also lead to the robot being perceived as repetitive. In addition, to decrease the superficialness of turns (sequential nature), backchannelling can be added using both verbal (e.g., "mm hm", "uh huh", "yeah") and non-verbal cues (e.g., nods, head shakes, facial expressions) [85], but it is important to do this in context with the dialogue, otherwise, it may also further deteriorate the user experience. Random gaze aversion should not be made during robot speech or while listening, as noted by two participants, because keeping eye contact with the user helps maintain the user engagement, perceived sociability, and improve quality of interaction [86].…”