Abstract

Recognizing the intention of others is important in all social interactions, especially in the service domain. Enabling a bartending robot to serve customers is particularly challenging as the system has to recognize the social signals produced by customers and respond appropriately. Detecting whether a customer would like to order is essential for the service encounter to succeed. This detection is particularly challenging in a noisy environment with multiple customers. Thus, a bartending robot has to be able to distinguish between customers intending to order, chatting with friends or just passing by. In order to study which signals customers use to initiate a service interaction in a bar, we recorded real-life customer-staff interactions in several German bars. These recordings were used to generate initial hypotheses about the signals customers produce when bidding for the attention of bar staff. Two experiments using snapshots and short video sequences then tested the validity of these hypothesized candidate signals. The results revealed that bar staff responded to a set of two non-verbal signals: first, customers position themselves directly at the bar counter and, secondly, they look at a member of staff. Both signals were necessary and, when occurring together, sufficient. The participants also showed a strong agreement about when these cues occurred in the videos. Finally, a signal detection analysis revealed that ignoring a potential order is deemed worse than erroneously inviting customers to order. We conclude that (a) these two easily recognizable actions are sufficient for recognizing the intention of customers to initiate a service interaction, but other actions such as gestures and speech were not necessary, and (b) the use of reaction time experiments using natural materials is feasible and provides ecologically valid results.

Highlights

  • For enabling users to interact intuitively with a robotic agent, the robot system has to be able to identify and to respond to social signals appropriately

  • Detecting whether a customer would like to order is essential for the service encounter to succeed. This detection is challenging in a noisy environment with multiple customers

  • The results revealed that bar staff responded to a set of two non-verbal signals: first, customers position themselves directly at the bar counter and, secondly, they look at a member of staff

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Summary

Introduction

For enabling users to interact intuitively with a robotic agent, the robot system has to be able to identify and to respond to social signals appropriately. One of the most difficult challenges is to distinguish between customers who are intending to place an order and those who are not. This is complicated by the fact that bars are often dimly-lit and noisy environments with multiple customers. Detecting customers who wish to order is crucial because failing to do so is fatal for the interaction as a whole. The system should detect the right signals, and avoid false alarms These signals could be very subtle, e.g., if a customer sits at the bar and decides to order another drink, s/he might not get up or move to another location. Our aim was to identify the signals that humans typically produce when they order in a bar from a natural data collection and to validate these signals in experiments

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