Personalized Human-Robot Interaction with a Robot Bartender

Abstract

The ability to personalize behaviors is essential for a robot to develop and maintain a long-lasting bond with a user in human-oriented applications, such as a service domain. Service robots must be capable of deducing what actions would be most desirable and best serve the needs and requirements of any interacting users. However, the personalization of a service robot in real-world human-robot interaction (HRI) requires the development of sophisticated mechanisms for identifying differences within the focused group of users, creating a relative user model representation, and finally, devising the varieties of the robot's behaviors. In this work, we briefly present the multiple methodologies developed for an autonomous bartender robot to personalize its behaviors upon the customers' moods, attention behaviors, purchasing preferences, personal preferences for interaction, and previous interaction strategies. We expect that the robot would need to serve and interact with multiple customers at the time, as it usually happens in human bartending scenarios. For this reason, our robot has been endowed with the ability to engage multiple users by alternating its attention between them, and personalizing enjoyable interactions through small talk (e.g., welcoming and conversing about topics of general interest related to recent news).