A ROS Architecture for Personalised HRI with a Bartender Social Robot

TL;DR

This paper presents a three-layer ROS architecture for a social robot bartender that enables personalized, natural interactions to enhance user engagement and loyalty in service robotics.

Contribution

The paper introduces a novel three-layer ROS architecture integrating perception, decision-making, and execution layers with user modeling for personalized social robot interactions.

Findings

Effective perception of social signals achieved

Multi-party interaction handling demonstrated

Personalization improves user engagement

Abstract

BRILLO (Bartending Robot for Interactive Long-Lasting Operations) project has the overall goal of creating an autonomous robotic bartender that can interact with customers while accomplishing its bartending tasks. In such a scenario, people's novelty effect connected to the use of an attractive technology is destined to wear off and, consequently, it negatively affects the success of the service robotics application. For this reason, providing personalised natural interaction while accessing its services is of paramount importance for increasing users' engagement and, consequently, their loyalty. In this paper, we present the developed three-layers ROS architecture integrating a perception layer managing the processing of different social signals, a decision-making layer for handling multi-party interactions, and an execution layer controlling the behaviour of a complex robot composed of arms and a face. Finally, user modelling through a beliefs layer allows for personalised interaction.