Evaluating a social multi-user interaction model using a Nao robot

This paper presents results from a user evaluation of a robot bartender system, which supports social engagement and interaction with multiple customers. The system is a Nao-based alternative version of an existing robot bartender developed in the JAMES project [1]. The Nao-based version has given us a local experimentation platform, allowing us to focus on social multi-user interaction rather than the robot technology of object manipulation. We will describe the design of the Nao-based system and discuss the differences with the original JAMES system. In a recent evaluation of the JAMES system with real users, a trained and a hand-coded version of the action selection policy were compared [2]. Here we present results from a similar comparative user evaluation on the Nao-based system, which confirm the conclusions of the previous experiment and provide further evidence in favour of the trained action selection mechanism. Task success was found to be almost 20% higher with the trained policy, with interaction times being about 10% shorter. Participants also rated the trained system as significantly more natural, more understanding, and better at providing appropriate attention.