Developing a Questionnaire to Evaluate Customersxe2x80x99 Perception in the Smart City Robotic Challenge In this paper, we present an approach to develop a new type of questionnaire for evaluating customersxe2x80x99 perceptions in the upcoming Smart CIty RObotic Challenge (SciRoc). The approach consists of two steps. First, it relies on interviewing experts on HumanRobot Interaction (HRI) to understand which robotxe2x80x99s behaviours can potentially affect the usersxe2x80x99 perceptions during a HRI task. Then, it leverages a user survey to filter out those robotxe2x80x99s behaviours that are not significantly relevant from the end user perspective. We concretely enacted our approach over a specific scenario developed in the context of SciRoc, which instructs a robot to take an elevator of a shopping mall asking support to the customers of the mall. The results of the survey have allowed us to derive a final list of 17 behaviours to be captured in the questionnaire, which has been finally developed relying on a 5point Likertscale.

Situation Awareness for Proactive Robots in HRI Perception of the intention of humans prior to an interaction is a demanding skill during humanrobot interaction (HRI). This skill is even more sought after during robotinitiated HRI. Initiating an interaction in an inappropriate situation can be avoided when robots are equipped with the ability to decide when to interact and when not to. Many of the existing systems investigate only a few characteristics of humans which demonstrate inner state of mind and are based on complex monitoring mechanisms which limit their use in most of the scenarios. This work presents an autoregressive model based on observable physical and emotional human cues to determine the level of interest displayed by a human towards an interaction with a robot. This model was implemented on a service robotic platform and the behavior of the robot was controlled using the model. The behavior of the robot was determined by means of proxemic approach and the nature of conversation with the human. The outcomes of the model were evaluated by analyzing user feedback in different situations inside a simulated social environment. Using the model, robot was given the ability to analyze the situation of its human user in an emotionally intelligent manner, prior to an interaction. The behavior of the model was reviewed by user feedback in order to validate the findings. Results of the experiment are presented and findings of the study are discussed.