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.

An Exploratory Study on Proxemics Preferences of Humans in Accordance with Attributes of Service Robots Service robots that possess social interactive capabilities are vital to cater to the demand in emerging domains of robotic applications. A service robot frequently needs to interact with users when performing service tasks. The comfortability of users depends on the humanrobot proxemics during these interactions. Hence, a service robot should be capable of maintaining proper proxemics that improves the comfort of users. The proxemics preferences of users might depend on diverse attributes of a robot, such as emotional state, noise level, and physical appearance. Therefore, it is vital to gain a better understanding of a robotxe2x80x99s attributes which influence humanrobot proxemics behavior. This paper contributes to an exploratory study to analyze the effects on humanrobot proxemics preferences due to a robotxe2x80x99s attributes; facial and vocal emotions, level of internal noises, and the physical appearance. Four substudies have been conducted to gather the required humanrobot proxemics data. The gathered data have been analyzed through statistical tests. The test statistics reveal that facial and vocal emotions, internal noise level, and the physical appearance of a robot have significant effects on proxemics preferences of humans. The outcomes of this exploratory study would be useful in designing and developing humanrobot proxemics strategies of a service robot that would enhance social interaction.