Human-aware navigation and Social Navigation are growing fields of robotics, attempting to tackle challenging navigation problems in human environments. One difficult aspect is understanding how a mobile robot's navigation behavior impacts the way they are perceived by humans, and how they interact together. In our previous work, we proposed an adaptable navigation architecture. In this paper, we describe how we determine which motion variables should be controlled by our architecture. We take a bottom-up approach by determining which primitive notions of locomotion and appearance have the most impact on people's perceptions of a mobile robot. We present the results of two online and one in-person experiment showing that the manner in which a mobile robot navigates has a significant impact on people's attribution of attitudes and physical properties to the robot.

Social Navigation methods attempt to integrate knowledge from Human Sciences fields such as the notion of Proxemics into mobile robot navigation. They are often evaluated in simulations, or lab conditions with informed participants, and studies of the impact of the robot behavior on humans are rare. Humans communicate and interact through many vectors, among which are motion and positioning, which can be related to social hierarchy and the socio-physical context. If a robot is to be deployed among humans, the methods it uses should be designed with this in mind. This work acts as the first step in an ongoing project in which we explore how to design navigation methods for mobile robots destined to be deployed among humans. We aim to consider navigation as more than just a functionality of the robot, and to study the impact of robot motion on humans. In this paper, we focus on the person-following task. We selected a state of the art person-following method as the basis for our method, which we modified and extended in order for it to be more general and adaptable. We conducted pilot experiments using this method on a real mobile robot in ecological contexts. We used results from the experiments to study the Human-Robot Interaction as a whole by analysing both the person-following method and the human behavior. Our preliminary results show that the way in which the robot followed a person had an impact on the interaction that emerged between them.