Mobile robots remain idle during significant amounts of time in many applications, while new tasks are not assigned. In this paper, we propose a framework to use those periods of inactivity to observe the surrounding environment and learn information that can be used later on during navigation. Events like someone entering or leaving a room, or someone approaching a printer to pick a document up, convey important information about the observed space and the role played by the objects therein. We explore the information implicitly present in the motion patterns people describe in a certain workspace, to allow the robot to infer a "meaningful" spatial description. Map building is thus bottom-up driven by the observation of human activity, and not simply a top-down oriented geometric construction.
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