  To perform tasks well in a new domain, one must first know something about
it. This paper reports on a robot controller for navigation through unfamiliar
indoor worlds. Based on spatial affordances, it integrates planning with
reactive heuristics. Before it addresses specific targets, however, the system
deliberately explores for high-level connectivity and captures that data in a
cognitive spatial model. Despite limited exploration time, planning in the
resultant model is faster and better supports successful travel in a
challenging, realistic space.
