We are working within the framework of the Spatial Semantic Hierarchy (SSH) (Kuipers 2000), which is a lattice of related representations for large-scale space. The SSH is conducive to research on learning from uninterpreted experience because it separates the interface representations -- the representations for local control laws and local metrical models -- from the symbolic representations built on them. We use two robot platforms in this research, both with multiple, partially redundant, sensory modalities. Lassie is an RWI Magellan with laser range-finder, 16 sonar sensors, 16 IR range sensors, 16 bump sensors and odometry. Vulcan is a custom-built robotic wheelchair with two laser rangefinders, binocular cameras on pan-tilt heads, 7 sonars, 12 IR proximity sensors, and odometry.