Abstract: Modern AI approaches have been on a scaling route: bigger models, datasets, and compute infrastructure have resulted in impressive performance in image and text processing. What do these mean for spatial perception, particularly in a robotics context? In this talk, I will (broadly) address two questions: (a) how do we leverage the advanced capabilities offered by large vision and language models for robot perception? (b) Is scaling enough? What other ingredients are we possibly missing? Much of the talk will revolve around (a), covering our recent work on building open-vocabulary 3D maps that may generally be employed across a broad class of robotics applications. Towards the end, we will also discuss (b), looking at promising avenues in the near- and long-term.