  We propose a new probabilistic framework that allows mobile robots to
autonomously learn deep, generative models of their environments that span
multiple levels of abstraction. Unlike traditional approaches that combine
engineered models for low-level features, geometry, and semantics, our approach
leverages recent advances in Sum-Product Networks (SPNs) and deep learning to
learn a single, universal model of the robot's spatial environment. Our model
is fully probabilistic and generative, and represents a joint distribution over
spatial information ranging from low-level geometry to semantic
interpretations. Once learned, it is capable of solving a wide range of tasks:
from semantic classification of places, uncertainty estimation, and novelty
detection, to generation of place appearances based on semantic information and
prediction of missing data in partial observations. Experiments on laser-range
data from a mobile robot show that the proposed universal model obtains
performance superior to state-of-the-art models fine-tuned to one specific
task, such as Generative Adversarial Networks (GANs) or SVMs.
