Deep Spatial Affordance Hierarchy: Spatial Knowledge Representation for Planning in Large-scale Environments

Domain-specific state representations are a fundamental component that enables planning of robot actions in unstructured human environments. In case of mobile robots, it is the spatial knowledge that constitutes the core of the state, and directly affects the performance of the planning algorithm. Here, we propose Deep Spatial Affordance Hierarchy (DASH), a probabilistic representation of spatial knowledge, spanning multiple levels of abstraction from geometry and appearance to semantics, and leveraging a deep model of generic spatial concepts. DASH is designed to represent space from the perspective of a mobile robot executing complex behaviors in the environment, and directly encodes gaps in knowledge and spatial affordances. In this paper, we explain the principles behind DASH, and present its initial realization for a robot equipped with laser-range sensor. We demonstrate the ability of our implementation to successfully build representations of large-scale environments, and leverage the deep model of generic spatial concepts to infer latent and missing information at all abstraction levels.