Graph Attention for Spatial Prediction

Abstract

Imbuing robots with human-levels of intelligence is a longstanding goal of AI research. A critical aspect of human-level intelligence is spatial reasoning. Spatial reasoning requires a robot to reason about relationships among objects in an environment to estimate the positions of unseen objects. In this work, we introduced a novel graph attention approach for predicting the locations of query objects in partially observable environments. We found that our approach achieved state of the art results on object location prediction tasks. Then, we evaluated our approach on never before seen objects, and we observed zero-shot generalization to estimate the positions of new object types.