Learning to Navigate in Mazes with Novel Layouts Using Abstract Top-down Maps

This work learns to navigate end-to-end with map input, aiming to generalize to novel map layouts in zero-shot.

Integrating Symmetry into Differentiable Planning with Steerable Convolutions

We formulate how differentiable planning algorithms can exploit inherent symmetry in path planning problems, named SymPlan, and propose practical algorithms.

Model-based Navigation in Environments with Novel Layouts Using Abstract 2-D Maps

This work aims to improve the zero-shot generalization performance in map-based navigation on novel layouts.