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.
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.
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.