We study E(2) Euclidean equivariance in navigation on geometric graphs and develop message passing network to solve it.
We study whether Euclidean symmetry can help in reinforcement learning and planning, which models the geometric transformations between reference frames of robots.
We train an equivariant network for pose prediction from single 2D image by using induced and restricted representations.
This work learns to navigate end-to-end with map input, aiming to generalize to novel map layouts in zero-shot.
This work aims to improve the zero-shot generalization performance in map-based navigation on novel layouts.
This work proposes a reinforcement learning framework for compositional object-oriented environments.