  The robustness of visual navigation policies trained through imitation often
hinges on the augmentation of the training image-action pairs. Traditionally,
this has been done by collecting data from multiple cameras, by using standard
data augmentations from computer vision, such as adding random noise to each
image, or by synthesizing training images. In this paper we show that there is
another practical alternative for data augmentation for visual navigation based
on extrapolating viewpoint embeddings and actions nearby the ones observed in
the training data. Our method makes use of the geometry of the visual
navigation problem in 2D and 3D and relies on policies that are functions of
equivariant embeddings, as opposed to images. Given an image-action pair from a
training navigation dataset, our neural network model predicts the latent
representations of images at nearby viewpoints, using the equivariance
property, and augments the dataset. We then train a policy on the augmented
dataset. Our simulation results indicate that policies trained in this way
exhibit reduced cross-track error, and require fewer interventions compared to
policies trained using standard augmentation methods. We also show similar
results in autonomous visual navigation by a real ground robot along a path of
over 500m.
