  Expert demonstrations are a rich source of supervision for training visual
robotic manipulation policies, but imitation learning methods often require
either a large number of demonstrations or expensive online expert supervision
to learn reactive closed-loop behaviors. In this work, we introduce SPARTN
(Synthetic Perturbations for Augmenting Robot Trajectories via NeRF): a
fully-offline data augmentation scheme for improving robot policies that use
eye-in-hand cameras. Our approach leverages neural radiance fields (NeRFs) to
synthetically inject corrective noise into visual demonstrations, using NeRFs
to generate perturbed viewpoints while simultaneously calculating the
corrective actions. This requires no additional expert supervision or
environment interaction, and distills the geometric information in NeRFs into a
real-time reactive RGB-only policy. In a simulated 6-DoF visual grasping
benchmark, SPARTN improves success rates by 2.8$\times$ over imitation learning
without the corrective augmentations and even outperforms some methods that use
online supervision. It additionally closes the gap between RGB-only and RGB-D
success rates, eliminating the previous need for depth sensors. In real-world
6-DoF robotic grasping experiments from limited human demonstrations, our
method improves absolute success rates by $22.5\%$ on average, including
objects that are traditionally challenging for depth-based methods. See video
results at \url{https://bland.website/spartn}.
