Training a policy that can generalize to unknown objects is a long standing challenge within the field of robotics. The performance of a policy often drops significantly in situations where an object in the scene was not seen during training. To solve this problem, we present NeRF-Aug, a novel method that is capable of teaching a policy to interact with objects that are not present in the dataset. This approach differs from existing approaches by leveraging the speed, photorealism, and 3D consistency of a neural radiance field for augmentation. NeRF-Aug both creates more photorealistic data and runs 63% faster than existing methods. We demonstrate the effectiveness of our method on 5 tasks with 9 novel objects that are not present in the expert demonstrations. We achieve an average performance boost of 55.6% when comparing our method to the next best method. You can see video results at https://nerf-aug.github.io.
@article{arxiv.2411.02482,
title = {NeRF-Aug: Data Augmentation for Robotics with Neural Radiance Fields},
author = {Eric Zhu and Mara Levy and Matthew Gwilliam and Abhinav Shrivastava},
journal= {arXiv preprint arXiv:2411.02482},
year = {2025}
}