RoboSplat is framework that leverages 3D Gaussian Splatting (3DGS) to generate novel demonstrations for RGB-based policy learning.

Starting from a single expert demonstration and multi-view images, our method generates diverse and visually realistic data for policy learning, enabling robust performance across six types of generalization (object poses, object types, camera views, scene appearance, lighting conditions, and embodiments) in the real world.

Compared to previous 2D data augmentation methods, our approach achieves significantly better results across various generalization types. Notably, we achieve this within a unified framework.