Novel Yet Clean: Towards Theory-Guided Data Augmentation for Robot Imitation Learning

Data augmentation is a standard remedy for the demonstration-data bottleneck in robot imitation learning, yet existing methods are largely designed empirically and lack a unified theory of when and why augmentation helps. We develop a behavior-cloning analysis that jointly models real and augmented trajectories and reveals a fundamental principle for effective augmentation: augmented data should provide sufficiently new information while introducing as little augmentation error as possible—*Novel Yet Clean*. Guided by this principle, we propose **NYC**, a trajectory-wise, geometry-consistent, diffusion-based augmentation system that increases trajectory coverage through geometry-aware view synthesis while reducing augmentation bias through trajectory-wise consistency and diffusion-based visual repair. Extensive experiments on Push-T and five bimanual RLBench tasks validate both the theoretical predictions and the effectiveness of the resulting system. NYC improves task success rates by +29% +180% over a real-only baseline and achieves state-of-the-art performance on four of the five bimanual tasks, demonstrating that our theoretical principles can effectively translate into practical system design and downstream performance gains.