  Generalising vision-based manipulation policies to novel environments remains
a challenging area with limited exploration. Current practices involve
collecting data in one location, training imitation learning or reinforcement
learning policies with this data, and deploying the policy in the same
location. However, this approach lacks scalability as it necessitates data
collection in multiple locations for each task. This paper proposes a novel
approach where data is collected in a location predominantly featuring green
screens. We introduce Green-screen Augmentation (GreenAug), employing a chroma
key algorithm to overlay background textures onto a green screen. Through
extensive real-world empirical studies with over 850 training demonstrations
and 8.2k evaluation episodes, we demonstrate that GreenAug surpasses no
augmentation, standard computer vision augmentation, and prior generative
augmentation methods in performance. While no algorithmic novelties are
claimed, our paper advocates for a fundamental shift in data collection
practices. We propose that real-world demonstrations in future research should
utilise green screens, followed by the application of GreenAug. We believe
GreenAug unlocks policy generalisation to visually distinct novel locations,
addressing the current scene generalisation limitations in robot learning.
