  While learning from demonstrations is powerful for acquiring visuomotor
policies, high-performance imitation without large demonstration datasets
remains challenging for tasks requiring precise, long-horizon manipulation.
This paper proposes a pipeline for improving imitation learning performance
with a small human demonstration budget. We apply our approach to assembly
tasks that require precisely grasping, reorienting, and inserting multiple
parts over long horizons and multiple task phases. Our pipeline combines
expressive policy architectures and various techniques for dataset expansion
and simulation-based data augmentation. These help expand dataset support and
supervise the model with locally corrective actions near bottleneck regions
requiring high precision. We demonstrate our pipeline on four furniture
assembly tasks in simulation, enabling a manipulator to assemble up to five
parts over nearly 2500 time steps directly from RGB images, outperforming
imitation and data augmentation baselines. Project website:
https://imitation-juicer.github.io/.
