JUICER: A Data-Efficient Imitation Learning Pipeline for Precise Long-Horizon Robotic Assembly
A pipeline combining expressive policy architectures, synthetic data augmentation, and iterative model improvement enables learning high-performance policies for precise long-horizon robotic assembly from a small number of human demonstrations.
