  Imitation learning has shown great potential for enabling robots to acquire
complex manipulation behaviors. However, these algorithms suffer from high
sample complexity in long-horizon tasks, where compounding errors accumulate
over the task horizons. We present PRIME (PRimitive-based IMitation with data
Efficiency), a behavior primitive-based framework designed for improving the
data efficiency of imitation learning. PRIME scaffolds robot tasks by
decomposing task demonstrations into primitive sequences, followed by learning
a high-level control policy to sequence primitives through imitation learning.
Our experiments demonstrate that PRIME achieves a significant performance
improvement in multi-stage manipulation tasks, with 10-34% higher success rates
in simulation over state-of-the-art baselines and 20-48% on physical hardware.
