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.
@article{arxiv.2403.00929,
title = {PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning},
author = {Tian Gao and Soroush Nasiriany and Huihan Liu and Quantao Yang and Yuke Zhu},
journal= {arXiv preprint arXiv:2403.00929},
year = {2024}
}