PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning
<|reference_start|>PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning: 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.<|reference_end|>
@article{gao2024prime:,
    title={PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for
  Data-Efficient Imitation Learning},
    author={Tian Gao, Soroush Nasiriany, Huihan Liu, Quantao Yang, Yuke Zhu},
    journal={arXiv preprint arXiv:2403.00929},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00929},
    primaryClass={cs.RO cs.AI cs.LG}
}