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arXiv · 2403.00929

PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning

Tian Gao·Soroush Nasiriany·Huihan Liu·Quantao Yang·Yuke Zhu
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Abstract

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.

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Tian Gao, Soroush Nasiriany, Huihan Liu, Quantao Yang, Yuke Zhu. 2024-03-01. PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning. https://arxiv.org/abs/2403.00929

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