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PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning
March 5, 2024, 2:43 p.m. | Tian Gao, Soroush Nasiriany, Huihan Liu, Quantao Yang, Yuke Zhu
cs.LG updates on arXiv.org arxiv.org
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 …
abstract algorithms arxiv behavior complexity cs.ai cs.lg cs.ro data efficiency enabling errors horizon imitation learning manipulation prime robots sample tasks type
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