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.

翻译：模仿学习在使机器人获取复杂操控行为方面展现出巨大潜力。然而，这些算法在长时域任务中存在样本效率低下的问题，其复合误差会随任务时域累积。我们提出PRIME（基于基元的数据高效模仿学习框架），这是一种基于行为基元的框架，旨在提升模仿学习的数据效率。PRIME通过将任务演示分解为基元序列来构建机器人任务的支架，随后通过模仿学习训练高层控制策略以编排基元序列。实验表明，PRIME在多阶段操控任务中实现了显著的性能提升：在仿真环境中，相对于最先进的基线方法，成功率提升10-34%；在物理硬件上，成功率提升20-48%。