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得复杂操作行为方面展现出巨大潜力。然而，这些算法在长时域任务中面临高样本复杂度问题，因为复合误差会在任务时域内持续累积。我们提出PRIME（基于基元的高效模仿学习框架），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搭建机器人任务脚手架，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在多阶段操作任务中实现了显著性能提升：在仿真环境下，其成功率相比最先进基线方法提升10-34%；在实体硬件上提升20-48%。