We consider imitation learning with access only to expert demonstrations, whose real-world application is often limited by covariate shift due to compounding errors during execution. We investigate the effectiveness of the Continuity-based Corrective Labels for Imitation Learning (CCIL) framework in mitigating this issue for real-world fine manipulation tasks. CCIL generates corrective labels by learning a locally continuous dynamics model from demonstrations to guide the agent back toward expert states. Through extensive experiments on peg insertion and fine grasping, we provide the first empirical validation that CCIL can significantly improve imitation learning performance despite discontinuities present in contact-rich manipulation. We find that: (1) real-world manipulation exhibits sufficient local smoothness to apply CCIL, (2) generated corrective labels are most beneficial in low-data regimes, and (3) label filtering based on estimated dynamics model error enables performance gains. To effectively apply CCIL to robotic domains, we offer a practical instantiation of the framework and insights into design choices and hyperparameter selection. Our work demonstrates CCIL's practicality for alleviating compounding errors in imitation learning on physical robots.

翻译：我们考虑仅通过专家演示进行模仿学习，其在实际应用中的效果常因执行过程中的累积误差导致的协变量偏移而受限。本文研究了基于连续性的模仿学习校正标签（CCIL）框架在缓解真实世界精细操作任务中该问题的有效性。CCIL通过从演示数据中学习局部连续动力学模型来生成校正标签，从而引导智能体返回专家状态。通过在轴孔装配和精细抓取任务上的大量实验，我们首次通过实证验证了CCIL能够显著提升模仿学习性能，即使在存在丰富接触的不连续操作任务中亦然。研究发现：（1）真实世界操作任务展现出足够的局部平滑性以应用CCIL；（2）生成的校正标签在低数据量场景中效益最为显著；（3）基于动力学模型误差估计的标签筛选机制能够有效提升性能。为将CCIL有效应用于机器人领域，我们提出了该框架的实用实现方案，并深入探讨了设计选择与超参数设置的考量。本工作证明了CCIL在缓解物理机器人模仿学习中累积误差方面的实用价值。