Offline Imitation Learning (IL) with imperfect demonstrations has garnered increasing attention owing to the scarcity of expert data in many real-world domains. A fundamental problem in this scenario is how to extract positive behaviors from noisy data. In general, current approaches to the problem select data building on state-action similarity to given expert demonstrations, neglecting precious information in (potentially abundant) $\textit{diverse}$ state-actions that deviate from expert ones. In this paper, we introduce a simple yet effective data selection method that identifies positive behaviors based on their resultant states -- a more informative criterion enabling explicit utilization of dynamics information and effective extraction of both expert and beneficial diverse behaviors. Further, we devise a lightweight behavior cloning algorithm capable of leveraging the expert and selected data correctly. In the experiments, we evaluate our method on a suite of complex and high-dimensional offline IL benchmarks, including continuous-control and vision-based tasks. The results demonstrate that our method achieves state-of-the-art performance, outperforming existing methods on $\textbf{20/21}$ benchmarks, typically by $\textbf{2-5x}$, while maintaining a comparable runtime to Behavior Cloning ($\texttt{BC}$).

翻译：离线模仿学习（IL）在存在不完美演示数据的情况下日益受到关注，这归因于许多现实领域中专家数据的稀缺性。该场景下的一个核心问题是如何从噪声数据中提取正向行为。通常，当前解决该问题的方法基于状态-动作与给定专家演示的相似性来选择数据，忽略了偏离专家演示的（可能大量存在的）$\textit{多样化}$状态-动作中所蕴含的宝贵信息。本文中，我们提出了一种简单而有效的数据选择方法，该方法根据行为所导致的结果状态来识别正向行为——这是一个更具信息量的标准，能够显式利用动态信息，并有效提取专家行为以及有益的多样化行为。此外，我们设计了一种轻量级的行为克隆算法，能够正确地利用专家数据和所选数据。在实验中，我们在包括连续控制和基于视觉任务在内的一系列复杂高维离线IL基准测试上评估了我们的方法。结果表明，我们的方法实现了最先进的性能，在$\textbf{20/21}$个基准测试上超越了现有方法，通常领先$\textbf{2-5倍}$，同时保持了与行为克隆（$\texttt{BC}$）相当的运行时间。