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 ($\texttt{BC}$).

离线仿真学习（IL）在实际领域中由于专家数据的稀缺性而受到越来越多的关注。本文介绍了一种简单而有效的数据选择方法，基于其结果状态识别积极行为，从而更好地利用动态信息并有效地提取专家行为和有益的多样行为。通过在复杂和高维离线 IL 基准测试中的实验评估，结果表明我们的方法达到了最先进的性能，在 20/21 个基准测试中超越了现有方法，通常是 2-5 倍，并且与行为克隆（BC）保持可比的运行时间。