Imitating skills from low-quality datasets, such as sub-optimal
demonstrations and observations with distractors, is common in real-world
applications. In this work, we focus on the problem of Learning from Noisy
Demonstrations (LND), where the imitator is required to learn from data with
noise that often occurs during the processes of data collection or
transmission. Previous IL methods improve the robustness of learned policies by
injecting an adversarially learned Gaussian noise into pure expert data or
utilizing additional ranking information, but they may fail in the LND setting.
To alleviate the above problems, we propose Denoised Imitation learning based
on Domain Adaptation (DIDA), which designs two discriminators to distinguish
the noise level and expertise level of data, facilitating a feature encoder to
learn task-related but domain-agnostic representations. Experiment results on
MuJoCo demonstrate that DIDA can successfully handle challenging imitation
tasks from demonstrations with various types of noise, outperforming most
baseline methods.

通过领域适应的去噪模仿学习，我们设计了两个鉴别器来区分数据的噪声水平和专业水平，从而使特征编码器能够学习与任务相关但领域无关的表示，实验证明我们的方法可以成功处理来自不同类型噪声的演示的有挑战性的模仿任务，优于大多数基线方法。