DIDA: Denoised Imitation Learning based on Domain Adaptation
Kaichen Huang Hai-Hang Sun Shenghua Wan Minghao Shao Shuai Feng Le Gan De-Chuan Zhan
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.