In this paper, we consider domain-adaptive imitation learning with visual observation, where an agent in a target domain learns to perform a task by observing expert demonstrations in a source domain. Domain adaptive imitation learning arises in practical scenarios where a robot, receiving visual sensory data, needs to mimic movements by visually observing other robots from different angles or observing robots of different shapes. To overcome the domain shift in cross-domain imitation learning with visual observation, we propose a novel framework for extracting domain-independent behavioral features from input observations that can be used to train the learner, based on dual feature extraction and image reconstruction. Empirical results demonstrate that our approach outperforms previous algorithms for imitation learning from visual observation with domain shift.

翻译：本文研究了基于视觉观测的域自适应模仿学习问题，其中目标域中的智能体通过观察源域中的专家演示来学习执行任务。域自适应模仿学习出现在实际场景中：当机器人接收视觉传感数据时，需要通过视觉观察不同角度的其他机器人或不同形态的机器人来模仿其动作。为克服跨域模仿学习中视觉观测存在的域偏移问题，我们提出了一种基于双重特征提取与图像重建的新型框架，用于从输入观测中提取域无关的行为特征，从而训练学习器。实验结果表明，我们的方法在存在域偏移的视觉观测模仿学习任务中优于现有算法。