  Traditional deep learning-based visual imitation learning techniques require
a large amount of demonstration data for model training, and the pre-trained
models are difficult to adapt to new scenarios. To address these limitations,
we propose a unified framework using a novel progressive learning approach
comprised of three phases: i) a coarse learning phase for concept
representation, ii) a fine learning phase for action generation, and iii) an
imaginary learning phase for domain adaptation. Overall, this approach leads to
a one-shot domain-adaptive imitation learning framework. We use robotic pouring
task as an example to evaluate its effectiveness. Our results show that the
method has several advantages over contemporary end-to-end imitation learning
approaches, including an improved success rate for task execution and more
efficient training for deep imitation learning. In addition, the
generalizability to new domains is improved, as demonstrated here with novel
background, target container and granule combinations. We believe that the
proposed method can be broadly applicable to different industrial or domestic
applications that involve deep imitation learning for robotic manipulation,
where the target scenarios have high diversity while the human demonstration
data is limited.
