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.

翻译：传统的深层学习的视觉模拟学习技术需要大量示范数据,用于示范培训,而经过培训的模型很难适应新的情景。为了解决这些局限性,我们提议一个统一框架,采用由三个阶段组成的新的渐进式学习方法,包括:一) 概念表述的粗略学习阶段;二) 行动生成的精细学习阶段;三) 领域适应的假想学习阶段。总体而言,这一方法导致一个一次性的域适应性模仿学习框架。我们用机器人做榜样来评价其有效性。我们的结果显示,该方法在当代端到端学习方法方面有一些优势,包括任务执行成功率的提高和深入模仿学习的更有效培训。此外,对新领域的一般适用性得到了改进,这里以新的背景、目标容器和颗粒组合展示了这一点。我们认为,拟议的方法可以广泛适用于不同工业或国内应用,这些应用涉及对机器人操纵的深度模仿学习,在这些应用中,目标情景具有高度的多样性,而人类演示数据则有限。