In contrast to single-skill tasks, long-horizon tasks play a crucial role in our daily life, e.g., a pouring task requires a proper concatenation of reaching, grasping and pouring subtasks. As an efficient solution for transferring human skills to robots, imitation learning has achieved great progress over the last two decades. However, when learning long-horizon visuomotor skills, imitation learning often demands a large amount of semantically segmented demonstrations. Moreover, the performance of imitation learning could be susceptible to external perturbation and visual occlusion. In this paper, we exploit dynamical movement primitives and meta-learning to provide a new framework for imitation learning, called Meta-Imitation Learning with Adaptive Dynamical Primitives (MiLa). MiLa allows for learning unsegmented long-horizon demonstrations and adapting to unseen tasks with a single demonstration. MiLa can also resist external disturbances and visual occlusion during task execution. Real-world robotic experiments demonstrate the superiority of MiLa, irrespective of visual occlusion and random perturbations on robots.

翻译：与单一技能任务不同，长视距任务在我们的日常生活中扮演着关键角色，例如倒水任务需要正确串联接近、抓握和倾倒等子任务。作为将人类技能迁移至机器人的高效解决方案，模仿学习在过去二十年中取得了巨大进展。然而，在学习长视距视觉运动技能时，模仿学习通常需要大量语义分段的演示数据。此外，模仿学习的性能易受外部扰动和视觉遮挡的影响。本文利用动态运动基元与元学习，提出了一种新的模仿学习框架——基于自适应动态运动基元的元模仿学习（MiLa）。MiLa能够学习非分段的长视距演示数据，并仅通过单次演示适应未见任务。MiLa在执行任务时还能抵抗外部干扰和视觉遮挡。真实世界机器人实验表明，无论机器人是否遭遇视觉遮挡或随机扰动，MiLa均表现出优越性能。