基于未分段演示的长时域视觉运动任务单次鲁棒模仿学习
One-Shot Robust Imitation Learning for Long-Horizon Visuomotor Tasks from Unsegmented Demonstrations
AI总结：
针对长时域视觉运动模仿依赖语义分段演示且易受扰动、遮挡影响的问题，提出MiLa框架，结合动态运动基元与元学习，实现未分段演示学习、单次演示泛化及鲁棒执行。
摘要：
与单一技能任务不同，长时域任务在我们的日常生活中发挥着关键作用，例如倾倒任务需要恰当地串联伸手、抓取和倾倒等子任务。作为将人类技能迁移到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，在学习长时域视觉运动技能时，模仿学习通常需要大量经过语义分段的演示。此外，模仿学习的性能可能容易受到外部扰动和视觉遮挡的影响。本文利用动态运动基元和元学习，提出了一种新的模仿学习框架，称为基于自适应动态基元的元模仿学习（Meta-Imitation Learning with Adaptive Dynamical Primitives，MiLa）。MiLa能够学习未分段的长时域演示，并仅凭单次演示适应未见过的任务。MiLa还能在任务执行过程中抵抗外部干扰和视觉遮挡。真实世界机器人实验证明了MiLa的优越性，无论机器人上是否存在视觉遮挡和随机扰动。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
