Imitation can allow us to quickly gain an understanding of a new task.
Through a demonstration, we can gain direct knowledge about which actions need
to be performed and which goals they have. In this paper, we introduce a new
approach to imitation learning that tackles the challenges of a robot imitating
a human, such as the change in perspective and body schema. Our approach can
use a single human demonstration to abstract information about the demonstrated
task, and use that information to generalise and replicate it. We facilitate
this ability by a new integration of two state-of-the-art methods: a diffusion
action segmentation model to abstract temporal information from the
demonstration and an open vocabulary object detector for spatial information.
Furthermore, we refine the abstracted information and use symbolic reasoning to
create an action plan utilising inverse kinematics, to allow the robot to
imitate the demonstrated action.

通过模仿学习，我们能够快速理解一个新任务，通过演示，我们可以直接获得哪些动作需要执行以及它们的目标的知识。本文介绍了一种新的模仿学习方法，解决了机器人模仿人类所面临的视角变化和身体模式等挑战。我们的方法可以利用单个人类演示来抽象展示任务的信息，并利用该信息进行泛化和复制。我们通过两种最先进的方法进行了新的集成：扩散式动作分割模型用于抽象演示的时间信息，以及开放词汇对象检测器用于空间信息。此外，我们通过符号推理来改进抽象的信息，并利用逆向运动学创建行动计划，以使机器人能够模仿演示的动作。