Robotic manipulation of unfamiliar objects in new environments is challenging and requires extensive training or laborious pre-programming. We propose a new skill transfer framework, which enables a robot to transfer complex object manipulation skills and constraints from a single human demonstration. Our approach addresses the challenge of skill acquisition and task execution by deriving geometric representations from demonstrations focusing on object-centric interactions. By leveraging the Functional Maps (FM) framework, we efficiently map interaction functions between objects and their environments, allowing the robot to replicate task operations across objects of similar topologies or categories, even when they have significantly different shapes. Additionally, our method incorporates a Task-Space Imitation Algorithm (TSIA) which generates smooth, geometrically-aware robot paths to ensure the transferred skills adhere to the demonstrated task constraints. We validate the effectiveness and adaptability of our approach through extensive experiments, demonstrating successful skill transfer and task execution in diverse real-world environments without requiring additional training.

翻译：机器人在新环境中操纵陌生物体具有挑战性，通常需要大量训练或繁琐的预编程。我们提出一种新的技能迁移框架，使机器人能够通过单次人类演示迁移复杂的物体操作技能与约束。该方法通过从以物体为中心的交互演示中推导几何表示，应对技能获取与任务执行的挑战。借助功能映射（FM）框架，我们高效地映射物体与其环境之间的交互函数，使机器人能够在拓扑结构或类别相似但形状差异显著的物体间复现任务操作。此外，我们的方法结合了任务空间模仿算法（TSIA），该算法能生成平滑且几何感知的机器人路径，确保迁移技能遵循演示的任务约束。我们通过大量实验验证了该方法的有效性和适应性，在无需额外训练的情况下，成功实现了多样化真实环境中的技能迁移与任务执行。