This paper introduces a method to enhance Interactive Imitation Learning (IIL) by extracting touch interaction points and tracking object movement from video demonstrations. The approach extends current IIL systems by providing robots with detailed knowledge of both where and how to interact with objects, particularly complex articulated ones like doors and drawers. By leveraging cutting-edge techniques such as 3D Gaussian Splatting and FoundationPose for tracking, this method allows robots to better understand and manipulate objects in dynamic environments. The research lays the foundation for more effective task learning and execution in autonomous robotic systems.

翻译：本文提出一种通过从视频演示中提取触觉交互点并追踪物体运动来增强交互式模仿学习的方法。该方法扩展了现有交互式模仿学习系统，为机器人提供关于与物体（特别是门、抽屉等复杂铰接物体）交互位置与方式的详细信息。通过利用3D高斯泼溅和FoundationPose等前沿追踪技术，该方法使机器人能更有效地理解并操控动态环境中的物体。本研究为自主机器人系统中更高效的任务学习与执行奠定了理论基础。