通过3D高斯点云追踪物体与接触点，提升机器人模仿学习精度。
Object and Contact Point Tracking in Demonstrations Using 3D Gaussian Splatting
- 利用3D高斯点云和FoundationPose追踪视频中物体与接触点
- 实现对门、抽屉等复杂可动物体的交互位置精准识别
- 适合需要精细操作的机器人任务学习场景
本文提出一种增强交互式模仿学习（IIL）的方法，通过视频演示提取物体接触交互点并追踪其运动。该方法结合3D高斯点云与FoundationPose技术，使机器人能够掌握复杂可动物体（如门、抽屉）的交互位置与运动轨迹。相比现有系统，本方法为机器人提供了更详细的交互知识，显著提升其在动态环境中的任务理解与执行能力，为自主机器人更高效的任务学习奠定基础。
原文摘要 · Abstract (English)
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.
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