Object and Contact Point Tracking in Demonstrations Using 3D Gaussian Splatting
This work addresses the challenge of detailed interaction knowledge for autonomous robotic systems, representing an incremental improvement over existing IIL methods.
This paper tackled the problem of enhancing Interactive Imitation Learning by extracting touch points and tracking object movement from videos, enabling robots to better manipulate complex articulated objects like doors and drawers.
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.