Articulated Object Manipulation using Online Axis Estimation with SAM2-Based Tracking
This work addresses the challenge of precise robotic manipulation of articulated objects, which is incremental as it builds on existing interactive perception techniques.
The paper tackles the problem of articulated object manipulation by developing a closed-loop pipeline that integrates interactive perception with online axis estimation from segmented 3D point clouds, resulting in enhanced precision and efficiency, as demonstrated in simulated environments where it outperforms baseline approaches.
Articulated object manipulation requires precise object interaction, where the object's axis must be carefully considered. Previous research employed interactive perception for manipulating articulated objects, but typically, open-loop approaches often suffer from overlooking the interaction dynamics. To address this limitation, we present a closed-loop pipeline integrating interactive perception with online axis estimation from segmented 3D point clouds. Our method leverages any interactive perception technique as a foundation for interactive perception, inducing slight object movement to generate point cloud frames of the evolving dynamic scene. These point clouds are then segmented using Segment Anything Model 2 (SAM2), after which the moving part of the object is masked for accurate motion online axis estimation, guiding subsequent robotic actions. Our approach significantly enhances the precision and efficiency of manipulation tasks involving articulated objects. Experiments in simulated environments demonstrate that our method outperforms baseline approaches, especially in tasks that demand precise axis-based control. Project Page: https://hytidel.github.io/video-tracking-for-axis-estimation/.