CenterArt: Joint Shape Reconstruction and 6-DoF Grasp Estimation of Articulated Objects
This addresses a key challenge in general robotic manipulation for articulated objects, representing an incremental improvement over prior methods.
The paper tackles the problem of precisely grasping and reconstructing articulated objects for robotic manipulation by proposing CenterArt, a method for simultaneous 3D shape reconstruction and 6-DoF grasp estimation from RGB-D images, which outperforms existing methods in accuracy and robustness.
Precisely grasping and reconstructing articulated objects is key to enabling general robotic manipulation. In this paper, we propose CenterArt, a novel approach for simultaneous 3D shape reconstruction and 6-DoF grasp estimation of articulated objects. CenterArt takes RGB-D images of the scene as input and first predicts the shape and joint codes through an encoder. The decoder then leverages these codes to reconstruct 3D shapes and estimate 6-DoF grasp poses of the objects. We further develop a mechanism for generating a dataset of 6-DoF grasp ground truth poses for articulated objects. CenterArt is trained on realistic scenes containing multiple articulated objects with randomized designs, textures, lighting conditions, and realistic depths. We perform extensive experiments demonstrating that CenterArt outperforms existing methods in accuracy and robustness.