We introduce AO-Grasp, a grasp proposal method that generates stable and actionable 6 degree-of-freedom grasps for articulated objects. Our generated grasps enable robots to interact with articulated objects, such as opening and closing cabinets and appliances. Given a segmented partial point cloud of a single articulated object, AO-Grasp predicts the best grasp points on the object with a novel Actionable Grasp Point Predictor model and then finds corresponding grasp orientations for each point by leveraging a state-of-the-art rigid object grasping method. We train AO-Grasp on our new AO-Grasp Dataset, which contains 48K actionable parallel-jaw grasps on synthetic articulated objects. In simulation, AO-Grasp achieves higher grasp success rates than existing rigid object grasping and articulated object interaction baselines on both train and test categories. Additionally, we evaluate AO-Grasp on 120 realworld scenes of objects with varied geometries, articulation axes, and joint states, where AO-Grasp produces successful grasps on 67.5% of scenes, while the baseline only produces successful grasps on 33.3% of scenes.
View on arXiv@article{morlans2025_2310.15928,
title={ AO-Grasp: Articulated Object Grasp Generation },
author={ Carlota Parés Morlans and Claire Chen and Yijia Weng and Michelle Yi and Yuying Huang and Nick Heppert and Linqi Zhou and Leonidas Guibas and Jeannette Bohg },
journal={arXiv preprint arXiv:2310.15928},
year={ 2025 }
}