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.

翻译：我们提出AO-Grasp，一种为铰接物体生成稳定且可交互的六自由度抓取位姿的抓取建议方法。该方法生成的抓取使机器人能够与铰接物体（如开关橱柜和家电）进行交互。给定单个铰接物体的分割部分点云，AO-Grasp通过新型可交互抓取点预测模型预测物体上的最佳抓取点，并利用最先进的刚体抓取方法为每个点获取对应的抓取方向。我们基于新构建的AO-Grasp数据集训练该模型，该数据集包含4.8万个针对合成铰接物体的可行平行爪抓取。在仿真实验中，AO-Grasp在训练和测试类别上的抓取成功率均高于现有刚体抓取和铰接物体交互基线方法。此外，我们在120个具有不同几何形状、铰接轴及关节状态的真实场景中评估AO-Grasp，其在67.5%的场景中成功生成抓取，而基线方法仅在33.3%的场景中成功。