  We introduce AO-Grasp, a grasp proposal method that generates 6 DoF grasps
that enable robots to interact with articulated objects, such as opening and
closing cabinets and appliances. AO-Grasp consists of two main contributions:
the AO-Grasp Model and the AO-Grasp Dataset. Given a segmented partial point
cloud of a single articulated object, the AO-Grasp Model predicts the best
grasp points on the object with an Actionable Grasp Point Predictor. Then, it
finds corresponding grasp orientations for each of these points, resulting in
stable and actionable grasp proposals. We train the AO-Grasp Model on our new
AO-Grasp Dataset, which contains 78K actionable parallel-jaw grasps on
synthetic articulated objects. In simulation, AO-Grasp achieves a 45.0 % grasp
success rate, whereas the highest performing baseline achieves a 35.0% success
rate. Additionally, we evaluate AO-Grasp on 120 real-world 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. To the best of our knowledge, AO-Grasp is
the first method for generating 6 DoF grasps on articulated objects directly
from partial point clouds without requiring part detection or hand-designed
grasp heuristics. Project website: https://stanford-iprl-lab.github.io/ao-grasp
