  We present a method for planning robust grasps over uncertain shape completed
objects. For shape completion, a deep neural network is trained to take a
partial view of the object as input and outputs the completed shape as a voxel
grid. The key part of the network is dropout layers which are enabled not only
during training but also at run-time to generate a set of shape samples
representing the shape uncertainty through Monte Carlo sampling. Given the set
of shape completed objects, we generate grasp candidates on the mean object
shape but evaluate them based on their joint performance in terms of analytical
grasp metrics on all the shape candidates. We experimentally validate and
benchmark our method against another state-of-the-art method with a Barrett
hand on 90000 grasps in simulation and 200 grasps on a real Franka Emika Panda.
All experimental results show statistically significant improvements both in
terms of grasp quality metrics and grasp success rate, demonstrating that
planning shape-uncertainty-aware grasps brings significant advantages over
solely planning on a single shape estimate, especially when dealing with
complex or unknown objects.
