Visual Completion Of 3D Object Shapes From A Single View For Robotic Tasks The goal of this paper is to predict 3D object shape to improve the visual perception of robots in grasping and manipulation tasks. The planning of imagebased robotic manipulation tasks depends on the recognition of the objectxe2x80x99s shape. Mostly, the manipulator robots usually use a camera with configuration eyeinhand. This fact limits the calculation of the grip on the visible part of the object. In this paper, we present a 3D Deep Convolutional Neural Network to predict the hidden parts of objects from a singleview and to accomplish recovering the complete shape of them. We have tested our proposal with both previously seen objects and novel objects from a wellknown dataset.

Robust Grasp Planning Over Uncertain Shape Completions 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 runtime 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 stateoftheart 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 shapeuncertaintyaware grasps brings significant advantages over solely planning on a single shape estimate, especially when dealing with complex or unknown objects.