Abstract: Apparatuses, systems, and techniques to generate an image of one or more objects. In at least one embodiment, an image of one or more objects is generated using a neural network based on, for example, a representation of a scene.

Abstract: Apparatuses, systems, and techniques to generate and select grasp proposals. In at least one embodiment, grasp proposals are generated and selected using one or more neural networks, based on, for example, a latent code corresponding to an object.

Abstract: One embodiment of a method for controlling a robot includes generating a representation of spatial occupancy within an environment based on a plurality of red, green, blue (RGB) images of the environment, determining one or more actions for the robot based on the representation of spatial occupancy and a goal, and causing the robot to perform at least a portion of a movement based on the one or more actions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for robotic control using demonstrations to learn category-level manipulation task. One of the methods includes obtaining a collection of object models for a plurality of different types of objects belonging to a same object category and training a category-level representation in a category-level space from the collection of object models. A category-level trajectory is generated the demonstration data of a demonstration object. For a new object in the object category, a trajectory projection is generated in the category-level space, which is used to cause a robot to perform the robotic manipulation task on the new object.

Abstract: A computer-implemented method includes obtaining a collection of object models for a plurality of different types of objects belonging to a same object category, generating a canonical representation for objects belonging to the object category, performing a plurality of downstream tasks using a plurality of different robot grasps on instances of objects belonging to the category and evaluating each grasp according to success or failure of the downstream task; and generating one or more category-level grasping areas for the canonical representation for objects belonging to the object category including aggregating the evaluations of grasps according to the downstream task.