Abstract: One embodiment of a method for generating an articulation model includes receiving a first set of images of an object in a first articulation and a second set of images of the object in a second articulation, performing one or more operations to generate first three-dimensional (3D) geometry based on the first set of images, performing one or more operations to generate second 3D geometry based on the second set of images, and performing one or more operations to generate an articulation model of the object based on the first 3D geometry and the second 3D geometry.

Abstract: One embodiment of a method for determining object poses includes receiving first sensor data and second sensor data, where the first sensor data is associated with a first modality, and the second sensor data is associated with a second modality that is different from the first modality, and performing one or more iterative operations to determine a pose of an object based on one or more comparisons of (i) one or more renderings of a three-dimensional (3D) representation of the object in the first modality with the first sensor data, and (ii) one or more renderings of the 3D representation of the object in the second modality with the second sensor data.

Abstract: Apparatuses, systems, and techniques to generate data to train a robotic device to perform tasks. In at least one embodiment, one or more first videos of a robotic device performing a task is used to generate one or more second videos of the robotic device performing the task differently than depicted in the one or more first videos.

Abstract: Apparatuses, systems, and techniques for constructing a data structure to store a shape of an object based at least in part on a portion of multiple images, and obtaining poses of the object by tracking a pose of the object through the multiple images based at least in part on the data structure. Optionally, the poses may be used to generate a plan for a path of a device to travel, generate a rendering of at least a portion of a Mixed Reality (“MR”) display to be viewed by a user, and/or the like.

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.