Abstract: In at least one embodiment, under the control of a robotic control system, a gripper on a robot is positioned to grasp a 3-dimensional object. In at least one embodiment, the relative position of the object and the gripper is determined, at least in part, by using a camera mounted on the gripper.

Abstract: Apparatuses, systems, and techniques to determine a pose and relative dimensions of an object from an image. In at least one embodiment, a pose and relative dimensions of an object are determined from an image based at least in part on, for example, features of the image.

Abstract: A robot is controlled using a combination of model-based and model-free control methods. In some examples, the model-based method uses a physical model of the environment around the robot to guide the robot. The physical model is oriented using a perception system such as a camera. Characteristics of the perception system may be are used to determine an uncertainty for the model. Based at least in part on this uncertainty, the system transitions from the model-based method to a model-free method where, in some embodiments, information provided directly from the perception system is used to direct the robot without reliance on the physical model.

Abstract: Apparatuses, systems, and techniques to map coordinates in task space to a set of joint angles of an articulated robot. In at least one embodiment, a neural network is trained to map task-space coordinates to joint space coordinates of a robot by simulating a plurality of robots at various joint angles, and determining the position of their respective manipulators in task space.

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: Various embodiments enable a robot, or other autonomous or semi-autonomous device or system, to receive data involving the performance of a task in the physical world. The data can be provided as input to a perception network to infer a set of percepts about the task, which can correspond to relationships between objects observed during the performance. The percepts can be provided as input to a plan generation network, which can infer a set of actions as part of a plan. Each action can correspond to one of the observed relationships. The plan can be reviewed and any corrections made, either manually or through another demonstration of the task. Once the plan is verified as correct, the plan (and any related data) can be provided as input to an execution network that can infer instructions to cause the robot, and/or another robot, to perform the task.

Abstract: Apparatuses, systems, and techniques generate poses of an object based on image data of the object obtained from a first viewpoint of the object and a second viewpoint of the object. The poses can be evaluated to determine a portion of the image data usable by an estimator to generate a pose of the object.

Abstract: Apparatuses, systems, and techniques to generate a prompt for one or more machine learning processes. In at least one embodiment, the machine learning process(es) generate(s) a plan to perform a task (identified in the prompt) that is to be performed by an agent (real world or virtual).

Abstract: Apparatuses, systems, and techniques generate poses of an object based on data of the object observed from a first viewpoint and a second viewpoint. The poses can be evaluated to determine a portion of the data usable by an estimator to generate a pose of the object.

Abstract: In at least one embodiment, under the control of a robotic control system, a gripper on a robot is positioned to grasp a 3-dimensional object. In at least one embodiment, the relative position of the object and the gripper is determined, at least in part, by using a camera mounted on the gripper.

Abstract: Apparatuses, systems, and techniques to determined a pose of an object from a plurality of images. In at least one embodiment, the pose of an object is determined from at least two images of a video sequence using one or more neural networks, in which the neural network produces a distribution of pose information that is filtered to determine the current pose.

Abstract: A robot device determines an error associated with equipment included in a data center environment. The robot device may compare the error to candidate errors for which the robot device is already trained to resolve. Based on a result of the comparison, the robot device may perform, in a control environment, candidate maintenance operations in association with resolving the error. The robot device may learn a set of actions associated with successfully resolving the error, based on performing the candidate maintenance operations. The robot device may perform maintenance operations associated with the error. Performing the maintenance operations may include applying the learned set of actions.

Abstract: In at least one embodiment, under the control of a robotic control system, a gripper on a robot is positioned to grasp a 3-dimensional object. In at least one embodiment, the relative position of the object and the gripper is determined, at least in part, by using a camera mounted on the gripper.

Abstract: A manifold voxel mesh or surface mesh is manufacturable by carving a single block of material and a non-manifold mesh is not manufacturable. Conventional techniques for constructing or extracting a surface mesh from an input point cloud often produce a non-manifold voxel mesh. Similarly, extracting a surface mesh from a voxel mesh that includes non-manifold geometry produces a surface mesh that includes non-manifold geometry. To ensure that the surface mesh includes only manifold geometry, locations of the non-manifold geometry in the voxel mesh are detected and converted into manifold geometry. The result is a manifold voxel mesh from which a manifold surface mesh of the object may be extracted.

Abstract: Training deep neural networks requires a large amount of labeled training data. Conventionally, labeled training data is generated by gathering real images that are manually labelled which is very time-consuming. Instead of manually labelling a training dataset, domain randomization technique is used generate training data that is automatically labeled. The generated training data may be used to train neural networks for object detection and segmentation (labelling) tasks. In an embodiment, the generated training data includes synthetic input images generated by rendering three-dimensional (3D) objects of interest in a 3D scene. In an embodiment, the generated training data includes synthetic input images generated by rendering 3D objects of interest on a 2D background image. The 3D objects of interest are objects that a neural network is trained to detect and/or label.

Abstract: A manifold voxel mesh or surface mesh is manufacturable by carving a single block of material and a non-manifold mesh is not manufacturable. Conventional techniques for constructing or extracting a surface mesh from an input point cloud often produce a non-manifold voxel mesh. Similarly, extracting a surface mesh from a voxel mesh that includes non-manifold geometry produces a surface mesh that includes non-manifold geometry. To ensure that the surface mesh includes only manifold geometry, locations of the non-manifold geometry in the voxel mesh are detected and converted into manifold geometry. The result is a manifold voxel mesh from which a manifold surface mesh of the object may be extracted.

Abstract: A single two-dimensional (2D) image can be used as input to obtain a three-dimensional (3D) representation of the 2D image. This is done by extracting features from the 2D image by an encoder and determining a 3D representation of the 2D image utilizing a trained 2D convolutional neural network (CNN). Volumetric rendering is then run on the 3D representation to combine features within one or more viewing directions, and the combined features are provided as input to a multilayer perceptron (MLP) that predicts and outputs color (or multi-dimensional neural features) and density values for each point within the 3D representation. As a result, single-image inverse rendering may be performed using only a single 2D image as input to create a corresponding 3D representation of the scene in the single 2D image.

Abstract: Apparatuses, systems, and techniques generate poses of an object based on data of the object observed from a first viewpoint and a second viewpoint. The poses can be evaluated to determine a portion of the data usable by an estimator to generate a pose of the object.