Abstract: The present technology relates to fence assemblies which are adaptable to various uses and can be used to build various fence configurations. According to certain embodiments, the fence assemblies of the present technology comprise a mount post having a first channel defined at least in part by side walls in the mount post body and a support stud configured to be inserted in the first channel and connected therein, the support stud having a support surface to support a rail and/or a panel extending transversely from the mount post.

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: 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: One embodiment of a method for generating simulation code includes generating first program code that simulates an environment in which a robot can perform a task and second program code that includes one or more tests; determining, using a first trained machine learning model, that one or more errors during execution of the first program code and the second program code are caused by at least one of the first program code or the second program code; and updating the at least one of the first program code or the second program code based on the one or more errors to generate at least one of updated first program code or updated second program code.

Abstract: One embodiment of a method for processing data includes receiving an image; segmenting, using a first trained machine learning model, the image to generate a segmentation mask; generating one or more descriptions of one or more objects using a second machine learning model and based on the segmentation mask; generating program code using a third trained machine learning model and based on the one or more descriptions, the image, and a task; and causing a robot to move based on the program code.

Abstract: One embodiment of a method for processing data includes receiving an image; segmenting, using a first trained machine learning model, the image to generate a segmentation mask; generating one or more descriptions of one or more objects using a second machine learning model and based on the segmentation mask; generating program code using a third trained machine learning model and based on the one or more descriptions, the image, and a task; and causing a robot to move based on the program code.

Abstract: Apparatuses, systems, and techniques are described that solve task and motion planning problems. In at least one embodiment, a task and motion planning problem is modeled using a geometric scene graph that records positions and orientations of objects within a playfield, and a symbolic scene graph that represents states of objects within context of a task to be solved. In at least one embodiment, task planning is performed using symbolic scene graph, and motion planning is performed using a geometric scene graph.

Abstract: Systems utilizing a vision-language model configured with a training dataset that includes images labeled with spatial question-and-answer pairs, the question-and-answer pairs encoding object-to-object relationships and object-to-space relationships depicted in the images, and at least one data processor configured to operate the vision-language model to carry out a robotic task.

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: 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 to perform a grasp of on object using an articulated robotic hand equipped with one or more tactile sensors. In at least one embodiment, a machine-learned model trained in simulation to grasp a cuboid using signals received from tactile sensors is applied to grasping objects of various shapes in a real-world environment.

Abstract: Apparatuses, systems, and techniques are presented to determine a pose of an object. In at least one embodiment, a network is trained to predict a pose of an autonomous object based, at least in part, on only one image of the autonomous object.

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: Apparatuses, systems, and techniques to determine orientation of an objects in an image. In at least one embodiment, images are processed using a neural network trained to determine orientation of an object.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, ray tracing is caused to be selectively performed on one or more objects within a three-dimensional (3D) environment.

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 are described that solve task and motion planning problems. In at least one embodiment, a task and motion planning problem is modeled using a geometric scene graph that records positions and orientations of objects within a playfield, and a symbolic scene graph that represents states of objects within context of a task to be solved. In at least one embodiment, task planning is performed using symbolic scene graph, and motion planning is performed using a geometric scene graph.

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: 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.