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 are presented to identify one or more objects. In at least one embodiment, one or more neural networks can be used to identify one or more objects based, at least in part, on one or more descriptors of one or more segments of the one or more objects.

Abstract: Apparatuses, systems, and techniques determine a set of grasp poses that would allow a robot to successfully grasp an object that is proximate to at least one additional object. In at least one embodiment, the set of grasp poses is modified based on a determination that at least one of the grasp poses in the set of grasp poses would interfere with at least one additional object that is proximate to the object.

Abstract: Apparatuses, systems, and techniques to estimate or predict depth information for image data. In at least one embodiment, depth information is predicted based at least in part on color information and geometry information associated with an image.

Abstract: Apparatuses, systems, and techniques determine a set of grasp poses that would allow a robot to successfully grasp an object that is proximate to at least one additional object. In at least one embodiment, the set of grasp poses is modified based on a determination that at least one of the grasp poses in the set of grasp poses would interfere with at least one additional object that is proximate to the object.

Abstract: Apparatuses, systems, and techniques to place one or more objects in a location and orientation. In at least one embodiment, one or more circuits are to use one or more neural networks to cause one or more autonomous devices to place one or more objects in a location and orientation based, at least in part, on one or more images of the location and orientation.

Abstract: In at least one embodiment, a system determines a set of possible grasp poses that allow a robot to successfully grasp an object by generating a set of potential grasp poses, and then evaluating the performance of each potential grasp pose. In at least one embodiment, the system performs a refinement operation on the grasp poses, and based on an evaluation of the poses, creates an improved set of possible grasps for the object.

Abstract: A three dimensional bounding box is determined from a two dimensional image. A two dimensional bounding box is calculated based on a detected object within the image. A three dimensional bounding box is parameterized as having a yaw angle, dimensions, and a position. The yaw angle is defined as the angle between a ray passing through a center of the two dimensional bounding box and an orientation of the three dimensional bounding box. The yaw angle and dimensions are determined by passing the portion of the image within the two dimensional bounding box through a trained convolutional neural network. The three dimensional bounding box is then positioned such that the projection of the three dimensional bounding box into the image aligns with the two dimensional bounding box previously detected. Characteristics of the three dimensional bounding box are then communicated to an autonomous system for collision and obstacle avoidance.

Abstract: In an embodiment, a system provides object tracking and 6D pose estimations to a robot that performs different tasks such as manipulation and navigation. In an embodiment the 6D object pose is determined using a Rao-Blackwellized particle filtering framework, where the 3-D rotation and the 3-D translation of the object is decoupled. In an embodiment, the system provides the 3-D translation of an object along with a full distribution over the 3-D rotation. In an embodiment, the 3-D rotation is determined by discretizing the rotation space, and training an autoencoder network to construct a codebook of feature embeddings for the discretized rotations. In an embodiment, the system is able to track objects with arbitrary symmetries while also maintaining adequate posterior distributions.

Abstract: Apparatuses, systems, and techniques to model a tactile force sensor. In at least one embodiment, output of tactile sensor is predicted from a modeled force and shape imposed on the sensor. In at least one embodiment, a shape of the surface of the tactile sensor is determined based at least in part on electrical signals received from the sensor.

Abstract: Apparatuses, systems, and techniques to grasp objects with a robot. In at least one embodiment, a neural network is trained to determine a grasp pose of an object within a cluttered scene using a point cloud generated by a depth camera.

Abstract: Apparatuses, systems, and techniques to estimate or predict depth information for image data. In at least one embodiment, depth information is predicted based at least in part on color information and geometry information associated with an image.

Abstract: Systems, devices, and methods are described for generating dense depth estimates, and confidence values associated with such depth estimates, from image data. A machine learning algorithm can be trained using image data and associated depth values captured by one or more LIDAR sensors providing a ground truth. When the algorithm is deployed in a machine vision system, image data and/or depth data can be used to determine dense depth estimates for all pixels of the image data, as well as confidence values for each depth estimate. Such confidence values may be indicative of how confident the machine learned algorithm is of the associated depth estimate.

Abstract: Apparatuses, systems, and techniques for determining whether collisions will occur in potential paths of an object within a scene. In at least one embodiment, one or more neural networks determine whether collisions will occur in potential paths of an object within a scene based at least in part on point cloud data of the object and the scene.

Abstract: Apparatuses, systems, and techniques estimate a pose of an object based on images generated from a combined image volume. In at least one embodiment, the combined image volume is obtained from a plurality of image volumes generated based on a plurality of images of an object.

Abstract: Apparatuses, systems, and techniques determine a set of grasp poses that would allow a robot to successfully grasp an object that is proximate to at least one additional object. In at least one embodiment, the set of grasp poses is modified based on a determination that at least one of the grasp poses in the set of grasp poses would interfere with at least one additional object that is proximate to the object.

Abstract: A three dimensional bounding box is determined from a two dimensional image. A two dimensional bounding box is calculated based on a detected object within the image. A three dimensional bounding box is parameterized as having a yaw angle, dimensions, and a position. The yaw angle is defined as the angle between a ray passing through a center of the two dimensional bounding box and an orientation of the three dimensional bounding box. The yaw angle and dimensions are determined by passing the portion of the image within the two dimensional bounding box through a trained convolutional neural network. The three dimensional bounding box is then positioned such that the projection of the three dimensional bounding box into the image aligns with the two dimensional bounding box previously detected. Characteristics of the three dimensional bounding box are then communicated to an autonomous system for collision and obstacle avoidance.

Abstract: In at least one embodiment, a system determines a set of possible grasp poses that allow a robot to successfully grasp an object by generating a set of potential grasp poses, and then evaluating the performance of each potential grasp pose. In at least one embodiment, the system performs a refinement operation on the grasp poses, and based on an evaluation of the poses, creates an improved set of possible grasps for the object.

Abstract: In an embodiment, a system provides object tracking and 6D pose estimations to a robot that performs different tasks such as manipulation and navigation. In an embodiment the 6D object pose is determined using a Rao-Blackwellized particle filtering framework, where the 3-D rotation and the 3-D translation of the object is decoupled. In an embodiment, the system provides the 3-D translation of an object along with a full distribution over the 3-D rotation. In an embodiment, the 3-D rotation is determined by discretizing the rotation space, and training an autoencoder network to construct a codebook of feature embeddings for the discretized rotations. In an embodiment, the system is able to track objects with arbitrary symmetries while also maintaining adequate posterior distributions.

Abstract: A three dimensional bounding box is determined from a two dimensional image. A two dimensional bounding box is calculated based on a detected object within the image. A three dimensional bounding box is parameterized as having a yaw angle, dimensions, and a position. The yaw angle is defined as the angle between a ray passing through a center of the two dimensional bounding box and an orientation of the three dimensional bounding box. The yaw angle and dimensions are determined by passing the portion of the image within the two dimensional bounding box through a trained convolutional neural network. The three dimensional bounding box is then positioned such that the projection of the three dimensional bounding box into the image aligns with the two dimensional bounding box previously detected. Characteristics of the three dimensional bounding box are then communicated to an autonomous system for collision and obstacle avoidance.