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    LEARNING ROBOTIC TASKS USING ONE OR MORE NEURAL NETWORKS
    3.
    发明申请
    LEARNING ROBOTIC TASKS USING ONE OR MORE NEURAL NETWORKS 有权

    公开(公告)号：US20210390653A1

    公开(公告)日：2021-12-16

    申请号：US17458221

    申请日：2021-08-26

    Applicant: Nvidia Corporation

    Inventor： Jonathan Tremblay ,  Stan Birchfield ,  Stephen Tyree ,  Thang To ,  Jan Kautz ,  Artem Molchanov

    IPC: G06T1/00 ,  G06T7/73 ,  B25J9/16 ,  G06N3/08 ,  G05B13/00

    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.

    Detecting and estimating the pose of an object using a neural network model
    4.
    发明授权
    Detecting and estimating the pose of an object using a neural network model 有权

    公开(公告)号：US11074717B2

    公开(公告)日：2021-07-27

    申请号：US16405662

    申请日：2019-05-07

    Applicant: NVIDIA Corporation

    Inventor： Jonathan Tremblay ,  Thang Hong To ,  Stanley Thomas Birchfield

    IPC: G06T7/73 ,  G06N3/04 ,  G06N3/08

    Abstract: An object detection neural network receives an input image including an object and generates belief maps for vertices of a bounding volume that encloses the object. The belief maps are used, along with three-dimensional (3D) coordinates defining the bounding volume, to compute the pose of the object in 3D space during post-processing. When multiple objects are present in the image, the object detection neural network may also generate vector fields for the vertices. A vector field comprises vectors pointing from the vertex to a centroid of the object enclosed by the bounding volume defined by the vertex. The object detection neural network may be trained using images of computer-generated objects rendered in 3D scenes (e.g., photorealistic synthetic data). Automatically labelled training datasets may be easily constructed using the photorealistic synthetic data. The object detection neural network may be trained for object detection using only the photorealistic synthetic data.

    OBJECT-TO-ROBOT POSE ESTIMATION FROM A SINGLE RGB IMAGE
    5.
    发明申请
    OBJECT-TO-ROBOT POSE ESTIMATION FROM A SINGLE RGB IMAGE 审中-公开

    公开(公告)号：US20200311855A1

    公开(公告)日：2020-10-01

    申请号：US16902097

    申请日：2020-06-15

    Applicant: NVIDIA Corporation

    Inventor： Jonathan Tremblay ,  Stephen Walter Tyree ,  Stanley Thomas Birchfield

    IPC: G06T1/00 ,  G06K9/00 ,  G06K9/20 ,  G06K9/62

    Abstract: Pose estimation generally refers to a computer vision technique that determines the pose of some object, usually with respect to a particular camera. Pose estimation has many applications, but is particularly useful in the context of robotic manipulation systems. To date, robotic manipulation systems have required a camera to be installed on the robot itself (i.e. a camera-in-hand) for capturing images of the object and/or a camera external to the robot for capturing images of the object. Unfortunately, the camera-in-hand has a limited field of view for capturing objects, whereas the external camera, which may have a greater field of view, requires costly calibration each time the camera is even slightly moved. Similar issues apply when estimating the pose of any object with respect to another object (i.e. which may be moving or not). The present disclosure avoids these issues and provides object-to-object pose estimation from a single image.

    FEW-SHOT VIEWPOINT ESTIMATION
    6.
    发明申请
    FEW-SHOT VIEWPOINT ESTIMATION 审中-公开

    公开(公告)号：US20200252600A1

    公开(公告)日：2020-08-06

    申请号：US16780738

    申请日：2020-02-03

    Applicant: NVIDIA Corporation

    Inventor： Hung-Yu Tseng ,  Shalini De Mello ,  Jonathan Tremblay ,  Sifei Liu ,  Jan Kautz ,  Stanley Thomas Birchfield

    IPC: H04N13/282 ,  H04N13/268 ,  G06N3/08 ,  G06K9/62

    Abstract: When an image is projected from 3D, the viewpoint of objects in the image, relative to the camera, must be determined. Since the image itself will not have sufficient information to determine the viewpoint of the various objects in the image, techniques to estimate the viewpoint must be employed. To date, neural networks have been used to infer such viewpoint estimates on an object category basis, but must first be trained with numerous examples that have been manually created. The present disclosure provides a neural network that is trained to learn, from just a few example images, a unique viewpoint estimation network capable of inferring viewpoint estimations for a new object category.

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