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    MACHINE LEARNING TECHNIQUES FOR DIRECT BOUNDARY REPRESENTATION SYNTHESIS
    272.
    发明公开
    MACHINE LEARNING TECHNIQUES FOR DIRECT BOUNDARY REPRESENTATION SYNTHESIS 审中-公开

    公开(公告)号：US20240289505A1

    公开(公告)日：2024-08-29

    申请号：US18407327

    申请日：2024-01-08

    Applicant: AUTODESK, INC.

    Inventor： Pradeep Kumar JAYARAMAN ,  Nishkrit DESAI ,  Joseph George LAMBOURNE ,  Nigel Jed Wesley MORRIS ,  Aditya SANGHI ,  Karl D. D. WILLIS

    IPC: G06F30/12 ,  G06N7/01

    CPC classification number: G06F30/12 ,  G06N7/01

    Abstract: One embodiment of the present invention sets forth a technique for generating 3D CAD model representations of three-dimensional objects. The technique includes generating a vertex list that includes a first ordered list of elements representing vertex coordinates and sampling a first index from the vertex list based on a first probability distribution. The technique also includes generating an edge list and sampling a second index from one or more indices into the edge list. The technique further includes generating an element in a face list, dereferencing the element in the face list to retrieve an element in the edge list, and dereferencing an element in the edge list to retrieve a vertex coordinate from an element in the vertex list. The technique further includes generating an indexed boundary representation for the 3D CAD model based on at least the vertex list, the edge list, and the face list.

    Techniques for CAD-informed robotic assembly
    274.
    发明授权
    Techniques for CAD-informed robotic assembly 有权

    公开(公告)号：US12030185B2

    公开(公告)日：2024-07-09

    申请号：US16667843

    申请日：2019-10-29

    Applicant: AUTODESK, INC.

    Inventor： Heather Kerrick ,  Erin Bradner ,  Hui Li ,  Evan Patrick Atherton ,  Nicholas Cote

    IPC: B25J9/16 ,  G05B19/4097

    CPC classification number: B25J9/1658 ,  B25J9/1664 ,  G05B19/4097 ,  G05B2219/35134 ,  G05B2219/45064

    Abstract: An assembly engine is configured to generate, based on a computer-aided design (CAD) assembly, a set of motion commands that causes the robot to manufacture a physical assembly corresponding to the CAD assembly. The assembly engine analyzes the CAD assembly to determine an assembly sequence for various physical components to be included in the physical assembly. The assembly sequence indicates the order in which each physical component should be incorporated into the physical assembly and how those physical components should be physically coupled together. The assembly engine further analyzes the CAD assembly to determine different component paths that each physical component should follow when being incorporated into the physical assembly. Based on the assembly sequence and the component paths, the assembly engine generates a set of motion commands that the robot executes to assemble the physical components into the physical assembly.

    TECHNIQUES FOR SOLVING INVERSE KINEMATIC PROBLEMS USING TRAINED MACHINE LEARNING MODELS
    279.
    发明公开
    TECHNIQUES FOR SOLVING INVERSE KINEMATIC PROBLEMS USING TRAINED MACHINE LEARNING MODELS 审中-公开

    公开(公告)号：US20240070949A1

    公开(公告)日：2024-02-29

    申请号：US17822108

    申请日：2022-08-24

    Applicant: AUTODESK, INC.

    Inventor： Evan Patrick ATHERTON ,  Dieu Linh TRAN

    IPC: G06T13/40

    CPC classification number: G06T13/40 ,  G06T2213/08

    Abstract: In various embodiments, a computer animation application automatically solves inverse kinematic problems when generating object animations. The computer animation application determines a target vector based on a target value for a joint parameter associated with a joint chain and at least one of a target position or a target orientation for an end-effector associated with the joint chain. The computer animation application executes a trained machine learning model on the target vector to generate a predicted vector that includes data associated with multiple joint parameters associated with the joint chain.

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