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    ADAPTING SIMULATION DATA TO REAL-WORLD CONDITIONS ENCOUNTERED BY PHYSICAL PROCESSES
    1.
    发明申请
    ADAPTING SIMULATION DATA TO REAL-WORLD CONDITIONS ENCOUNTERED BY PHYSICAL PROCESSES 审中-公开

    公开(公告)号：US20180349527A1

    公开(公告)日：2018-12-06

    申请号：US15995003

    申请日：2018-05-31

    Applicant: AUTODESK, INC.

    Inventor： Hui LI ,  Evan Patrick ATHERTON ,  Erin BRADNER ,  Nicholas COTE ,  Heather KERRICK

    IPC: G06F17/50 ,  G06T17/00 ,  G06N99/00

    Abstract: One embodiment of the present invention sets forth a technique for generating simulated training data for a physical process. The technique includes receiving, as input to at least one machine learning model, a first simulated image of a first object, wherein the at least one machine learning model includes mappings between simulated images generated from models of physical objects and real-world images of the physical objects. The technique also includes performing, by the at least one machine learning model, one or more operations on the first simulated image to generate a first augmented image of the first object. The technique further includes transmitting the first augmented image to a training pipeline for an additional machine learning model that controls a behavior of the physical process.

    ADAPTING SIMULATION DATA TO REAL-WORLD CONDITIONS ENCOUNTERED BY PHYSICAL PROCESSES
    2.
    发明申请
    ADAPTING SIMULATION DATA TO REAL-WORLD CONDITIONS ENCOUNTERED BY PHYSICAL PROCESSES 审中-公开

    公开(公告)号：US20180345496A1

    公开(公告)日：2018-12-06

    申请号：US15995005

    申请日：2018-05-31

    Applicant: AUTODESK, INC.

    Inventor： Hui LI ,  Evan Patrick ATHERTON ,  Erin BRADNER ,  Nicholas COTE ,  Heather KERRICK

    IPC: B25J9/16

    Abstract: One embodiment of the present invention sets forth a technique for controlling the execution of a physical process. The technique includes receiving, as input to a machine learning model that is configured to adapt a simulation of the physical process executing in a virtual environment to a physical world, simulated output for controlling how the physical process performs a task in the virtual environment and real-world data collected from the physical process performing the task in the physical world. The technique also includes performing, by the machine learning model, one or more operations on the simulated output and the real-world data to generate augmented output. The technique further includes transmitting the augmented output to the physical process to control how the physical process performs the task in the physical world.

    ROBOTIC ASSEMBLY OF A MESH SURFACE
    3.
    发明申请
    ROBOTIC ASSEMBLY OF A MESH SURFACE 审中-公开

    公开(公告)号：US20180341730A1

    公开(公告)日：2018-11-29

    申请号：US15607289

    申请日：2017-05-26

    Applicant: AUTODESK, INC.

    Inventor： Evan Patrick ATHERTON ,  David THOMASSON ,  Maurice Ugo CONTI ,  Heather KERRICK ,  Nicholas COTE

    IPC: G06F17/50 ,  B25J9/10 ,  A01C7/04

    Abstract: A robotic assembly cell is configured to generate a physical mesh of physical polygons based on a simulated mesh of simulated triangles. A control application configured to operate the assembly cell selects a simulated polygon in the simulated mesh and then causes a positioning robot in the cell to obtain a physical polygon that is similar to the simulated polygon. The positioning robot positions the polygon on the physical mesh, and a welding robot in the cell then welds the polygon to the mesh. The control application captures data that reflects how the physical polygon is actually positioned on the physical mesh, and then updates the simulated mesh to be geometrically consistent with the physical mesh. In doing so, the control application may execute a multi-objective solver to generate an updated simulated mesh that meets specific design criteria.

    TECHNIQUES FOR SOLVING INVERSE KINEMATIC PROBLEMS USING TRAINED MACHINE LEARNING MODELS
    5.
    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.

    MACHINE LEARNING TECHNIQUES FOR GENERATING INVERSE KINEMATIC MODELS FOR CHARACTER ANIMATION
    6.
    发明公开
    MACHINE LEARNING TECHNIQUES FOR GENERATING INVERSE KINEMATIC MODELS FOR CHARACTER ANIMATION 审中-公开

    公开(公告)号：US20240070517A1

    公开(公告)日：2024-02-29

    申请号：US17822106

    申请日：2022-08-24

    Applicant: AUTODESK, INC.

    Inventor： Evan Patrick ATHERTON ,  Dieu Linh TRAN

    IPC: G06N20/00 ,  G06T15/10 ,  G06T17/20

    CPC classification number: G06N20/00 ,  G06T15/10 ,  G06T17/20

    Abstract: In various embodiments, an inverse kinematic (IK) modeling application generates models that are used to solve IK problems for object animations. The IK modeling application generates configuration vectors based on a set of joint parameters associated with a joint chain. The IK modeling application executes forward kinematic operation(s) on the joint chain based on the configuration vectors to generate target vectors. Each target vector includes data associated with at least one of a position or an orientation for an end-effector associated with the joint chain. The IK modeling application performs one or more machine learning (ML) operations on an ML model based on the configuration vectors and the target vectors to generate a trained ML model that computes a predicted joint vector associated with the joint chain based on at least one of a target position or a target orientation for the end-effector.

    TECHNIQUES FOR ROBOTIC WORKCELL DESIGN
    7.
    发明公开
    TECHNIQUES FOR ROBOTIC WORKCELL DESIGN 审中-公开

    公开(公告)号：US20230182302A1

    公开(公告)日：2023-06-15

    申请号：US17548355

    申请日：2021-12-10

    Applicant: AUTODESK, INC.

    Inventor： Evan Patrick ATHERTON ,  Ardavan BIDGOLI

    IPC: B25J9/16

    CPC classification number: B25J9/1674

    Abstract: A computer-implemented method for generating and evaluating robotic workcell solutions includes: determining a plurality of locations within a workcell volume, wherein each location corresponds to a possible workcell solution; for each location included in the plurality of locations, determining a value for a first robot-motion attribute for a first robot based on position information associated with the location and a trajectory associated with a component of the first robot; and, for each location included in the plurality of locations, computing a first value for a first performance metric based on the value for the first robot-motion attribute.

    AGENT-BASED SLICING
    8.
    发明申请
    AGENT-BASED SLICING 审中-公开

    公开(公告)号：US20180348735A1

    公开(公告)日：2018-12-06

    申请号：US15613070

    申请日：2017-06-02

    Applicant: AUTODESK, INC.

    Inventor： Evan Patrick ATHERTON ,  David THOMASSON ,  Maurice Ugo CONTI ,  Heather KERRICK ,  Nicholas COTE ,  Hui LI

    IPC: G05B19/4099 ,  B22D23/00 ,  B33Y50/00 ,  B23K9/04 ,  B23K9/095

    CPC classification number: G05B19/4099 ,  B22D23/003 ,  B23K9/044 ,  B33Y50/00 ,  G05B2219/49023

    Abstract: An agent engine allocates a collection of agents to scan the surface of an object model. Each agent operates autonomously and implements particular behaviors based on the actions of nearby agents. Accordingly, the collection of agents exhibits swarm-like behavior. Over a sequence of time steps, the agents traverse the surface of the object model. Each agent acts to avoid other agents, thereby maintaining a relatively consistent distribution of agents across the surface of the object model over all time steps. At a given time step, the agent engine generates a slice through the object model that intersects each agent in a group of agents. The slice associated with a given time step represents a set of locations where material should be deposited to fabricate a 3D object. Based on a set of such slices, a robot engine causes a robot to fabricate the 3D object.

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