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.

公开(公告)号：US11679506B2

公开(公告)日：2023-06-20

申请号：US17691838

申请日：2022-03-10

Applicant: AUTODESK, INC.

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

IPC: B25J9/16 ,  G06N20/00 ,  G05B19/418 ,  G06N3/08 ,  G06F30/20 ,  G06N3/044 ,  G06T17/00

CPC classification number: B25J9/1671 ,  B25J9/161 ,  B25J9/163 ,  B25J9/1605 ,  G05B19/41885 ,  G06F30/20 ,  G06N3/044 ,  G06N3/08 ,  G06N20/00 ,  G06T17/00 ,  G05B2219/32017 ,  G05B2219/35353

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.

公开(公告)号：US10751879B2

公开(公告)日：2020-08-25

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: G06F19/00 ,  B25J9/16 ,  G06N20/00 ,  G05B19/418 ,  G06N3/04 ,  G06N3/08 ,  G06F30/20 ,  G06T17/00

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.

公开(公告)号：US11654565B2

公开(公告)日：2023-05-23

申请号：US16940288

申请日：2020-07-27

Applicant: AUTODESK, INC.

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

IPC: G06F17/00 ,  B25J9/16 ,  G06N20/00 ,  G05B19/418 ,  G06N3/04 ,  G06N3/08 ,  G06F30/20 ,  G06T17/00

CPC classification number: B25J9/1671 ,  B25J9/161 ,  B25J9/163 ,  B25J9/1605 ,  G05B19/41885 ,  G06F30/20 ,  G06N3/0445 ,  G06N3/08 ,  G06N20/00 ,  G06T17/00 ,  G05B2219/32017 ,  G05B2219/35353

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.

公开(公告)号：US11607806B2

公开(公告)日：2023-03-21

申请号：US16892216

申请日：2020-06-03

Applicant: AUTODESK, INC.

Inventor： Michael Haley ,  Erin Bradner ,  Pantelis Katsiaris

IPC: B25J9/16

Abstract: A model generator implements a data-driven approach to generating a robot model that describes one or more physical properties of a robot. The model generator generates a set of basis functions that generically describes a range of physical properties of a wide range of systems. The model generator then generates a set of coefficients corresponding to the set of basis functions based on one or more commands issued to the robot, one or more corresponding end effector positions implemented by the robot, and a sparsity constraint. The model generator generates the robot model by combining the set of basis functions with the set of coefficients. In doing so, the model generator disables specific basis functions that do not describe physical properties associated with the robot. The robot model can subsequently be used within a robot controller to generate commands for controlling the robot.

公开(公告)号：US11593533B2

公开(公告)日：2023-02-28

申请号：US16358635

申请日：2019-03-19

Applicant: AUTODESK, INC.

Inventor： Tovi Grossman ,  Erin Bradner ,  George Fitzmaurice ,  Ali Baradaran Hashemi ,  Michael Glueck ,  Justin Frank Matejka

IPC: G06F3/048 ,  G06F30/00 ,  G06F3/04815 ,  G06F3/04845 ,  G06F3/0482 ,  G06F16/2457 ,  G06T15/00 ,  G06F30/17 ,  G06F30/23

Abstract: A design application is configured to visualize and explore large-scale generative design datasets. The design explorer includes a graphical user interface (GUI) engine that generates a design explorer, a composite explorer, and a tradeoff explorer. The design explorer displays a visualization of a multitude of design options included in a design space. The design explorer allows a user to filter the design space based on input parameters that influence a generative design process as well as various design characteristics associated with the different design options. The composite explorer displays a fully interactive composite of multiple different design options. The composite explorer exposes various tools that allow the user to filter the design space via interactions with the composite. The tradeoff explorer displays a tradeoff space based on different rankings of design options. The different rankings potentially correspond to competing design characteristics specified by different designers.

公开(公告)号：US11273553B2

公开(公告)日：2022-03-15

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: B25J9/16 ,  G06N20/00 ,  G05B19/418 ,  G06N3/04 ,  G06N3/08 ,  G06F30/20 ,  G06T17/00

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.