公开(公告)号：US11953879B2

公开(公告)日：2024-04-09

申请号：US17015000

申请日：2020-09-08

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

CPC classification number: G05B19/4099 ,  B22D23/003 ,  B33Y50/00 ,  B23K9/044 ,  G05B2219/49023 ,  G06T2219/008

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.

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：US10956739B2

公开(公告)日：2021-03-23

申请号：US15194342

申请日：2016-06-27

Applicant: Autodesk, Inc.

Inventor： David Thomasson ,  Evan Patrick Atherton ,  Maurice Ugo Conti ,  Heather Kerrick

IPC: G06K9/00 ,  B25J9/16 ,  G02B27/01 ,  G06T19/00 ,  H04N7/18

Abstract: A technique for displaying a representative path associated with a robotic device. The technique includes detecting at least one reference point within a first image of a workspace, generating the representative path based on path instructions associated with the robotic device and the at least one reference point, and displaying the representative path within the workspace.

公开(公告)号：US20170372139A1

公开(公告)日：2017-12-28

申请号：US15194342

申请日：2016-06-27

Applicant: Autodesk, Inc.

Inventor： David Thomasson ,  Evan Patrick Atherton ,  Maurice Ugo Conti ,  Heather Kerrick

IPC: G06K9/00 ,  G06T19/00 ,  B25J9/16 ,  H04N7/18 ,  G02B27/01

CPC classification number: G06K9/00671 ,  B25J9/1664 ,  B25J9/1671 ,  G02B27/017 ,  G05B2219/39451 ,  G06T19/006 ,  H04N7/183 ,  Y10S901/30 ,  Y10S901/47

Abstract: A technique for displaying a representative path associated with a robotic device. The technique includes detecting at least one reference point within a first image of a workspace, generating the representative path based on path instructions associated with the robotic device and the at least one reference point, and displaying the representative path within the workspace.

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：US20200264583A1

公开(公告)日：2020-08-20

申请号：US16866442

申请日：2020-05-04

Applicant: Autodesk, Inc.

Inventor： Evan Patrick Atherton ,  David Thomasson ,  Maurice Ugo Conti ,  Heather Kerrick

IPC: G05B19/402 ,  G05B19/18

Abstract: A robot is configured to assist an end-user with creative tasks. While the end-user modifies the work piece, the robot observes the modifications made by the end-user and determines one or more objectives that the end-user may endeavor to accomplish. The robot then determines a set of actions to perform that assist the end-user with accomplishing the objectives.

公开(公告)号：US20180065247A1

公开(公告)日：2018-03-08

申请号：US15258957

申请日：2016-09-07

Applicant: Autodesk, Inc.

Inventor： Evan Patrick Atherton ,  David Thomasson ,  Heather Kerrick ,  Maurice Ugo Conti

IPC: B25J9/16 ,  G05B19/042

CPC classification number: B25J9/163 ,  G05B19/042 ,  G05B2219/40264 ,  G05B2219/40391

Abstract: A control engine is trained to operate a robotic camera according to a variety of different cinematographic techniques. The control engine may reconfigure the robotic camera to respond to a set of cues, to enforce a set of constraints, or to apply one or more characteristic styles. A training engine trains a network within the control engine based on training data that exemplifies cue responses, enforced constraints, and characteristic styles.

公开(公告)号：US20170177747A1

公开(公告)日：2017-06-22

申请号：US14274508

申请日：2014-05-09

Applicant: Autodesk, Inc.

Inventor： Evan Patrick Atherton ,  Mark Thomas Davis ,  Heike Rapp-Wurm ,  Arthur Harsuvanakit ,  Negar Arabani ,  Erin Marie Bradner ,  James La Fleur

IPC: G06F17/50

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reconfigurable spaces. One of the methods includes identifying plan information relating to a space under evaluation. Constraints related to structures associated with the plan information are identified. Input regarding uses or elements to be included in a reconfigurable design for the space is received. A library of elements for inclusion in the space is evaluated, including determining one or more reconfigurable elements that satisfy the received input. A first configuration of a reconfigurable element is determined including a first placement in a first design associated with the space, and the first design in accordance with the first configuration is presented. A second different configuration is determined, including a second placement in a second different design associated with the space, and the second different design in accordance with the second different configuration is presented.

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：US11556108B2

公开(公告)日：2023-01-17

申请号：US16866442

申请日：2020-05-04

Applicant: Autodesk, Inc.

Inventor： Evan Patrick Atherton ,  David Thomasson ,  Maurice Ugo Conti ,  Heather Kerrick

IPC: G05B19/402 ,  G05B19/18

Abstract: A robot is configured to assist an end-user with creative tasks. While the end-user modifies the work piece, the robot observes the modifications made by the end-user and determines one or more objectives that the end-user may endeavor to accomplish. The robot then determines a set of actions to perform that assist the end-user with accomplishing the objectives.