Invention Grant
- Patent Title: Adapting simulation data to real-world conditions encountered by physical processes
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Application No.: US16940288Application Date: 2020-07-27
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Publication No.: US11654565B2Publication Date: 2023-05-23
- Inventor: Hui Li , Evan Patrick Atherton , Erin Bradner , Nicholas Cote , Heather Kerrick
- Applicant: AUTODESK, INC.
- Applicant Address: US CA San Francisco
- Assignee: AUTODESK, INC.
- Current Assignee: AUTODESK, INC.
- Current Assignee Address: US CA San Francisco
- Agency: Artegis Law Group, LLP
- Main IPC: G06F17/00
- IPC: G06F17/00 ; 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.
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