ADAPTING SIMULATION DATA TO REAL-WORLD CONDITIONS ENCOUNTERED BY PHYSICAL PROCESSES

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...

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Bibliographische Detailangaben
Hauptverfasser:	LI, Hui, COTE, Nicholas, ATHERTON, Evan Patrick, KERRICK, Heather, BRADNER, Erin
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING CHAMBERS PROVIDED WITH MANIPULATION DEVICES COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONTROL OR REGULATING SYSTEMS IN GENERAL CONTROLLING COUNTING ELECTRIC DIGITAL DATA PROCESSING FUNCTIONAL ELEMENTS OF SUCH SYSTEMS HAND TOOLS IMAGE DATA PROCESSING OR GENERATION, IN GENERAL MANIPULATORS MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS PERFORMING OPERATIONS PHYSICS PORTABLE POWER-DRIVEN TOOLS REGULATING TRANSPORTING
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Beschreibung
Zusammenfassung:	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.