Simulated Training Image Adaptation for Real-World Physical Processes
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Solution Overview
Problem
Existing methods for training physical processes, such as industrial robots and self-driving cars, face inefficiencies due to the tedious and costly collection of real-world data, and synthetic data often fails to accurately capture real-world conditions, requiring manual processing to be effective.
Innovation Solution
A technique using machine learning models to generate and augment simulated training data by mapping simulated images to real-world images, adding shading, lighting, and noise to create more realistic images that can be used for training physical processes without degrading performance in real-world settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world data is collected to train physical processes, then training data accuracy reflects real conditions, but the process becomes tedious, inefficient, and difficult to scale
Solution Approach 1:
The patent creates synthetic copies of real-world images through simulation environments. These synthetic images replicate the visual characteristics, lighting conditions, and object appearances of real-world scenarios without requiring physical data collection. The simulation engine generates training data that mirrors real conditions while eliminating the inefficiencies of manual collection.
Solution Approach 2:
The patent systematically varies simulation parameters such as lighting conditions, camera angles, object positions, and environmental factors to generate diverse training datasets. By changing these parameters programmatically, the system achieves scalability and efficiency while maintaining the realism needed for accurate training.
2Productivity
If synthetic data is used to train physical processes, then data collection becomes efficient and scalable, but the data fails to capture real-world conditions like shading, lighting, and noise
Solution Approach 1:
The patent introduces an intermediary processing layer that translates synthetic simulation data into realistic training images. This intermediary system applies transformations that mimic real-world effects, bridging the gap between idealized simulation and actual physical conditions. The intermediary ensures synthetic data maintains the statistical properties and visual characteristics of real data.
Solution Approach 2:
The patent applies multiple layers of augmentation and transformation to synthetic images, adding realistic imperfections, noise, and variations that exceed the basic simulation output. By applying excessive transformations, the system ensures the synthetic data captures the full range of real-world variability.
3Reliability
If manual processing or augmentation is applied to synthetic data to reflect real-world conditions, then real-world condition representation improves, but the automation and efficiency benefits are reduced
Solution Approach 1:
The patent implements self-service automation where the simulation system automatically generates, processes, and augments its own training data without human intervention. The system self-adjusts parameters, applies transformations, and validates output quality programmatically, maintaining both automation and realism through automated feedback loops.
Solution Approach 2:
The patent incorporates feedback mechanisms that automatically evaluate synthetic data quality and adjust generation parameters accordingly. The system uses automated metrics to assess whether synthetic images adequately represent real-world conditions and iteratively refines the simulation parameters to improve realism without manual processing.
Data Source
AI summary
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.