Simulated Image Adaptation for Real-World Physical Process Training
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Solution Overview
Problem
Existing techniques 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 method involving machine learning models that generate and augment simulated images to reflect real-world conditions by creating mappings between simulated and real-world images, allowing for automated and scalable training data generation 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 data collection is 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, providing accurate training data without requiring physical data collection. The simulation engine generates unlimited synthetic training data that mirrors real-world conditions.
Solution Approach 2:
The patent pre-configures simulation environments with realistic lighting models, material properties, and camera parameters before generating training data. By establishing accurate simulation settings in advance, the system produces synthetic images that closely match real-world conditions, eliminating the need for subsequent manual adjustments or real-world data collection.
2Productivity
If synthetic data is used to train physical processes, then data collection efficiency improves and scalability increases, but synthetic data fails to capture real-world conditions accurately
Solution Approach 1:
The patent systematically varies simulation parameters including lighting angles, intensity, material properties, camera positions, and environmental conditions to generate diverse synthetic training data. This parameter variation ensures the synthetic data captures the full range of real-world conditions, improving generalization while maintaining generation efficiency.
Solution Approach 2:
The simulation engine creates synthetic images that copy the essential visual characteristics of real-world images, including shading, lighting effects, noise patterns, and color distributions. By replicating these real-world properties in synthetic data, the system maintains high fidelity without requiring manual processing.
3Reliability
If manual processing or augmentation is applied to synthetic data, then real-world condition representation improves, but processing time and complexity increase
Solution Approach 1:
The simulation engine automatically generates realistic lighting, shading, and environmental effects without requiring manual intervention. The system self-configures camera parameters, material properties, and lighting conditions to produce synthetic images that directly reflect real-world conditions, eliminating the need for manual augmentation or post-processing.
Solution Approach 2:
The patent pre-configures the simulation environment with accurate physical models, material properties, and lighting conditions before generating training data. By establishing realistic simulation parameters in advance, the system produces synthetic images that require no manual processing, saving significant time and computational resources.
4Adaptability or versatility
If synthetic data is used without adaptation, then training scalability improves, but performance in real-world settings degrades
Solution Approach 1:
The patent varies simulation parameters to match different real-world operating conditions, including lighting environments, camera characteristics, and object properties. This parameter adaptation ensures the synthetic training data is specific to the target application, improving real-world performance while maintaining scalable automated generation.
Solution Approach 2:
The simulation engine creates synthetic data that copies the specific visual characteristics of the target real-world environment, including lighting conditions, material appearances, and sensor noise patterns. This targeted copying ensures high transferability from simulation to real-world deployment.
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.