Simulation-to-Real Control Adaptation for Physical Processes
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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 need for extensive real-world data collection, and synthetic data often fails to accurately reflect real-world conditions, requiring manual processing to be effective.
Innovation Solution
A machine learning model is used to adapt simulation data from a virtual environment to real-world conditions by combining simulated output with real-world data, generating augmented output that can control physical processes in various environments without manual customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world data is collected to train physical processes, then training accuracy is improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent creates synthetic copies of real-world data through simulation environments. These synthetic datasets replicate the statistical properties and distributions of real-world data without requiring actual physical data collection, thereby maintaining training accuracy while dramatically reducing time consumption
Solution Approach 2:
The system performs preliminary actions by pre-generating synthetic training data through simulations before actual physical training is needed. This allows the physical processes to be trained using pre-prepared synthetic datasets, eliminating the time-consuming real-world data collection phase
2Productivity
If synthetic data is used to train physical processes, then productivity is improved, but data realism and training effectiveness deteriorate
Solution Approach 1:
The patent applies parameter changes by adjusting synthetic data characteristics to match real-world data distributions. Techniques include modifying noise levels, adding realistic artifacts, and tuning statistical parameters to bridge the gap between synthetic and real data, thereby improving data realism while maintaining high productivity
Solution Approach 2:
The system introduces an intermediary layer that transforms synthetic data into more realistic forms. This intermediary processing stage applies real-world effects and characteristics to synthetic datasets, serving as a bridge between the idealized simulation environment and actual physical conditions
3Ease of operation
If synthetic data is used without manual processing, then ease of operation is improved, but manual processing would enhance data quality
Solution Approach 1:
The patent implements self-service by enabling the synthetic data generation system to automatically produce high-quality training data without requiring manual intervention. The system self-adjusts parameters and automatically generates realistic synthetic datasets, maintaining both ease of operation and data quality through automated processes
4Adaptability or versatility
If simulation data is adapted to real-world conditions, then adaptability is improved, but system complexity increases
Solution Approach 1:
The patent applies universality by creating a multi-functional system that can generate synthetic data for multiple different physical processes and environments using a single simulation framework. This universal approach improves adaptability across different applications while avoiding the complexity of separate systems for each specific case
Data Source
AI summary
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.