Difference Model Adaptation of Robotic Simulator State Data
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Solution Overview
Problem
Machine learning-based robotic control approaches face challenges in generating accurate training data, as real-world data collection is time-consuming, resource-intensive, and causes wear to physical robots, while simulated data often fails to accurately reflect real-world environments due to a significant 'reality gap'.
Innovation Solution
A difference model is trained to modify simulated state data generated by robotic simulators, making it more reflective of real-world conditions by compensating for unmodeled or incorrectly modeled properties, thereby reducing the need for real-world data and improving the accuracy of machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world physical robots are used to generate training data, then the training data accurately reflects real-world conditions, but the process is time-consuming, resource-intensive, and causes wear to physical robots
Solution Approach 1:
The patent uses a robotic simulator to create virtual copies of physical robots and their operating environments. These simulated robots generate training data through simulation rather than physical operation, eliminating time consumption and wear while maintaining realistic training scenarios through careful modeling of physical properties and dynamics
Solution Approach 2:
The patent introduces a domain adaptation layer as an intermediary between the simulated environment and the machine learning model. This layer translates simulated observations into formats that match real-world distributions, allowing training data from simulation to accurately reflect real-world conditions without requiring direct physical robot operation
2Productivity
If robotic simulators are used to generate simulated training data, then the process is efficient and resource-saving, but a significant 'reality gap' exists between simulated and real-world data
Solution Approach 1:
The domain adaptation layer serves as a mediator that bridges the reality gap between simulated and real-world data distributions. It transforms simulated observations into real-world equivalent formats through learned translation mappings, enabling efficient simulated data generation while maintaining accuracy for machine learning training
Solution Approach 2:
The patent modifies the parameter distributions of simulated data through the domain adaptation layer. By learning and applying distributional transformations, the system changes the statistical parameters of simulated observations to match real-world distributions, closing the reality gap while preserving generation efficiency
3Reliability
If more real-world training data is collected to mitigate the reality gap, then the accuracy of machine learning models improves, but the time consumption, resource usage, and robot wear increase
Solution Approach 1:
The patent creates virtual training data through robotic simulation that, when processed through the domain adaptation layer, provides machine learning models with training examples that accurately reflect real-world conditions. This approach achieves model reliability without requiring extensive physical robot operation
Solution Approach 2:
The domain adaptation layer enables the use of simulated training data to achieve real-world model performance by translating simulated observations into real-world equivalent distributions. This eliminates the need for extensive real-world data collection while maintaining model reliability
Data Source
AI summary
Mitigating the reality gap through training and utilization of at least one difference model. The difference model can be utilized to generate, for each of a plurality of instances of simulated state data generated by a robotic simulator, a corresponding instance of modified simulated state data. The difference model is trained so that a generated modified instance of simulated state data is closer to “real world data” than is a corresponding initial instance of simulated state data. Accordingly, the difference model can be utilized to mitigate the reality gap through modification of initially generated simulated state data, to make it more accurately reflect what would occur in a real environment. Moreover, the difference representation from the difference model can be used as input to the control policy to adapt the control learned from simulator to the real environment.