Robotic Simulator State Correction for Reality Gap Mitigation
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Solution Overview
Problem
Existing 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 'reality gap' between simulated and real-world conditions.
Innovation Solution
A difference model is trained to modify simulated state data generated by robotic simulators, making it more accurate by compensating for unmodeled or incorrectly modeled properties, thereby reducing the need for real-world data and improving the realism of simulated training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world physical robots are used to generate training data, then data accuracy is improved, but time consumption and resource usage increase significantly
Solution Approach 1:
The patent creates a virtual copy (robotic simulator) that replicates the physical robot's behavior and environment. This digital twin generates training data without requiring physical robot operation, thus maintaining data accuracy while eliminating time consumption and resource usage associated with physical robots.
Solution Approach 2:
The patent replaces the mechanical system (physical robot) with a software-based system (robotic simulator). The simulator uses computational models to replicate physical robot dynamics, sensor readings, and environmental interactions, substituting hardware resource consumption with computational processing.
2Measurement precision
If real-world physical robots are used to generate training data, then data accuracy is improved, but wear and tear on robots increases
Solution Approach 1:
The patent uses a virtual replica of the physical robot in simulation environment. This copy undergoes all the operational stress and wear instead of the actual robot, preserving the physical robot while generating equivalent training data through the simulated counterpart.
3Productivity
If robotic simulator is used to generate simulated training data, then time consumption is reduced, but data realism deteriorates due to reality gap
Solution Approach 1:
The patent implements feedback mechanisms where simulated data is continuously compared with real-world data, and the simulator parameters are adjusted based on this comparison. This closed-loop approach ensures that the simulated data progressively converges toward real-world accuracy while maintaining generation efficiency.
Solution Approach 2:
The patent modifies simulator parameters, physics models, and environmental conditions to better match real-world characteristics. By adjusting these parameters based on empirical data and validation, the simulator produces more realistic training data without sacrificing generation speed.
4Reliability
If more real-world training data is collected to mitigate reality gap, then model performance is improved, but resource consumption and time usage increase
Solution Approach 1:
The patent uses the robotic simulator as a digital twin to generate diverse training scenarios that would be time-consuming or impossible to capture with physical robots. This includes edge cases, failure modes, and varied environmental conditions, all generated virtually to improve model robustness without additional physical resource consumption.
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.