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 significant 'reality gap'.
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 training data is generated using real-world physical robots, then the accuracy and realism of training data is improved, but the time consumption, resource consumption, and wear to robots increase significantly
Solution Approach 1:
The patent creates a virtual copy (digital twin) of the physical robot that replicates its dynamics, kinematics, and behavior. This virtual replica generates training data through simulation, eliminating the need to physically move and operate real robots for data collection. The digital twin is trained using real-world data initially, then generates synthetic training data that maintains high fidelity to real robot behavior without requiring continuous physical robot operation.
Solution Approach 2:
The patent replaces the mechanical data collection process (physically moving robots to collect training data) with a computational simulation system. Instead of using physical actuators and sensors to gather data, the system uses mathematical models and computer simulations to generate equivalent training data, substituting mechanical operations with information processing operations.
2Loss of time
If training data is generated using robotic simulators, then the time consumption and resource usage are reduced, but the accuracy and realism of training data deteriorate due to the reality gap
Solution Approach 1:
The patent enhances the virtual replica by training it with real-world collected data, creating a high-fidelity digital copy that bridges the reality gap. The digital twin learns the nuances of real robot behavior, including unmodeled dynamics and environmental interactions, from actual sensor data and control signals, then reproduces this behavior in simulated training scenarios.
Solution Approach 2:
The system uses real-world robot performance data as feedback to continuously improve and refine the virtual replica's accuracy. By comparing simulated outputs with actual robot behavior and adjusting the digital twin's parameters accordingly, the system closes the reality gap and ensures the generated training data remains highly accurate while maintaining computational efficiency.
3Device complexity
If a simple robotic simulator is used, then the device complexity is reduced, but the ability to accurately model real-world properties deteriorates
Solution Approach 1:
Instead of attempting to create a perfectly accurate physical simulator, the patent creates a virtual replica that is trained to copy real robot behavior. The digital twin uses learned models from real data to replicate complex phenomena like friction, inertia, and environmental interactions, achieving high accuracy without requiring the simulator to explicitly model every physical detail.
Solution Approach 2:
The system dynamically adjusts parameters of the virtual replica based on real-world observations and operational conditions. By learning optimal parameter values from actual robot performance data, the simplified simulator adapts its characteristics to match real-world behavior across different scenarios, maintaining accuracy without increasing structural complexity.
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.