Robot Control Training With Reality-Gap-Calibrated Simulation
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Solution Overview
Problem
Existing machine learning-based robotic control approaches face challenges in generating accurate training examples, as real-world data collection is time-consuming, resource-intensive, and causes wear to physical robots, while simulated data often lacks realism due to a significant 'reality gap' between simulated and real environments.
Innovation Solution
A method to quantify and adapt the parameters of a robotic simulator to reduce the reality gap by comparing simulated and real-world task success measures, iteratively modifying simulator parameters until the gap meets criteria, allowing for the generation of more realistic simulated training examples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training examples are generated using real-world physical robots, then the training data accuracy is improved, but the time consumption and resource usage increase significantly
Solution Approach 1:
The patent creates virtual copies of physical robots and their environments through robotic simulators. These digital twins replicate the mechanical properties, sensor behaviors, and environmental conditions of real robots, enabling training data generation without physically deploying actual robots. The virtual environment maintains sufficient fidelity to produce training examples that accurately reflect real-world scenarios while eliminating time-consuming physical experimentation.
Solution Approach 2:
The patent systematically adjusts simulator parameters such as friction coefficients, mass properties, sensor noise characteristics, and environmental conditions to match real-world physics. By calibrating these parameters through comparison with empirical data and iterative refinement, the simulator generates training examples that preserve the statistical properties and physical realism of real robot operations without requiring actual physical trials.
2Measurement precision
If training examples are generated using real-world physical robots, then the training data realism is improved, but the wear and tear on robots increases
Solution Approach 1:
The patent replaces physical robots with virtual replicas in simulated environments. These digital twins replicate all mechanical components, actuator behaviors, and sensor characteristics of the physical robots. By conducting all training operations in the virtual domain, the patent eliminates physical wear and tear on actuators, joints, and sensors while maintaining training data realism through accurate modeling of robot dynamics and sensor noise profiles.
Solution Approach 2:
The robotic simulator serves itself by generating all necessary training data internally without requiring external physical resources. The simulator contains embedded models of robot mechanics, physics engines for realistic interaction, and synthetic sensor generation capabilities, allowing it to produce unlimited training examples autonomously without consuming physical robot resources or causing degradation.
3Ease of operation
If robotic simulator parameters are kept simple, then the ease of operation is improved, but the reality gap between simulated and real environments increases
Solution Approach 1:
The patent divides the complex task of reducing the reality gap into separate, manageable modules: physics engine configuration, sensor model calibration, environmental parameter tuning, and validation against real-world data. Each module can be independently adjusted and validated, allowing users to systematically improve realism without overwhelming complexity. This modular approach maintains ease of operation while achieving high fidelity through targeted parameter refinement.
4Measurement precision
If extensive real-world data collection is performed to mitigate the reality gap, then the training data quality is improved, but the resource consumption and time usage increase
Solution Approach 1:
The patent performs preliminary calibration of the robotic simulator using a small initial dataset from real robots. During this setup phase, key parameters such as friction coefficients, mass properties, and sensor noise characteristics are tuned to match real-world behavior. Once calibrated, the simulator can generate unlimited high-quality training examples without requiring additional real-world data collection, thereby minimizing resource consumption while maintaining training data quality throughout the training process.
Data Source
AI summary
Implementations are directed to generating simulated training examples for training of a machine learning model, training the machine learning model based at least in part on the simulated training examples, and/or using the trained machine learning model in control of at least one real-world physical robot. Implementations are additionally or alternatively directed to performing one or more iterations of quantifying a “reality gap” for a robotic simulator and adapting parameter(s) for the robotic simulator based on the determined reality gap. The robotic simulator with the adapted parameter(s) can further be utilized to generate simulated training examples when the reality gap of one or more iterations satisfies one or more criteria.