Robot Control Training Using Reality-Gap Feedback Calibration
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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 fails to accurately reflect real-world environments due to a significant 'reality gap' between simulated and real systems.
Innovation Solution
The method involves quantifying and adapting 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, and using the adapted simulator to generate more realistic simulated training examples for machine learning model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training data is collected from real-world physical robots, then the training examples accurately reflect real environment conditions, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent creates virtual copies of real-world robot environments through simulated environments that replicate physical robot characteristics, sensor behaviors, and task conditions. These virtual copies generate training data without requiring physical robot operation, thus maintaining data accuracy while eliminating time-consuming real-world data collection
Solution Approach 2:
The system adjusts simulator parameters to match real-world conditions, including robot dynamics, sensor noise characteristics, and environmental properties. By calibrating these parameters based on real robot data, the simulated training examples accurately reflect real environment conditions without requiring extensive real-world data collection
2Measurement precision
If training data is collected from real-world physical robots, then the training examples are authentic, but resource consumption and robot wear increase
Solution Approach 1:
Virtual environments serve as digital twins of physical robot systems, capturing authentic robot behaviors and environmental interactions through simulation. This copying approach generates authentic training data without consuming physical robot resources or energy
Solution Approach 2:
The patent replaces physical mechanical robot operation with computational simulation. Instead of physically moving robots to collect data, the system uses software-based simulations that model robot dynamics and sensor outputs, eliminating energy consumption and mechanical wear while maintaining data authenticity
3Productivity
If a robotic simulator is used to generate simulated training examples, then data collection is efficient, but a reality gap exists between simulated and real environments
Solution Approach 1:
The system systematically adjusts simulator parameters to bridge the reality gap, including robot mass, friction coefficients, sensor noise levels, and actuator dynamics. By calibrating these parameters against real robot measurements, the simulated environments more accurately reflect physical reality while maintaining generation efficiency
Solution Approach 2:
The patent implements feedback mechanisms where simulated training examples are evaluated against real robot performance data. Discrepancies identify areas where simulator parameters need adjustment, creating an iterative improvement process that reduces the reality gap while preserving the efficiency benefits of simulation
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.