Simulated Robot Hardware Parameter Tuning to Reduce Reality Gap
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning-based robotic control approaches face challenges in generating accurate training data, as real-world robot data collection is time-consuming, resource-intensive, and causes wear and tear, while simulated data often lacks realism due to the 'reality gap' between simulated and real environments.
Innovation Solution
Optimizing simulated hardware parameters of robotic simulators by using real navigation data instances to iteratively adjust parameters, such as wheel friction and controller gains, to reduce the reality gap and improve the accuracy 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 and tear increase significantly
Solution Approach 1:
The patent creates a virtual copy (simulated robot) that replicates the physical robot's hardware parameters and behavior. This digital twin is trained in a simulated environment, producing training data that accurately reflects real-world robot behavior without requiring extensive physical robot operation. The copy principle resolves the contradiction by replacing time-consuming real robot data collection with efficient simulation-based data generation.
Solution Approach 2:
The patent systematically adjusts hardware parameters (wheel diameter, friction coefficients, motor characteristics) in the simulation to match the physical robot's actual parameters. By calibrating these parameters through optimization algorithms that minimize the difference between simulated and real robot behavior, the simulation generates accurate training data without requiring the physical robot to be operated extensively, thus reducing time consumption while maintaining data accuracy.
2Measurement precision
If training data is generated using real-world physical robots, then the quality of training data is improved, but the resource consumption and operational costs increase
Solution Approach 1:
The virtual robot copy performs all training operations in a digital environment, eliminating the need for continuous physical robot operation. This drastically reduces energy consumption and operational costs while maintaining training data quality, as the simulation can run indefinitely without wear and tear or additional resource costs.
Solution Approach 2:
The simulation system generates its own training data autonomously without requiring physical robot operation. The virtual environment self-generates diverse training scenarios and automatically collects data, eliminating the need for human operators to physically maneuver the robot through various situations, thereby reducing both resource consumption and operational complexity.
3Quantity of substance
If the number of physical robots used for data collection is increased, then the quantity of training data is improved, but the wear and tear and maintenance requirements increase
Solution Approach 1:
Instead of deploying multiple physical robots that would all suffer wear and tear, the patent uses a single virtual robot copy that can generate unlimited training data without degradation. The digital twin can be reset and reused indefinitely, producing vast quantities of training data while eliminating the wear and tear problem entirely.
Solution Approach 2:
The patent pre-configures the virtual robot with accurate hardware parameters and pre-simulates diverse operating conditions to generate comprehensive training data before actual deployment. This preliminary data generation in the virtual environment provides abundant training examples without requiring multiple physical robots to undergo wear and tear during data collection.
4Ease of operation
If simulated robot hardware parameters are not optimized, then the simplicity of simulation is maintained, but the reality gap between simulated and real environments increases
Solution Approach 1:
The patent systematically adjusts simulation hardware parameters (wheel friction, motor characteristics, base inertia) to match the physical robot's actual parameters. Through optimization algorithms that minimize the difference between simulated and real robot behavior, the simulation maintains simplicity while accurately reflecting real-world physics, thereby reducing the reality gap without significantly complicating the simulation setup.
Solution Approach 2:
The patent implements a feedback mechanism where the simulated robot's behavior is continuously compared with the physical robot's actual behavior. Based on this feedback, hardware parameters in the simulation are iteratively adjusted to minimize discrepancies. This closed-loop optimization process reduces the reality gap while maintaining simulation simplicity, as the parameter tuning is automated rather than requiring complex manual configuration.
Data Source
AI summary
Mitigating the reality gap through optimization of one or more simulated hardware parameters for simulated hardware components of a simulated robot. Implementations generate and store real navigation data instances that are each based on a corresponding episode of locomotion of a real robot. A real navigation data instance can include a sequence of velocity control instances generated to control a real robot during a real episode of locomotion of the real robot, and one or more ground truth values, where each of the ground truth values is a measured value of a corresponding property of the real robot (e.g., pose). The velocity control instances can be applied to a simulated robot, and one or more losses can be generated based on comparing the ground truth value(s) to corresponding simulated value(s) generated from applying the velocity control instances to the simulated robot. The simulated hardware parameters and environmental parameters can be optimized based on the loss(es).