Robot Training via Ray Traced Simulation and Domain Randomization
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Solution Overview
Problem
Conventional simulation techniques generate training data that is not sufficiently realistic for machine learning models, leading to failures in controlling robots in real-world environments, and real-world training can cause damage to robots and objects.
Innovation Solution
The use of a vision model trained with simulated images rendered via ray tracing and augmented with different camera parameters and visual effects, combined with a robot control model trained using reinforcement learning and simulations with randomized physics and non-physics parameters, to generate robust training data for controlling robots in real-world environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional simulation techniques are used to generate training data, then training data can be obtained without physical robot operation, but the training data is not sufficiently realistic leading to model failure in real-world environments
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical robot and its environment that accurately replicates real-world physics, geometry, and sensor characteristics. This virtual copy generates training data that mirrors real-world conditions without requiring physical robot operation, thereby improving both data realism and operational safety.
Solution Approach 2:
The patent systematically varies parameters in the simulation environment including physics parameters (friction, mass, gravity), sensor parameters (camera noise, lighting conditions), and task parameters to generate diverse training scenarios. This parameter randomization enables the model to learn robust behaviors across varying conditions while maintaining realistic training data generation.
2Manufacturing precision
If real-world training is used to improve model realism, then training data becomes more realistic, but damage and wear occur to the robot and objects
Solution Approach 1:
By using a digital twin to replicate real-world physics and interactions, the system obtains training data without physical contact between the robot and objects. The virtual environment captures realistic collision dynamics, friction, and material properties while eliminating physical wear and damage.
Solution Approach 2:
The patent performs extensive training in the virtual environment before any physical robot operation. This preliminary training in realistic simulation conditions prepares the model for real-world deployment while minimizing the need for physical trial-and-error that would cause wear and damage.
3Object-affected harmful factors
If simulation training is used to avoid damage, then no damage occurs to robot and objects, but the training data lacks realism causing model failure
Solution Approach 1:
The patent implements domain randomization by systematically varying simulation parameters including physics properties (mass, friction, restitution), sensor characteristics (camera noise, exposure, field of view), and environmental conditions (lighting, background). This exposes the model to diverse realistic scenarios during training, improving real-world generalization.
Solution Approach 2:
The digital twin accurately replicates real-world sensor models, physics engines, and environmental characteristics, ensuring that training data from simulation closely matches real-world data distributions while maintaining zero physical wear.
4Reliability
If extensive real-world training is performed to improve model robustness, then model reliability increases, but training time and operational disruption increase
Solution Approach 1:
The digital twin enables parallel training experiments to be conducted virtually without consuming physical robot time. Multiple training scenarios can be executed simultaneously in simulation, dramatically reducing the time required to achieve robust model performance compared to sequential physical training.
Solution Approach 2:
The patent performs comprehensive training in the virtual environment before physical deployment. This preliminary training in diverse scenarios prepares the model for real-world operation, minimizing the need for time-consuming physical trial-and-error tuning and reducing operational disruption.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for the successful deployment of machine learning models to control robots in real-world environments without causing damage, relying on images captured by RGB cameras and reducing the need for marker-based setups or real-world training.
Implementation Method 1
relying on images captured by RGB cameras
Implementation Method 2
images of simulations of the robot that are rendered via ray tracing
Data Source
AI summary
Systems and techniques are described related to training one or more machine learning models for use in control of a robot. In at least one embodiment, one or more machine learning models are trained based at least on simulations of the robot and renderings of such simulations—which may be performed using one or more ray tracing algorithms, operations, or techniques.