Neural Digital Twin Modeling for Sim-to-Real Robotics Accuracy
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Solution Overview
Problem
Existing robotics simulators face significant 'sim-to-real' gaps due to inadequate representation of features like friction, backlash, and unpredictable behaviors, leading to inaccurate simulations and limited flexibility and scalability.
Innovation Solution
A neural network-based system that trains on real-world measurements to predict states of robotics devices, using a transformer architecture to augment digital simulations and model components based on observed behaviors, reducing the gap between simulated and real-world performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional robotics simulators are used to create digital representations, then the simulation can be generated without real-world data, but the simulation accuracy suffers due to inadequate representation of friction, backlash, and unpredictable behaviors
Solution Approach 1:
The patent creates a digital twin by copying the physical robotics device's behavior through neural network training on real-world sensor data. The neural network learns to replicate the actual device's state transitions, capturing complex phenomena like friction and backlash that are difficult to model traditionally. This copying approach achieves high simulation accuracy without requiring explicit mathematical models of all physical effects.
Solution Approach 2:
The patent replaces traditional physics-based simulation mechanics with a data-driven neural network approach. Instead of using explicit mechanical models for friction, backlash, and other complex behaviors, the system uses a trained neural network that has learned these patterns from real-world data. This substitution simplifies the simulation system while improving accuracy for unpredictable behaviors.
2Reliability
If detailed physics models are added to represent friction, backlash, and unpredictable behaviors, then simulation accuracy improves, but the device complexity and computational requirements increase significantly
Solution Approach 1:
The patent substitutes complex physics-based models with a neural network that learns reliable patterns from real-world data. The neural network captures friction, backlash, and unpredictable behaviors through training on actual sensor data, achieving high reliability without explicit mechanical models. This data-driven approach reduces model complexity while maintaining or improving simulation reliability.
Solution Approach 2:
The patent changes the fundamental parameter representation from explicit physics model parameters (friction coefficients, backlash values) to neural network weights and activations learned from data. This parameter transformation allows the system to capture complex behaviors reliably while keeping the model structure relatively simple and computationally efficient.
3Measurement precision
If custom simulation models are created for each specific robotics device, then the simulation accuracy for that device improves, but the scalability and flexibility across different devices decreases
Solution Approach 1:
The patent creates a universal neural network framework that can be applied to multiple robotics devices. The system processes sensor data from various device types through a common architecture, learning device-specific patterns while maintaining consistent methodology. This universal approach enables the same system to generate accurate digital twins for different robotics devices without requiring custom model development for each one.
Solution Approach 2:
The patent segments the simulation problem into learnable components through the neural network architecture. By processing sensor data through distinct processing streams and combining them in the network, the system captures device-specific characteristics while using a unified framework. This segmentation allows the model to adapt to different devices while maintaining overall system consistency and scalability.
Data Source
AI summary
Systems and methods for training a neural network to predict states of a robotics device are disclosed. Robotics data is received for a robotics device, including indications of a set of components, a digital simulation of the robotics device, and measurement data received from a sensor associated with the robotics device. The set of components includes an actuator and a structural element. A training dataset is generated using the received robotics data. Generating the training dataset includes comparing the measurement data with simulated measurement data based on the digital simulation. A neural network is trained using the generated training dataset to modify the digital simulation of the robotics device to predict a state of the robotics device, such as a position, motion, electrical quantity, or other. When trained, the neural network is applied to predict states of the robotics device or a different robotics device.