Graph Neural Networks for Physical System State Inference and Control
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Solution Overview
Problem
Existing neural networks struggle to accurately represent and predict the dynamic and static properties of physical systems, particularly in real-world scenarios where properties are partially observable and require efficient learning and control.
Innovation Solution
A graph neural network system that processes input graphs representing physical systems, comprising nodes for bodies and edges for joints, using edge and node neural networks to infer static and dynamic properties, and a global feature vector to provide accurate predictions and control actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neural networks are used to represent physical systems, then the model structure is simple, but the accuracy of predicting dynamic and static properties is insufficient
Solution Approach 1:
The patent segments the physical system into discrete bodies and joints, representing them as nodes and edges in a graph structure. This segmentation allows the complex continuous physical system to be represented in a discrete, computationally manageable form while preserving the essential dynamics and interactions, thereby improving prediction accuracy without excessive complexity
Solution Approach 2:
The patent introduces a graph-based representation dimension, transforming the traditional Euclidean space representation of physical systems into a graph manifold. This dimensional transformation enables the model to capture topological relationships and interactions between bodies and joints, significantly improving the ability to predict both static and dynamic properties simultaneously
2Measurement precision
If the neural network learns from real-world physical systems, then the model becomes more accurate, but the learning process requires significant computational resources and time
Solution Approach 1:
The patent employs preliminary actions by pre-defining the graph structure and initial node/embedding representations before training. The graph skeleton and connectivity information are prepared in advance, allowing the neural network to focus computational resources on learning the dynamic properties rather than reconstructing the entire system representation, thereby reducing learning time while maintaining accuracy
Solution Approach 2:
The patent uses simulated physical systems as copies of real-world systems for training purposes. These simulations provide ground truth labels and training data that replicate real-world physics without the computational burden of processing actual sensor data, enabling efficient learning of physical system properties through replicated environments
3Measurement precision
If the model is trained on one specific physical system, then it achieves high accuracy for that system, but it cannot generalize to other physical systems without retraining
Solution Approach 1:
The patent designs a universal graph neural network architecture that can represent multiple types of physical systems through a common framework. The graph structure and neural network layers are designed to be agnostic to specific physical domains, allowing the same model to handle different bodies, joints, and physical interactions by simply changing the graph connectivity and initial embeddings, thereby achieving both high system-specific accuracy and broad generalization without retraining
Solution Approach 2:
The patent leverages parameter changes in the graph structure and neural network weights to adapt the model to different physical systems. By modifying the graph connectivity matrix and initial node embeddings to match the target system's topology and physics, the model can accurately represent new physical systems using the same learned representations and architectural parameters, enabling zero-shot or few-shot generalization
Data Source
AI summary
A graph neural network system for processing data representing a physical system is disclosed. The graph neural network system receives physical system data and action data, where the physical system includes bodies coupled by joints. The graph neural network system processes an input graph to provide an output graph, the input and output graphs each having a graph structure including nodes and edges corresponding, respectively, to the bodies and joints of the physical system. The graph processing neural network provides the inferred static property or dynamic state of the physical system. The graph processing neural network is differentiable and may be used for control and/or reinforcement learning.