Spatio-Temporal Graph Message Passing for Dynamic Sensor Data
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Solution Overview
Problem
Existing spatio-temporal graph neural networks face challenges in handling dynamic graphs with varying node and edge structures over time, particularly in integrating multi-modal data like visual and tactile data for tasks such as human pose estimation and robot navigation, due to inconsistencies in node locations and sensor modality constraints.
Innovation Solution
A system and method for spatio-temporal graph message passing that generates edges based on proximity, using Euclidean or embedding space distances, performs message passing across nodes, and incorporates multi-layer perceptron functions to generate updated feature vectors, while utilizing temporal edge generation techniques and objective functions to maintain temporal consistency and smoothness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dynamic graph structures with varying nodes and edges are used to model spatio-temporal data, then the system can accommodate changing sensor inputs and temporal relationships, but the complexity of graph construction and message passing increases significantly
Solution Approach 1:
The patent implements dynamic graph structures where nodes and edges are created, modified, or removed based on temporal relationships and sensor data characteristics. The graph structure adapts over time to reflect changing spatial and temporal relationships in the data, allowing the system to handle varying input modalities and temporal patterns while maintaining computational tractability through structured update rules.
Solution Approach 2:
The patent segments the spatio-temporal data processing into discrete temporal steps and spatial regions, creating a modular graph structure where message passing occurs in structured phases. This segmentation allows the complex problem to be broken down into manageable computational units that can be processed sequentially, reducing overall system complexity.
2Loss of information
If multi-modal data from different sensor types are integrated into the graph, then the representation capability for complex tasks is enhanced, but the difficulty of handling inconsistent node locations and modality constraints increases
Solution Approach 1:
The patent introduces embedding spaces as intermediary representations that map different sensor modalities into a unified feature space. Nodes from different sensor types (visual, tactile, proprioceptive) are projected into this shared embedding space, allowing consistent message passing and comparison across modalities while preserving modality-specific characteristics through learned embedding transformations.
Solution Approach 2:
The patent transforms physical sensor coordinates into embedding space representations, changing the parameter space from raw sensor coordinates to learned feature embeddings. This parameter transformation allows nodes from different modalities to be compared and integrated consistently by operating in the transformed embedding space rather than disparate physical coordinate systems.
3Stability of the object's composition
If temporal edge generation techniques are used to maintain temporal consistency, then the smoothness of spatio-temporal representations is improved, but the computational overhead for edge generation and update increases
Solution Approach 1:
The patent implements periodic message passing and edge update operations at fixed temporal intervals rather than continuously. Graph edges and node features are updated at discrete time steps, with message passing occurring periodically between temporal layers. This periodic approach maintains temporal consistency while reducing computational overhead compared to continuous updates.
Data Source
AI summary
According to one aspect, spatio-temporal graph message passing may include generating edges for a spatio-temporal graph. Nodes for the spatio-temporal graph may be defined by a first point cloud and a second point cloud. The edges may be generated based on a proximity between nodes of the spatio-temporal graph. The proximity may be defined based on a Euclidean distance or an embedding space distance. Message passing may be performed between respective nodes based on the proximity to generate updated feature vectors for respective nodes and a graph readout may be generated based on the updated feature vectors. Additionally, a downstream task may be performed based on the graph readout.