Graph Tokenization for Robotic Perception Data Encoding
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Solution Overview
Problem
Existing robotic systems struggle to effectively convert complex robotic perception data into high-dimensional vectors that preserve interrelationships, limiting their decision-making and control capabilities in dynamic environments.
Innovation Solution
A method using a graph neural network (GNN) for token-based representation and learning of robotic perception data, involving token-based representation, construction of an initial feature graph, learning a compact representation with an autoencoder, and reconstructing the graph structure to convert perception data into high-dimensional feature vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fixed data structures and simple feature extraction techniques are used for processing robotic perception data, then the processing method is simple and easy to implement, but the robot's ability to understand complex environments and execute tasks flexibly is limited
Solution Approach 1:
The patent transforms robotic perception data from traditional fixed numerical arrays into graph-structured data with nodes and edges, adding a topological dimension that captures spatial and relational information. This dimensional transformation enables the robot to understand complex environments by preserving structural relationships while maintaining computational tractability through graph neural network processing.
Solution Approach 2:
The patent changes the fundamental parameters of data representation by introducing graph-structured tokens with node features, edge attributes, and hierarchical relationships. This parameter transformation from simple numerical values to structured graph objects enables flexible task execution while the modular graph neural network architecture keeps the processing method manageable despite increased complexity.
2Adaptability or versatility
If large pre-trained models are applied to robotic decision control, then the model's capability to handle complex tasks is improved, but the requirement for tight coupling with embodied perceptual inputs increases
Solution Approach 1:
The patent introduces graph-structured perception data as an intermediary layer between raw sensor inputs and the large pre-trained model. This intermediary representation with its hierarchical token structure and relational graph format serves as a bridge that translates diverse embodied perceptual inputs into a unified format suitable for large model processing, reducing the direct coupling complexity while maintaining task handling capability.
3Loss of information
If simple approaches such as directly using raw numerical values or basic feature engineering are employed for processing robotic perception data, then the processing method is straightforward, but the complexity and multi-dimensional nature of the perception data cannot be adequately expressed
Solution Approach 1:
The patent segments perception data into discrete graph tokens representing different spatial locations, objects, or features, with each token containing node attributes and relational information. This segmentation preserves the multi-dimensional nature of perception data by maintaining local details in individual tokens while the graph structure captures global relationships, avoiding information loss without requiring overly complex processing methods.
Data Source
AI summary
Provided is a method for token-based representation and learning of robotic perception data based on a graph neural network, comprising: obtaining a plurality of types of perception data of a robot; performing token-based representation according to types of the plurality of types of perception data; constructing an initial feature graph based on the plurality of types of perception data after the token-based representation; learning a compact representation of the initial feature graph based on an autoencoder and reconstructing a graph structure; after the autoencoder completes learning of the graph structure, fixing the graph structure; and converting the plurality of types of perception data into node feature vectors, constructing a feature graph based on the graph structure, and performing numerical encoding on each of the node feature vectors by utilizing the graph neural network to obtain a representation of high-dimensional feature vectors of the plurality of types of perception data.