{
  "abstract": "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.",
  "assignees": [
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  "authors": [
    "HE BIN",
    "JIANG SHUO",
    "LIU JIAHANG",
    "LI ZIHAN",
    "WANG ZHIPENG",
    "ZHOU YANMIN"
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  "title": "Methods for tokenization representation and learning of robotic perception data based on graph neural network",
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