---
publication_id: "OT-2026-000001196"
title: "Methods for tokenization representation and learning of robotic perception data based on graph neural network"
published_at: "2026-09-15T19:04:03.936384Z"
canonical_url: "https://opentechnical.org/p/OT-2026-000001196"
categories:
  - "robotics-automation/robot-architectures"
keywords:
  - "b25j9/1697"
  - "b25j9/1697"
  - "g06f40/289"
  - "b25j9/16"
  - "b25j9/161"
  - "b25j9/161"
---

# Methods for tokenization representation and learning of robotic perception data based on graph neural network

## 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.