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This is a record of a document published elsewhere, first published by the United States Patent and Trademark Office, as US-2026091503-A1, on 2 April 2026. OpenTechnical did not first disclose it. The identifier, hashes and timestamps below record what this library served and when, not when the material became public. View the original.

OT-2026-000001196

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

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Permanent URL
Classification
Robotics & Automation › Robot Architectures
Inventors
HE BIN, JIANG SHUO, LIU JIAHANG, LI ZIHAN, WANG ZHIPENG, ZHOU YANMIN
Assignee
UNIV TONGJI
Original source
US-2026091503-A1, the United States Patent and Trademark Office, 2 April 2026
Document SHA-256
af27692f6c146934859f0a44589765ebb76b889f10b76888711a05cee04ac1d5

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.

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Citation

OpenTechnical Publication OT-2026-000001196, “Methods for tokenization representation and learning of robotic perception data based on graph neural network,” published September 15, 2026 at 19:04:03 UTC, https://opentechnical.org/p/OT-2026-000001196.

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