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论文arXiv 2610.088003 个月前

基于因子图和场景图的动态环境机器人世界模型综述 ​

该综述面向动态环境中静态图式世界模型受限问题，梳理因子图与场景图表示、更新管线及下游应用。

AI 整理
  • 数据与采集
  • 具身模型
  • 评测与安全

原文链接：A Review Of Robotic World Models For Dynamic Environments Based On Factor And Scene Graphs（arXiv 2610.08800 · 发布日期：2026-07-27）

A Review Of Robotic World Models For Dynamic Environments Based On Factor And Scene Graphs

该综述面向动态环境中静态图式世界模型受限问题，梳理因子图与场景图表示、更新管线及下游应用。

核心要点 ​

  • 动机在于真实机器人环境常含动态因素，纯静态世界表示面临严重挑战。
  • 文章围绕表示、构建与更新管线、下游任务利用三方面组织评述。
  • 重点分析因子图、场景图及二者结合的混合模型如何建模不同动态类型。
  • 总结新兴趋势与开放挑战，包括不确定性传播、可观测性、终身维护和评测缺乏。

英文摘要 (Abstract) ​

Models based on graphs have emerged in robotics as a powerful foundation for internal world representations, where factor and scene graphs are among the most prominent model types found in the related literature and in successful robotic solutions. Initially, many of these models were assuming static environments as a simplification. Herein, factor graphs mainly provide uncertainty-aware geometric estimations while scene graphs enable a structured semantic abstraction. However, real-world robotic environments are often dynamic, posing severe challenges for purely static world representations. Therefore, this review presents a comprehensive view on how dynamic aspects of real-world environments can be addressed in such graph-based world models. We organize our assessments around three main aspects: (I) suitable representations, (II) pipelines to construct and update the representations, and (III) their exploitation for downstream tasks. We review approaches that are either based on factor or scene graphs, but put special emphasis on novel approaches that combine both types to form hybrid models. We mainly analyze how different types of dynamics can be modeled herein, and categorize common architectural patterns. Finally, emerging trends and open challenges are identified, including uncertainty propagation from learned perception through the representation layers, the observability of dynamic-entity motion and scale under minimal sensing, scalable lifelong maintenance, and the lack of datasets and evaluation protocols that ground world-model quality in downstream task performance under dynamics.

作者：Marco Giberna, Miguel Fernández-Cortizas, José Luis Sánchez-López, Holger Voos

来源

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  1. arXiv 2610.08800：A Review Of Robotic World Models For Dynamic Environments Based On Factor And Scene Graphs访问 2026-10-21