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<p>The Moonshot R&amp;D project, “Self-evolving AI robot system for lunar exploration and human outpost construction,” aims to realize AI modular robots that integrate advanced physical capabilities with self-evolving AI learning. Modular robots face significant challenges in achieving optimal and rapid shape transitions due to the vast number of possible configurations. To tackle this challenge, we first developed a Graph Neural Network (GNN) designed to learn abstract structural features inherent to modular robots—such as functional equivalence and geometric symmetry. To enable effective training of such a GNN, we developed a novel isomorphism determination method called Canonical Robot Adaptive Graph Encode (CRAGE), which converts structural graphs into canonical strings for efficient and consistent comparison. This graph-to-string encoding makes it possible to identify structurally equivalent configurations with high accuracy and speed. Moreover, CRAGE allows for the rapid and scalable generation of high-quality training data for robot structures, isomorphism classification, and deformation path planning—providing essential input for the GNN. Experiments show that CRAGE achieves 100% accuracy in small- to medium-scale structures while reducing processing time by over 90% compared to conventional methods. GNNs trained on CRAGE-generated data accurately predicted Lv2 structural equivalence from Lv1 inputs, achieving up to 100% classification accuracy and demonstrating strong generalization across structural variations. Together, CRAGE and the GNN form a unified framework for fast and scalable transition planning, contributing to the realization of autonomous self-evolving modular robots.</p>

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CRAGE and GNN-based AI architecture for autonomous transitions in modular robots

  • 期刊论文

  • 发表日期：

    2025年12月10日

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  • Kenichiro Satonaka,
  • Ryusei Nishii,
  • Ryota Kinjo,
  • Seiichi Ohashi,
  • Tomoya Negoro,
  • Yuki Takagi,
  • Hiroshi Oku,
  • Yuki Tanigaki,
  • Koki Harada,
  • Ryo Ariizumi,
  • Tomohiro Simomura,
  • Guang Yang,
  • Xixun Wang,
  • Fumitoshi Matsuno

摘要

The Moonshot R&D project, “Self-evolving AI robot system for lunar exploration and human outpost construction,” aims to realize AI modular robots that integrate advanced physical capabilities with self-evolving AI learning. Modular robots face significant challenges in achieving optimal and rapid shape transitions due to the vast number of possible configurations. To tackle this challenge, we first developed a Graph Neural Network (GNN) designed to learn abstract structural features inherent to modular robots—such as functional equivalence and geometric symmetry. To enable effective training of such a GNN, we developed a novel isomorphism determination method called Canonical Robot Adaptive Graph Encode (CRAGE), which converts structural graphs into canonical strings for efficient and consistent comparison. This graph-to-string encoding makes it possible to identify structurally equivalent configurations with high accuracy and speed. Moreover, CRAGE allows for the rapid and scalable generation of high-quality training data for robot structures, isomorphism classification, and deformation path planning—providing essential input for the GNN. Experiments show that CRAGE achieves 100% accuracy in small- to medium-scale structures while reducing processing time by over 90% compared to conventional methods. GNNs trained on CRAGE-generated data accurately predicted Lv2 structural equivalence from Lv1 inputs, achieving up to 100% classification accuracy and demonstrating strong generalization across structural variations. Together, CRAGE and the GNN form a unified framework for fast and scalable transition planning, contributing to the realization of autonomous self-evolving modular robots.