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<p>Spatial cognition enables adaptive goal-directed behavior through structured internal models of space. Robust biological systems consolidate spatial knowledge into three interconnected forms: landmarks for salient cues, route knowledge for movement trajectories, and survey knowledge for map-like representations. While recent advances in multi-modal large language models (MLLMs) have enabled visual-language reasoning in embodied agents, these efforts lack structured spatial memory and instead operate reactively, limiting their generalization and adaptability in complex real-world environments. Here we present Brain-inspired Spatial Cognition for Navigation (BSC-Nav), a unified framework for constructing and leveraging structured spatial memory in embodied agents. BSC-Nav builds allocentric cognitive maps from egocentric trajectories and contextual cues, and dynamically retrieves spatial knowledge aligned with semantic goals. Integrated with powerful MLLMs, BSC-Nav achieves state-of-the-art efficacy and efficiency across diverse embodied navigation tasks (e.g., improving success weighted by path length from 17.6% to 44.9% in instance-level navigation and from 42.7% to 53.1% in zero-shot long-horizon instruction-following), while also supporting versatile embodied behaviors in the real physical world. These results highlight a scalable path toward general-purpose spatial intelligence.</p>

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Brain-inspired spatial intelligence for embodied agents

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月27日

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  • Shouwei Ruan,
  • Liyuan Wang,
  • Caixin Kang,
  • Qihui Zhu,
  • Songming Liu,
  • Xingxing Wei,
  • Hang Su

摘要

Spatial cognition enables adaptive goal-directed behavior through structured internal models of space. Robust biological systems consolidate spatial knowledge into three interconnected forms: landmarks for salient cues, route knowledge for movement trajectories, and survey knowledge for map-like representations. While recent advances in multi-modal large language models (MLLMs) have enabled visual-language reasoning in embodied agents, these efforts lack structured spatial memory and instead operate reactively, limiting their generalization and adaptability in complex real-world environments. Here we present Brain-inspired Spatial Cognition for Navigation (BSC-Nav), a unified framework for constructing and leveraging structured spatial memory in embodied agents. BSC-Nav builds allocentric cognitive maps from egocentric trajectories and contextual cues, and dynamically retrieves spatial knowledge aligned with semantic goals. Integrated with powerful MLLMs, BSC-Nav achieves state-of-the-art efficacy and efficiency across diverse embodied navigation tasks (e.g., improving success weighted by path length from 17.6% to 44.9% in instance-level navigation and from 42.7% to 53.1% in zero-shot long-horizon instruction-following), while also supporting versatile embodied behaviors in the real physical world. These results highlight a scalable path toward general-purpose spatial intelligence.