{"ID":2878817,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17198","arxiv_id":"2508.17198","title":"From reactive to cognitive: brain-inspired spatial intelligence for embodied agents","abstract":"Spatial cognition enables adaptive goal-directed behavior by constructing internal models of space. Robust biological systems consolidate spatial knowledge into three interconnected forms: \\textit{landmarks} for salient cues, \\textit{route knowledge} for movement trajectories, and \\textit{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 navigation tasks, demonstrates strong zero-shot generalization, and supports versatile embodied behaviors in the real physical world, offering a scalable and biologically grounded path toward general-purpose spatial intelligence.","short_abstract":"Spatial cognition enables adaptive goal-directed behavior by constructing internal models of space. Robust biological systems consolidate spatial knowledge into three interconnected forms: \\textit{landmarks} for salient cues, \\textit{route knowledge} for movement trajectories, and \\textit{survey knowledge} for map-like...","url_abs":"https://arxiv.org/abs/2508.17198","url_pdf":"https://arxiv.org/pdf/2508.17198v1","authors":"[\"Shouwei Ruan\",\"Liyuan Wang\",\"Caixin Kang\",\"Qihui Zhu\",\"Songming Liu\",\"Xingxing Wei\",\"Hang Su\"]","published":"2025-08-24T03:20:48Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}