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.
Ruan et al. (Sat,) studied this question.