From reactive to cognitive: brain-inspired spatial intelligence for
  embodied agents
Spatial cognition enables adaptive goal-directed behavior by constructing
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 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.
68b6af405aa065c9126126e0
Shouwei Ruan
68b6af405aa065c9126126e1
Liyuan Wang
68b6af405aa065c9126126e2
Caixin Kang
68b6af405aa065c9126126e3
Qihui Zhu
68b6af405aa065c9126126e4
Songming Liu
68b6af405aa065c9126126e5
Xingxing Wei
68b6af405aa065c9126126e6
Hang Su
From reactive to cognitive: brain-inspired spatial intelligence for
  embodied agents
Spatial cognition enables adaptive goal-directed behavior by constructing
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 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.
BSC-Nav constructs allocentric cognitive maps from egocentric trajectories and contextual cues, enabling embodied agents to perform diverse navigation tasks with zero-shot generalization and versatile behaviors.
allocentric cognitive maps egocentric trajectories contextual cues structured spatial memory zero-shot generalization embodied behaviors spatial intelligence