<p>Animals navigate unfamiliar environments for hours on minimal energy, yet current robotic navigation systems — whether model-based or data-driven — struggle to generalize, operate within tight energy budgets or respond rapidly enough to changing conditions. These shortcomings reflect a fundamental absence: robots lack the integrated cognitive architecture that underpins biological navigation. This Review examines how biological principles can close this gap, showing that cognitive maps, adaptive memory and hierarchical planning translate into robotic architectures capable of flexible, energy-efficient navigation, and evaluates real-world deployment, including low-power neuromorphic implementations. Bridging biology and engineering will require advances in spatial representation, knowledge use and spatial reasoning, together with sustained interdisciplinary collaboration.</p>

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Bio-inspired cognitive navigation for robots

  • Zhaotie Hao,
  • Bin Guo,
  • Yasan Ding,
  • Linwei Li,
  • Shiqi Liu,
  • Tingting Gao,
  • Sicong Liu,
  • Zhiwen Yu

摘要

Animals navigate unfamiliar environments for hours on minimal energy, yet current robotic navigation systems — whether model-based or data-driven — struggle to generalize, operate within tight energy budgets or respond rapidly enough to changing conditions. These shortcomings reflect a fundamental absence: robots lack the integrated cognitive architecture that underpins biological navigation. This Review examines how biological principles can close this gap, showing that cognitive maps, adaptive memory and hierarchical planning translate into robotic architectures capable of flexible, energy-efficient navigation, and evaluates real-world deployment, including low-power neuromorphic implementations. Bridging biology and engineering will require advances in spatial representation, knowledge use and spatial reasoning, together with sustained interdisciplinary collaboration.