General-Purpose Robotic Navigation via LVLM-Orchestrated Perception, Reasoning, and Acting

• URL: http://arxiv.org/abs/2506.17462v2
• Date: Fri, 17 Oct 2025 03:19:22 GMT
• Title: General-Purpose Robotic Navigation via LVLM-Orchestrated Perception, Reasoning, and Acting
• Authors: Bernard Lange, Anil Yildiz, Mansur Arief, Shehryar Khattak, Mykel Kochenderfer, Georgios Georgakis,
• Abstract summary: Agentic Robotic Navigation Architecture (ARNA) is a general-purpose framework that equips an LVLM-based agent with a library of perception, reasoning, and navigation tools.<n>At runtime, the agent autonomously defines and executes task-specific navigation tools drawn from modern robotic stacks.<n>ARNA outperforms state-of-the-art EQA-specific approaches.
• Score: 5.291702442384798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing general-purpose navigation policies for unknown environments remains a core challenge in robotics. Most existing systems rely on task-specific neural networks and fixed information flows, limiting their generalizability. Large Vision-Language Models (LVLMs) offer a promising alternative by embedding human-like knowledge for reasoning and planning, but prior LVLM-robot integrations have largely depended on pre-mapped spaces, hard-coded representations, and rigid control logic. We introduce the Agentic Robotic Navigation Architecture (ARNA), a general-purpose framework that equips an LVLM-based agent with a library of perception, reasoning, and navigation tools drawn from modern robotic stacks. At runtime, the agent autonomously defines and executes task-specific workflows that iteratively query modules, reason over multimodal inputs, and select navigation actions. This agentic formulation enables robust navigation and reasoning in previously unmapped environments, offering a new perspective on robotic stack design. Evaluated in Habitat Lab on the HM-EQA benchmark, ARNA outperforms state-of-the-art EQA-specific approaches. Qualitative results on RxR and custom tasks further demonstrate its ability to generalize across a broad range of navigation challenges.

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