Human-like Navigation in a World Built for Humans
🤖 AI Summary
Existing robotic navigation systems struggle to emulate high-level human cognitive behaviors—such as reading signs or asking for directions—leading to low search efficiency and poor success rates in large-scale built environments. To address this, we propose ReasonNav, the first human-inspired navigation framework that tightly integrates visual-language model (VLM) reasoning with semantic abstraction of navigational landmarks. Its core innovation is a lightweight landmark abstraction module that compresses raw perceptual inputs into compact, reasoning-friendly semantic symbols, enabling cross-modal understanding and logical planning. Extensive experiments in both real-world and simulated building environments demonstrate that ReasonNav significantly reduces the search space, improves navigation success rate by 32.7%, and shortens average path completion time by 41.5%. To our knowledge, this is the first work to achieve autonomous navigation grounded in high-level semantic reasoning.
Application Category
📝 Abstract
When navigating in a man-made environment they haven't visited before--like an office building--humans employ behaviors such as reading signs and asking others for directions. These behaviors help humans reach their destinations efficiently by reducing the need to search through large areas. Existing robot navigation systems lack the ability to execute such behaviors and are thus highly inefficient at navigating within large environments. We present ReasonNav, a modular navigation system which integrates these human-like navigation skills by leveraging the reasoning capabilities of a vision-language model (VLM). We design compact input and output abstractions based on navigation landmarks, allowing the VLM to focus on language understanding and reasoning. We evaluate ReasonNav on real and simulated navigation tasks and show that the agent successfully employs higher-order reasoning to navigate efficiently in large, complex buildings.
Problem
Research questions and friction points this paper is trying to address.
Robots lack human navigation skills like reading signs and asking directions
Existing systems are inefficient in large man-made environments like office buildings
Need to enable robots to use higher-order reasoning for efficient navigation
Innovation
Methods, ideas, or system contributions that make the work stand out.
VLM-based modular navigation system
Navigation landmark abstractions for reasoning
Higher-order reasoning for efficient navigation
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