Rethinking Embodied Navigation via Relational Inductive Bias

• URL: http://arxiv.org/abs/2606.10348v1
• Date: Tue, 09 Jun 2026 02:57:34 GMT
• Title: Rethinking Embodied Navigation via Relational Inductive Bias
• Abstract summary: We propose DB-Nav, a framework that reshapes the search space via dual relational biases.<n>It factorizes target-centric relations into an Activation Bias and an Inhibition Bias.<n>It significantly outperforms existing methods in success rate (SR) and Success weighted by Path Length (SPL)
• Score: 53.72276435022479
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object navigation requires an agent to locate a target in an unknown environment through visual observations. Existing methods typically rely on open-vocabulary detectors or vision-language models (VLMs) to answer where to search, but often overlook what not to trust - which semantic cues are unreliable. Open-vocabulary perception is prone to systematic misleading evidence: false positives, outdated static priors, and repeated failed exploration due to lack of embodied verification, which contaminates mapping and decision-making. Such errors are rooted in structured object relations in real-world scenes. To address this, we propose DB-Nav, a framework that reshapes the search space via dual relational biases. It factorizes target-centric relations into an Activation Bias (propagates contextual evidence) and an Inhibition Bias (suppresses unreliable regions via perceptual confusion and action-level falsification). These biases are unified into a Relational Activation-Inhibition Exploration Graph that modulates frontier exploration values using online observations and failed accesses. Experiments on ObjectNav benchmarks show that DB-Nav significantly outperforms existing methods in success rate (SR) and Success weighted by Path Length (SPL), offering a lightweight, interpretable, and robust navigation framework without costly online VLM reasoning.

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