Instruction-as-State: Environment-Guided and State-Conditioned Semantic Understanding for Embodied Navigation

TL;DR

This paper introduces a dynamic instruction understanding framework for embodied navigation, modeling instructions as evolving states conditioned on the agent's perceptual context to improve navigation performance.

Contribution

It proposes the S-EGIU framework that dynamically updates instruction semantics based on visual context, enhancing adaptability over static instruction encoding methods.

Findings

Achieves +2.68% SPL gain on REVERIE Test Unseen

Demonstrates consistent efficiency improvements across VLN benchmarks

Models instruction understanding as a step-by-step evolving state

Abstract

Vision-and-Language Navigation requires agents to follow natural-language instructions in visually changing environments. A central challenge is the dynamic entanglement between language and observations: the meaning of instruction shifts as the agent's field of view and spatial context evolve. However, many existing models encode the instruction as a static global representation, limiting their ability to adapt instruction meaning to the current visual context. We therefore model instruction understanding as an Instruction-as-State variable: a decision-relevant, token-level instruction state that evolves step by step conditioned on the agent's perceptual state, where the perceptual state denotes the observation-grounded navigation context at each step. To realize this principle, we introduce State-Entangled Environment-Guided Instruction Understanding (S-EGIU), a coarse-to-fine framework for state-conditioned segment activation and token-level semantic refinement. At the coarse level, S-EGIU activates the instruction segment whose semantics align with the current observation. At the fine level, it refines the activated segment through observation-guided token grounding and contextual modeling, sharpening its internal semantics under the current observation. Together, these stages maintain an instruction state that is continuously updated according to the agent's perceptual state during navigation. S-EGIU delivers strong performance on several key metrics, including a +2.68% SPL gain on REVERIE Test Unseen, and demonstrates consistent efficiency gains across multiple VLN benchmarks, underscoring the value of dynamic instruction--perception entanglement.