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ARXIV:2603.12696 · VISION-LANGUAGE NAVIGATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.12696VISION-LANGUAGE NAVIGATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
HaltNav enhances vision-language navigation by integrating lightweight topological maps with reactive visual halting for robust real-world performance.
Opportunity summary
Pain HaltNav enhances vision-language navigation by integrating lightweight topological maps with reactive visual halting for robust real-world performance.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
HaltNav enhances vision-language navigation by integrating lightweight topological maps with reactive visual halting for robust real-world performance. Achieving this transition without exhaustive routing prompts requires agents to leverage structural priors.
Vision-and-Language Navigation (VLN) is shifting from rigid, step-by-step instruction following toward open-vocabulary, goal-oriented autonomy. Achieving this transition without exhaustive routing prompts requires agents to leverage structural priors.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments demonstrate that our hierarchical framework outperforms several baseline methods without tedious language instructions, and significantly improves robustness for long-horizon vision-language navigation under…
Vision-Language Navigation moved forward this cycle; last verified April 2026. Public score 7.0/10.
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HaltNav enhances vision-language navigation by integrating lightweight topological maps with reactive visual halting for robust real-world performance.
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Paper Pack
10.48550/arXiv.2603.12696HaltNav enhances vision-language navigation by integrating lightweight topological maps with reactive visual halting for robust real-world performance.
Abstract
Vision-and-Language Navigation (VLN) is shifting from rigid, step-by-step instruction following toward open-vocabulary, goal-oriented autonomy. Achieving this transition without exhaustive routing prompts requires agents to leverage structural priors. While prior work often assumes computationally heavy 2D/3D metric maps, we instead exploit a lightweight, text-based osmAG (OpenStreetMap Area Graph), a floorplan-level topological representation that is easy to obtain and maintain. However, global planning over a prior map alone is brittle in real-world deployments, where local connectivity can change (e.g., closed doors or crowded passages), leading to execution-time failures. To address this gap, we propose a hierarchical navigation framework HaltNav that couples the robust global planning of osmAG with the local exploration and instruction-grounding capability of VLN. Our approach features an MLLM-based brain module, which is capable of high-level task grounding and obstruction awareness. Conditioned on osmAG, the brain converts the global route into a sequence of localized execution snippets, providing the VLN executor with prior-grounded, goal-centric sub-instructions. Meanwhile, it detects local anomalies via a mechanism we term Reactive Visual Halting (RVH), which interrupts the local control loop, updates osmAG by invalidating the corresponding topology, and triggers replanning to orchestrate a viable detour. To train this halting capability efficiently, we introduce a data synthesis pipeline that leverages generative models to inject realistic obstacles into otherwise navigable scenes, substantially enriching hard negative samples. Extensive experiments demonstrate that our hierarchical framework outperforms several baseline methods without tedious language instructions, and significantly improves robustness for long-horizon vision-language navigation under environmental changes.
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What was readable
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Dimensions overall score 7.0
PROBLEM
HaltNav enhances vision-language navigation by integrating lightweight topological maps with reactive visual halting for robust real-world performance. Achieving this transition without exhaustive routing prompts requires agents to leverage structural priors.
METHOD
Vision-and-Language Navigation (VLN) is shifting from rigid, step-by-step instruction following toward open-vocabulary, goal-oriented autonomy. Achieving this transition without exhaustive routing prompts requires agents to leverage structural priors.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments demonstrate that our hierarchical framework outperforms several baseline methods without tedious language instructions, and significantly improves robustness for long-horizon vision-...
WHY NOW
Vision-Language Navigation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
HaltNav enhances vision-language navigation by integrating lightweight topological maps with reactive visual halting for robust real-world performance. Achieving this transition without exhaustive routing prompts requires agents to leverage structural priors.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision-and-Language Navigation (VLN) is shifting from rigid, step-by-step instruction following toward open-vocabulary, goal-oriented autonomy. Achieving this transition without exhaustive routing prompts requires agents to leverage structural priors.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments demonstrate that our hierarchical framework outperforms several baseline methods without tedious language instructions, and significantly improves robustness for long-horizon vision-language navigation under environmental changes.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision-Language Navigation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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HaltNav enhances vision-language navigation by integrating lightweight topological maps with reactive visual halting for robust real-world performance.
Segment
Vision-Language Navigation
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Commercial read
7.0/10 public viability
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