{"paper":{"title":"A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A modular vision-language navigation system separates sensing from reasoning to run efficiently on real robots.","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chen Wang, Denan Liang, Kuan Xu, Lihua Xie, Ruimeng Liu, Shenghai Yuan, Tongxing Jin, Yizhuo Yang","submitted_at":"2026-04-23T07:27:00Z","abstract_excerpt":"Bridging the gap between embodied intelligence and embedded deployment remains a key challenge in intelligent robotic systems, where perception, reasoning, and planning must operate under strict constraints on computation, memory, energy, and real-time execution. In vision-and-language navigation (VLN), existing approaches often face a trade-off between reasoning capability and deployment efficiency on real-world platforms. In this paper, we present a deployable embodied VLN system that achieves both high efficiency and strong high-level reasoning on real-world robots. The system is decomposed"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Extensive experiments in both simulation and real-world robotic platforms demonstrate improved navigation success and efficiency over existing VLN approaches, while maintaining real-time performance on resource-constrained hardware.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"Decoupling the system into asynchronous modules and decomposing the cognitive memory graph into subgraphs for VLM reasoning will preserve necessary information and avoid latency that harms performance in changing real-world environments.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A modular VLN architecture builds a cognitive memory graph, decomposes it for VLM reasoning, and solves a weighted traveling repairman problem for context-aware exploration to achieve real-time performance and higher success on embedded hardware.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A modular vision-language navigation system separates sensing from reasoning to run efficiently on real robots.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"ce199f84a74a9bc3577c8e6fd78f32004ef45947805319b353887bc9775bc5a0"},"source":{"id":"2604.21363","kind":"arxiv","version":2},"verdict":{"id":"089e1121-538f-4678-b6d7-693f76a2d63a","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-09T22:06:31.798863Z","strongest_claim":"Extensive experiments in both simulation and real-world robotic platforms demonstrate improved navigation success and efficiency over existing VLN approaches, while maintaining real-time performance on resource-constrained hardware.","one_line_summary":"A modular VLN architecture builds a cognitive memory graph, decomposes it for VLM reasoning, and solves a weighted traveling repairman problem for context-aware exploration to achieve real-time performance and higher success on embedded hardware.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"Decoupling the system into asynchronous modules and decomposing the cognitive memory graph into subgraphs for VLM reasoning will preserve necessary information and avoid latency that harms performance in changing real-world environments.","pith_extraction_headline":"A modular vision-language navigation system separates sensing from reasoning to run efficiently on real robots."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.21363/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}