pith:4VEGZFNT
A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration
A modular vision-language navigation system separates sensing from reasoning to run efficiently on real robots.
arxiv:2604.21363 v2 · 2026-04-23 · cs.RO
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\pithnumber{4VEGZFNTOR2UMZCWP5XPK6BUVQ}
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Record completeness
Claims
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.
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.
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.
Receipt and verification
| First computed | 2026-05-20T00:03:12.289492Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
e5486c95b374754664567f6ef57834ac0ffa1198553b2870800eb355074947ca
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/4VEGZFNTOR2UMZCWP5XPK6BUVQ \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e5486c95b374754664567f6ef57834ac0ffa1198553b2870800eb355074947ca
Canonical record JSON
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