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pith:KJDRCWTR

pith:2025:KJDRCWTRHYDFH6Y6X67JCM2MFN

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Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey

Lingsen Zhang, Liqiang Nie, Ran Chen, Renshan Zhang, Rui Shao, Wei Li, Zhiyang Liu

Large VLM-based VLA models for robotic manipulation can be systematically classified into monolithic and hierarchical architectures.

arxiv:2508.13073 v2 · 2025-08-18 · cs.RO · cs.CV

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Claims

C1strongest claim

This survey provides the first systematic, taxonomy-oriented review of large VLM-based VLA models for robotic manipulation, resolving inconsistencies in existing taxonomies and filling a critical gap.

C2weakest assumption

That the proposed split into monolithic (single/dual-system) and hierarchical models, along with the listed integration domains, comprehensively captures the field without significant omissions or overlaps that would undermine the taxonomy's utility.

C3one line summary

This survey organizes large VLM-based VLA models for robotic manipulation into monolithic and hierarchical paradigms, reviews their integrations and datasets, and outlines future directions.

References

243 extracted · 243 resolved · 28 Pith anchors

[1] //arxiv.org/abs/2402.02385 2024

[2] A Survey on Vision-Language-Action Models for Embodied AI 2024 · arXiv:2405.14093

[3] Metaurban: a simulation platform for embodied ai in urban spaces, 2025

[4] Generative artificial intelligence in robotic manipulation: A survey 2025

[5] Aligning cyber space with physical world: A comprehensive survey on embodied AI 2024

Cited by

19 papers in Pith

RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions

FASTER: Rethinking Real-Time Flow VLAs

RoboEvolve: Co-Evolving Planner-Simulator for Robotic Manipulation with Limited Data

From Video to Control: A Survey of Learning Manipulation Interfaces from Temporal Visual Data

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First computed	2026-05-17T23:38:13.173723Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5247115a713e0653fb1ebfbe91334c2b53750093a87c76fe467a5744a7e3bb46

Aliases

arxiv: 2508.13073 · arxiv_version: 2508.13073v2 · doi: 10.48550/arxiv.2508.13073 · pith_short_12: KJDRCWTRHYDF · pith_short_16: KJDRCWTRHYDFH6Y6 · pith_short_8: KJDRCWTR

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/KJDRCWTRHYDFH6Y6X67JCM2MFN \
  | 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: 5247115a713e0653fb1ebfbe91334c2b53750093a87c76fe467a5744a7e3bb46

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "ca67b0f9cf1a480f3dad4c5e03c723c6e779cd4fad89a3bf2e5d3abbaf71aebb",
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      "cs.CV"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2025-08-18T16:45:48Z",
    "title_canon_sha256": "5c704c940d76581b6834b4f713d011330cc8435255f2c993a4f040337734f580"
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