Article(id=1322960879691637014, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322960856543273127, articleNumber=null, orderNo=null, doi=10.11834/jig.250544, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1763308800000, receivedDateStr=2025-11-17, revisedDate=1775145600000, revisedDateStr=2026-04-03, acceptedDate=null, acceptedDateStr=null, onlineDate=1793348053142, onlineDateStr=2026-10-30, pubDate=1786809600000, pubDateStr=2026-08-16, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793348053142, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793348053142, creator=13701087609, updateTime=1793348053142, updator=13701087609, issue=Issue{id=1322960856543273127, tenantId=1146029695717560320, journalId=1249024232475115590, year='2026', volume='31', issue='8', pageStart='2687', pageEnd='3074', issueExtLink='null', onlineDate='null', pubDate='1786809600000', pubDateStr='2026-08-16', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793348047624, creator='13701087609', updateTime=1793348605344, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322963195874378645, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322960856543273127, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322963195874378646, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322960856543273127, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2731, endPage=2773, ext={EN=ArticleExt(id=1322960881369358616, articleId=1322960879691637014, tenantId=1146029695717560320, journalId=1249024232475115590, language=EN, title=Survey on embodied intelligence： advances in vision-language-action models, columnId=1322960859068244136, journalTitle=Journal of Image and Graphics, columnName=Embodied Intelligence and Humanoid Robotics Frontiers, runingTitle=null, highlight=null, articleAbstract=

Embodied artificial intelligence aims to endow agents with the ability to perceive，reason，and act through continuous interaction with the physical world. In contrast with disembodied intelligence，which mostly operates on static data and symbolic tasks，embodied intelligence must handle sensorimotor coupling，environmental uncertainty，long-horizon decision-making，and real-time control in open-world settings. These requirements expose the limitations of traditional perception-planning-control pipelines. Conventional robotic systems are often built from cascaded modules for visual perception，language understanding，state estimation，task planning，and low-level control. Although such pipelines benefit from modular engineering and interpretability，they frequently suffer from error accumulation across stages，brittle generalization to novel tasks and scenes，and heavy dependence on manually designed interfaces，reward functions，and domain-specific annotations. Recent advances in large-scale foundation models have opened a new path for embodied intelligence by enabling the integration of visual grounding，language reasoning，and action generation within a shared model space. This trend has led to the rapid emergence of vision-language-action （VLA） models，which aim to translate multimodal observations and task instructions directly into executable actions or action-conditioned policies，narrowing the gap between semantic understanding and physical execution.This survey presents a systematic review of VLA models in embodied intelligence from the perspectives of historical development，technical architecture，system design，training strategies，evaluation protocols，and future trends. We first revisit the technological trajectory that made VLA possible. The evolution of large vision models and large language models has substantially improved semantic representation，multimodal alignment，and knowledge transfer，while progress in robot learning has highlighted the importance of scalable policy learning from heterogeneous data sources. Within this context，VLA models can be considered the convergence of three lines of research： multimodal foundation models for perception and reasoning，imitation and reinforcement learning for embodied decision-making，and robot action modeling for continuous control. Their significance lies not merely in adding language prompts to robot policies，but also in establishing a unified interface through which high-level task intent，environmental perception，and low-level action generation can be jointly optimized.A central objective of this survey is to clarify the internal structure of VLA systems. We analyze the key technical modules that constitute current VLA pipelines. On the perception side，VLA models rely on visual encoders to transform raw observations into compact and task-relevant representations. These observations may include RGB images，depth maps，multi-view input，temporal image sequences，or structured spatial representations，such as 3D or bird’s-eye-view features. The choice of visual representation directly influences the model’s capability to capture object semantics，spatial relations，affordances，and scene dynamics. On the language side，textual encoders provide instruction understanding，goal specification，contextual reasoning，and cross-modal alignment. Language not only describes tasks but also offers a flexible interface for compositional generalization，abstract planning，and human-robot interaction. The crucial problem of action representation lies between perception and control. Compared with visual and textual tokens，action signals are continuous，temporally dense，and strongly constrained by embodiment. Therefore，we pay special attention to action tokenization and decoding，reviewing how actions can be represented as discrete tokens，continuous vectors，chunked trajectories，latent action codes，or generative distributions. We further compare the major decoding paradigms，including direct regression，classification after discretization，autoregressive sequence modeling，diffusion-based policy generation，and flow-based generation，and then discuss their respective trade-offs in precision，expressivity，stability，and inference speed.Beyond module-level analysis，this survey proposes a system-level taxonomy that categorizes existing VLA models into three broad classes： single systems，dual-system architectures，and hierarchical systems. Single systems integrate perception，reasoning，and action generation within a unified model，and they are attractive for end-to-end deployment，parameter sharing，and simplified optimization. However，they may experience difficulties in balancing high-level deliberation with fast control. Dual-system architectures address this issue by explicitly separating slow reasoning from fast acting，frequently coupling a deliberative module for semantic planning with a lightweight controller for reactive execution. This design is particularly relevant to robotics because reasoning and motor control operate under different temporal constraints. Hierarchical systems introduce structured intermediate abstractions，such as subgoals，skills，waypoints，object-centric plans，or symbolic task graphs，to improve modularity，interpretability，and reuse across tasks. We argue that this taxonomy provides a useful perspective for understanding the design space of VLA models and analyzing how different systems trade off generalization，efficiency，and robustness.The survey also reviews the training and evaluation ecosystem that supports VLA research. Current VLA models are typically trained through multistage pipelines that involve internet-scale visual-language corpora，simulated interactive data，teleoperated demonstrations，and real robot trajectories. Pretraining equips the model with broad perceptual and linguistic priors，while post-training and task-specific adaptation align those priors with embodiment，action semantics，and control requirements. We summarize commonly used strategies，such as supervised imitation learning，behavior cloning，instruction tuning，parameter-efficient fine-tuning，model distillation，reinforcement learning augmentation，and hybrid training paradigms that combine offline and online data. We further discuss the role of simulator-based benchmarks and real-world evaluations. Simulation enables scalable and reproducible testing under controlled settings，while real robot experiments reveal embodiment gaps，latency constraints，and deployment failures that are not fully captured by simulation alone. In reviewing the literature，we emphasize that cross-paper comparisons must be interpreted with caution because reported results are often influenced by differences in robot morphology，sensor configurations，action horizons，training data quality，evaluation metrics，hardware platforms，and implementation details.Despite their remarkable progress，VLA models still face several fundamental challenges. First，real-time inference remains a major bottleneck. Many powerful multimodal models rely on autoregressive decoding or iterative generative sampling，which may be too slow for high-frequency control. Second，action learning suffers from limited robotic data，inconsistent annotation quality，and poor cross-platform transfer，especially when embodiments differ substantially. Third，environmental generalization is far from solved. Models trained on curated datasets may degrade sharply in cluttered scenes，long-horizon tasks，dynamic environments，or safety-critical settings where perception is ambiguous and recovery from failure is required. Fourth，current VLA systems still exhibit limited causal reasoning and world modeling capabilities. Many methods align observations and instructions effectively but lack deep predictive understanding of object dynamics，interaction consequences，and long-term task structure. Fifth，evaluation remains fragmented. Existing studies often emphasize task success rate while underreporting execution frequency，safety violations，intervention cost，recovery capability，and robustness under distribution shift. Finally，broader issues of trustworthiness，including interpretability，failure diagnosis，ethical risk，and human-centered deployment，have become increasingly important as VLA systems move from laboratory demonstrations toward real applications.In the future，we argue that the next stage of VLA research will be defined by several converging directions. One is the shift from passive perception-conditioned action generation to active reasoning-driven embodied intelligence，in which agents can query，explore，verify，and adapt during execution. Another is the integration of richer world models that support prediction，imagination，and planning across time and modality. Lifelong and continual learning will also be essential for agents that are operating in evolving environments. In addition，future VLA systems are likely to expand beyond fixed single-robot manipulation toward integrated mobile manipulation，long-horizon household assistance，multi-agent collaboration，and broader open-world autonomy. Achieving these objectives will require not only stronger models，but also better data curation，more principled action representations，more realistic evaluation standards，and more reliable safety mechanisms. By synthesizing the development of VLA models from architecture to deployment，this survey aims to provide a structured reference for researchers and practitioners，and to support the advancement of embodied intelligence toward more general，robust，and trustworthy interactions in the physical world. Furthermore，a comprehensively curated list of open-source VLA algorithms，datasets，and simulation benchmarks discussed in this survey is updated at https://github.com/DefaultRui/vision-language-action-models-for-embodied-AI.

, authors=Rui Liu1^, 2, Wenguan Wang1^, 2, Jun Wang3, Yi Yang1^, 2^{, *}, authorsList=Rui Liu, Wenguan Wang, Jun Wang, Yi Yang, authorCompany=null, correspAuthors=Yi Yang, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322960899434225955, articleId=1322960879691637014, tenantId=1146029695717560320, journalId=1249024232475115590, language=CN, title=面向具身智能的视觉—语言—动作模型研究进展, columnId=1322960859248599210, journalTitle=中国图象图形学报, columnName=具身智能与人形机器人前沿, runingTitle=null, highlight=null, articleAbstract=

具身智能作为人工智能与机器人学交叉的前沿领域，旨在使智能体通过与物理世界的动态交互来感知、推理并执行任务。然而，传统基于深度学习的级联式感知—控制模型在开放、动态环境下泛化能力不足，且高度依赖大规模标注数据。近年来，视觉—语言—动作模型（vision-language-action models，VLA）通过融合视觉感知、语言理解与动作生成，为具身智能的研究与应用提供了新的推动力。本文系统梳理了基于VLA的具身智能研究进展，从发展历程、模型架构、系统分类、训练与评估等方面展开综述。首先，本文追溯了视觉与语言基础模型的演进脉络，并阐述VLA概念的提出背景；随后，本文深入剖析VLA的关键技术模块，包括视觉编码、语言表征及动作词元化与解码机制；在此基础上，引入系统架构分类法，将现有工作归纳为单系统、双系统与层次化3类，并分析其设计权衡与适用场景；此外，本文总结了模型的预训练与后训练策略，并梳理了仿真及真实环境下的主流评测基准；最后，分析了VLA在实时推理效率、数据质量、环境泛化性与安全伦理等维度面临的挑战，并展望从被动感知到主动推理、持续学习、场景泛化与可靠部署等未来方向。本文旨在为相关研究者提供系统的技术参考，推动VLA在开放世界具身任务中的理论发展和应用落地。本文提及的算法、数据集和评估指标已汇总至https://github.com/DefaultRui/vision-language-action-models-for-embodied-AI。

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² College of Artificial Intelligence，Zhejiang University，Hangzhou 310027，China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322960900164034866, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, authorId=1322960900017234222, language=CN, stringName=刘锐, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1^, 2, address=¹　浙江大学脑机智能全国重点实验室，杭州　310027
²　浙江大学人工智能学院，杭州　310027, bio={"content":"

刘锐，男，博士研究生，主要研究方向为具身智能和多模态大模型。E-mail： rui.liu@zju.edu.cn

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刘锐，男，博士研究生，主要研究方向为具身智能和多模态大模型。E-mail： rui.liu@zju.edu.cn

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²　浙江大学人工智能学院，杭州　310027, bio={"content":"

王文冠，男，研究员，主要研究方向为具身智能和人工智能赋能科学研究。E-mail： wenguanwang.ai@gmail.com

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王文冠，男，研究员，主要研究方向为具身智能和人工智能赋能科学研究。E-mail： wenguanwang.ai@gmail.com

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王俊，男，博士，主要研究方向为隐私计算。E-mail： junwang.lu@gmail.com

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王俊，男，博士，主要研究方向为隐私计算。E-mail： junwang.lu@gmail.com

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² College of Artificial Intelligence，Zhejiang University，Hangzhou 310027，China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322960900818346307, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, authorId=1322960900650574143, language=CN, stringName=杨易, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1^, 2^{, *}, address=¹　浙江大学脑机智能全国重点实验室，杭州　310027
²　浙江大学人工智能学院，杭州　310027, bio={"content":"

杨易，通信作者，男，教授，主要研究方向为人工智能和跨媒体研究。E-mail： yangyics@zju.edu.cn

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杨易，通信作者，男，教授，主要研究方向为人工智能和跨媒体研究。E-mail： yangyics@zju.edu.cn

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Singapore，Singapore： AAAI Press： 18764-18772 ［DOI： 10.1609/aaai.v40i22.38945］, articleTitle=MoLe-VLA： dynamic layer-skipping vision language action model via mixture-of-layers for efficient robot manipulation, refAbstract=null), Reference(id=1322960948352393836, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, doi=null, pmid=null, pmcid=null, year=2024c, volume=null, issue=null, pageStart=null, pageEnd=null, url=https://arxiv.org/pdf/2412.18194.pdf, language=null, rfNumber=null, rfOrder=259, authorNames=Zhang S D, Xu Z, Liu P J, Yu X P, Li Y, Gao Q H, journalName=null, refType=null, unstructuredReference=Zhang S D，Xu Z，Liu P J，Yu X P，Li Y，Gao Q H，et al. 2024c. VLABench： a large-scale benchmark for language-conditioned robotics manipulation with long-horizon reasoning tasks ［EB/OL］. ［2025-11-17］., articleTitle=VLABench： a large-scale benchmark for language-conditioned robotics manipulation with long-horizon reasoning tasks, refAbstract=null), Reference(id=1322960948415308397, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, doi=null, pmid=null, pmcid=null, year=2025k, volume=null, issue=null, pageStart=null, pageEnd=null, url=https://arxiv.org/pdf/2503.21696.pdf, language=null, rfNumber=null, rfOrder=260, authorNames=Zhang W Q, Wang M N, Liu G G, Xu H X, Jiang Y W, Shen Y L, journalName=null, refType=null, unstructuredReference=Zhang W Q，Wang M N，Liu G G，Xu H X，Jiang Y W，Shen Y L，et al. 2025k. Embodied-reasoner： synergizing visual search，reasoning，and action for embodied interactive tasks ［EB/OL］. ［2025-11-17］., articleTitle=Embodied-reasoner： synergizing visual search，reasoning，and action for embodied interactive tasks, refAbstract=null), Reference(id=1322960948503388782, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, doi=null, pmid=null, pmcid=null, year=2024d, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=null, rfOrder=261, authorNames=Zhang X, Zhang D, Li S M, Zhou Y Q, Qiu X P, journalName=null, refType=null, unstructuredReference=Zhang X，Zhang D，Li S M，Zhou Y Q and Qiu X P. 2024d. SpeechTokenizer： unified speech tokenizer for speech language models//Proceedings of the 12th International Conference on Learning Representations. 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CoT-VLA： visual chain-of-thought reasoning for vision-language-action models//Proceedings of 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville，USA： IEEE： 1702-1713 ［DOI： 10.1109/CVPR52734.2025.00166］, articleTitle=CoT-VLA： visual chain-of-thought reasoning for vision-language-action models, refAbstract=null), Reference(id=1322960948801184370, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, doi=null, pmid=null, pmcid=null, year=2023, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=null, rfOrder=265, authorNames=Zhao T Z, Kumar V, Levine S, Finn C, journalName=Proceedings of Robotics： Science and Systems XIX, refType=null, unstructuredReference=Zhao T Z，Kumar V，Levine S and Finn C. 2023. Learning fine-grained bimanual manipulation with low-cost hardware//Proceedings of Robotics： Science and Systems XIX. 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Unveiling the potential of vision-language-action models with open-ended multimodal instructions ［EB/OL］. ［2025-11-17］., articleTitle=Unveiling the potential of vision-language-action models with open-ended multimodal instructions, refAbstract=null), Reference(id=1322960949015093877, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, doi=null, pmid=null, pmcid=null, year=2024, volume=null, issue=null, pageStart=61229, pageEnd=61245, url=null, language=null, rfNumber=null, rfOrder=268, authorNames=Zhen H Y, Qiu X W, Chen P H, Yang J C, Yan X, Du Y L, journalName=null, refType=null, unstructuredReference=Zhen H Y，Qiu X W，Chen P H，Yang J C，Yan X，Du Y L，et al. 2024. 3D-VLA： a 3D vision-language-action generative world model//Proceedings of the 41st International Conference on Machine Learning. 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Vienna，Austria： OpenReview.net, articleTitle=： exploring unified 3D representation at scale, refAbstract=null), Reference(id=1322960949908480636, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, doi=null, pmid=null, pmcid=null, year=2026, volume=null, issue=null, pageStart=13782, pageEnd=13790, url=null, language=null, rfNumber=null, rfOrder=275, authorNames=Zhou X C, Han X Y, Yang F, Ma Y P, Tresp V, Knoll A, journalName=null, refType=null, unstructuredReference=Zhou X C，Han X Y，Yang F，Ma Y P，Tresp V and Knoll A. 2026. OpenDriveVLA： towards end-to-end autonomous driving with large vision language action model//Proceedings of 2026 AAAI Conference on Artificial Intelligence. Singapore，Singapore： AAAI Press： 13782-13790 ［DOI： 10.1609/aaai.v40i16.38386］, articleTitle=OpenDriveVLA： towards end-to-end autonomous driving with large vision language action model, refAbstract=null), Reference(id=1322960950046892669, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, doi=null, pmid=null, pmcid=null, year=2025a, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=null, rfOrder=276, authorNames=Zhou X Y, Tie G Y, Zhang G W, Wang H C, Zhou P, Sun L C, journalName=null, refType=null, unstructuredReference=Zhou X Y，Tie G Y，Zhang G W，Wang H C，Zhou P and Sun L C. 2025a. BadVLA： towards backdoor attacks on vision-language-action models via objective-decoupled optimization//Proceedings of the 39th Annual Conference on Neural Information Processing Systems. 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Robosuite： a modular simulation framework and benchmark for robot learning ［EB/OL］. ［2025-11-17］., articleTitle=Robosuite： a modular simulation framework and benchmark for robot learning, refAbstract=null), Reference(id=1322960950323716737, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, doi=null, pmid=null, pmcid=null, year=2023, volume=null, issue=null, pageStart=2165, pageEnd=2183, url=null, language=null, rfNumber=null, rfOrder=280, authorNames=Zitkovich B, Yu T H, Xu S C, Xu P, Xiao T, Xia F, journalName=null, refType=null, unstructuredReference=Zitkovich B，Yu T H，Xu S C，Xu P，Xiao T，Xia F，et al. 2023. RT-2： vision-language-action models transfer web knowledge to robotic control//Proceedings of the 7th Conference on Robot Learning. 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建模方式	优势	劣势	适用场景
离散化词元	泛化性强，零样本泛化能力突出，推理较快	存在精度误差，长序列影响输出频率	指令跟随、移动导航
连续回归	输出轨迹平滑，精度较高，推理高效	难以建模复杂动作分布，泛化能力弱	精确轨迹跟踪、动态的连续控制任务
扩散模型	能建模复杂动作分布，策略鲁棒，训练稳定	推理慢，计算开销大	复杂抓取操控、精细装配
流匹配	推理效率高，平衡表达能力与效率	训练数据质量要求高	对实时性有要求的操作任务

), ArticleFig(id=1322960904811323738, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, language=CN, label=表1, caption=

不同动作词元解码建模方式对比

, figureFileSmall=null, figureFileBig=null, tableContent=

建模方式	优势	劣势	适用场景
离散化词元	泛化性强，零样本泛化能力突出，推理较快	存在精度误差，长序列影响输出频率	指令跟随、移动导航
连续回归	输出轨迹平滑，精度较高，推理高效	难以建模复杂动作分布，泛化能力弱	精确轨迹跟踪、动态的连续控制任务
扩散模型	能建模复杂动作分布，策略鲁棒，训练稳定	推理慢，计算开销大	复杂抓取操控、精细装配
流匹配	推理效率高，平衡表达能力与效率	训练数据质量要求高	对实时性有要求的操作任务

), ArticleFig(id=1322960904911987035, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, language=EN, label=Table 2, caption=Comparison of performance of diffusion-policy-based and flow-matching-based VLA（Jiang et al.，2025b）, figureFileSmall=null, figureFileBig=null, tableContent=

方法	Push-T		RoboMimic Square
	动作模仿平均分数/%	延迟/ms	动作模仿平均分数/%	延迟/ms
扩散策略（Chi等，2025）	87.0	127.2	77.2	53.4
流匹配策略（Zhang和Gienger等，2024）	71.0	12.9	54.0	4.8

), ArticleFig(id=1322960904995873116, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, language=CN, label=表2, caption=

基于扩散策略/流匹配的VLA性能对比（Jiang等，2025b）

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	Push-T		RoboMimic Square
	动作模仿平均分数/%	延迟/ms	动作模仿平均分数/%	延迟/ms
扩散策略（Chi等，2025）	87.0	127.2	77.2	53.4
流匹配策略（Zhang和Gienger等，2024）	71.0	12.9	54.0	4.8

), ArticleFig(id=1322960905067176285, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, language=EN, label=Table 3, caption=Single-system VLA methods and their characteristics, figureFileSmall=null, figureFileBig=null, tableContent=

方法	语言模型	视觉编码器	学习方式	特点
RT-2（Zitkovich等，2023）	PaLM-E	-	模仿学习（连续动作）+微调（离散动作）	将动作表征为VLM词元以实现泛化
RT-2-X（O’Neill等，2024）		ViT-22B	模仿学习（连续动作）+微调（离散动作）	跨机器人数据微调以实现模型迁移
OpenVLA（Kim等，2024）	LLaMA2-7B	DINOv2 + SigLIP	模仿学习（连续动作）	开源7B参数VLA实现通用机器人控制
LEO（Huang等，2024a）	Vicuna-7B	ConvNext	模仿学习（连续动作）+微调（离散动作）	结合三维特征与LLM用于动作决策
ReVLA（Dey等，2025）	LLaMA2-7B	DINOv2 + SigLIP	模仿学习（连续动作）	反向回溯到策略执行提升可解释性
TraceVLA（Zheng等，2025b）	LLaMA2-7B	DINOv2 + SigLIP	模仿学习（连续动作）	利用视觉轨迹提示实现跨模态时间感知
FuSe（Jones等，2025）	PaLM-Gemma-3B	-	模仿学习（连续动作）+微调（离散动作）	使用自然语言用于跨模态微调
UniAct（Zheng等，2025a）	LLaVA-0.5B	SigLIP ViT-L	策略蒸馏（连续动作）	提出通用动作空间实现自适应控制
SpatialVLA（Qu等，2025）	Gemini	-	模仿学习（连续动作）	通过三维语义和动作网格改进泛化
RoboFlamingo（Li等，2024b）	MPT-1B	ViT	模仿学习（连续动作）+微调（离散动作）	适配开源VLM用于机器人控制
RoboMamba（Liu等，2024a）	Mamba-2.8B	CLIP/SigLIP ViT-L	模仿学习（连续动作）+微调（离散动作）	将Mamba架构引入VLA领域
OpenVLA-OFT（Kim等， 2025b）	LLaMA2-7B	DINOv2 + SigLIP	策略蒸馏（连续动作）	通过OpenVLA蒸馏提升性能
Spec-VLA（Wang等，2025c）	LLaMA2-7B	DINOv2 + SigLIP	策略蒸馏（连续动作）	在解码阶段进行加速，实现无损推理
FlashVLA（Tan等，2025b）	LLaMA2-7B	DINOv2 + SigLIP	模仿学习（连续动作）	训练加速并提升VLA推理效率
CogVLA（Li等，2025g）	LLaMA2-7B	DINOv2 + SigLIP	策略蒸馏（连续动作）	提出认知对齐框架以实现高效操作
UnifiedVLA（Wang等， 2025f）	Emu	-	模仿学习（连续动作）+微调（离散动作）	将输入信号转换为词元构建统一模型
PD-VLA（Song等，2025a）	Vicuna-1.5B	CLIP ViT-L	策略蒸馏（连续动作）	增强基于蒸馏的快速机器人控制并行
4D-VLA（Zhang等，2025f）	InternVL-1.4B	-	策略蒸馏（连续动作）	整合时空线索实现高效VLA预训练
OE-VLA（Zhao等，2025c）	Qwen1.5-5B	-	模仿学习（连续动作）+微调（离散动作）	为开放末端任务引入跨模态交互
ST-VLA（Patratskiy等，2025）	PalGemma2	-	模仿学习（连续动作）+微调（离散动作）	将视觉轨迹投影到深度图增强理解
VLAS（Zhao等，2025b）	Vicuna-7B	CLIP ViT-L	模仿学习（连续动作）+微调（离散动作）	引入双模态自编码器并构建配对数据集
DeeR-VLA（Yue等，2024）	MPT-1B / 7B	CLIP ViT-L	策略蒸馏（连续动作）	提出动态早退出以减少推理开销
BitVLA（Wang等，2025b）	BitNet1.5 8B	SigLIP ViT-L	策略蒸馏（连续动作）+微调（离散动作）	设计权重模型以减少部署内存成本
MoLe-VLA（Zhang等，2026）	LLaMA2-7B	DINOv2 + SigLIP	策略蒸馏（连续动作）	通过动态模型层激活减少计算量

), ArticleFig(id=1322960905146868062, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, language=CN, label=表3, caption=

单系统模型VLA方法及其特点

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	语言模型	视觉编码器	学习方式	特点
RT-2（Zitkovich等，2023）	PaLM-E	-	模仿学习（连续动作）+微调（离散动作）	将动作表征为VLM词元以实现泛化
RT-2-X（O’Neill等，2024）		ViT-22B	模仿学习（连续动作）+微调（离散动作）	跨机器人数据微调以实现模型迁移
OpenVLA（Kim等，2024）	LLaMA2-7B	DINOv2 + SigLIP	模仿学习（连续动作）	开源7B参数VLA实现通用机器人控制
LEO（Huang等，2024a）	Vicuna-7B	ConvNext	模仿学习（连续动作）+微调（离散动作）	结合三维特征与LLM用于动作决策
ReVLA（Dey等，2025）	LLaMA2-7B	DINOv2 + SigLIP	模仿学习（连续动作）	反向回溯到策略执行提升可解释性
TraceVLA（Zheng等，2025b）	LLaMA2-7B	DINOv2 + SigLIP	模仿学习（连续动作）	利用视觉轨迹提示实现跨模态时间感知
FuSe（Jones等，2025）	PaLM-Gemma-3B	-	模仿学习（连续动作）+微调（离散动作）	使用自然语言用于跨模态微调
UniAct（Zheng等，2025a）	LLaVA-0.5B	SigLIP ViT-L	策略蒸馏（连续动作）	提出通用动作空间实现自适应控制
SpatialVLA（Qu等，2025）	Gemini	-	模仿学习（连续动作）	通过三维语义和动作网格改进泛化
RoboFlamingo（Li等，2024b）	MPT-1B	ViT	模仿学习（连续动作）+微调（离散动作）	适配开源VLM用于机器人控制
RoboMamba（Liu等，2024a）	Mamba-2.8B	CLIP/SigLIP ViT-L	模仿学习（连续动作）+微调（离散动作）	将Mamba架构引入VLA领域
OpenVLA-OFT（Kim等， 2025b）	LLaMA2-7B	DINOv2 + SigLIP	策略蒸馏（连续动作）	通过OpenVLA蒸馏提升性能
Spec-VLA（Wang等，2025c）	LLaMA2-7B	DINOv2 + SigLIP	策略蒸馏（连续动作）	在解码阶段进行加速，实现无损推理
FlashVLA（Tan等，2025b）	LLaMA2-7B	DINOv2 + SigLIP	模仿学习（连续动作）	训练加速并提升VLA推理效率
CogVLA（Li等，2025g）	LLaMA2-7B	DINOv2 + SigLIP	策略蒸馏（连续动作）	提出认知对齐框架以实现高效操作
UnifiedVLA（Wang等， 2025f）	Emu	-	模仿学习（连续动作）+微调（离散动作）	将输入信号转换为词元构建统一模型
PD-VLA（Song等，2025a）	Vicuna-1.5B	CLIP ViT-L	策略蒸馏（连续动作）	增强基于蒸馏的快速机器人控制并行
4D-VLA（Zhang等，2025f）	InternVL-1.4B	-	策略蒸馏（连续动作）	整合时空线索实现高效VLA预训练
OE-VLA（Zhao等，2025c）	Qwen1.5-5B	-	模仿学习（连续动作）+微调（离散动作）	为开放末端任务引入跨模态交互
ST-VLA（Patratskiy等，2025）	PalGemma2	-	模仿学习（连续动作）+微调（离散动作）	将视觉轨迹投影到深度图增强理解
VLAS（Zhao等，2025b）	Vicuna-7B	CLIP ViT-L	模仿学习（连续动作）+微调（离散动作）	引入双模态自编码器并构建配对数据集
DeeR-VLA（Yue等，2024）	MPT-1B / 7B	CLIP ViT-L	策略蒸馏（连续动作）	提出动态早退出以减少推理开销
BitVLA（Wang等，2025b）	BitNet1.5 8B	SigLIP ViT-L	策略蒸馏（连续动作）+微调（离散动作）	设计权重模型以减少部署内存成本
MoLe-VLA（Zhang等，2026）	LLaMA2-7B	DINOv2 + SigLIP	策略蒸馏（连续动作）	通过动态模型层激活减少计算量

), ArticleFig(id=1322960905230754143, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, language=EN, label=Table 4, caption=Dual-system VLA methods and their characteristics, figureFileSmall=null, figureFileBig=null, tableContent=

方法	System2 骨干网络	System1 学习方式	特点
DP-VLA（Han等，2024）	OpenVLA	回归	提出双系统架构用于机器人操作
RoboDual（Bu等，2024）	OpenVLA	扩散式	引入用于控制的DiT专家模型
LCB（Shentu等，2024）	LLaVA	扩散式	新增特殊词元编码并作为策略条件
GR00T N1（Bjorck等，2025）	Eagle-2	流匹配	结合VLM和DiT用于人形机器人操作
CogACT（Li等，2024a）	OpenVLA	扩散式	将动作扩散过程融入VLA
HiRT（Zhang等，2024a）	InstructCLIP	回归	提出双系统模型，System 2以较低频率运行
Fast-in-Slow（Chen等，2025a）	Prismatic	扩散式、自回归	在基于VLM的推理器中提高执行效率
OpenHelix（Cui等，2025）	LLaVA	扩散式	对连接VLM和策略的词元进行辅助训练
ChatVLA-1（Zhou等，2025c）	Qwen2-VL	扩散式	用于独立感知和控制的共享注意力机制
ChatVLA-2（Zhou等，2025b）	Qwen2-VL	扩散式	动态路由和开放世界机器人推理
Diffusion-VLA（Wen等，2025a）	Qwen2-VL	扩散式	通过FiLM将Qwen2-VL推理与扩散动作融合
TriVLA（Liu等，2025d）	Eagle-2	扩散式	将动力学感知模块作为System 3补充静态感知
GF-VLA（Li等，2025b）	LLaVA-v1.5	回归	通过人类视频的信息实现可解释的双手操作
RationalVLA（Song等，2025b）	LLaMA2	扩散式	引入可学习的隐式接口，实现鲁棒操作
VQ-VLA（Wang等，2025g）	OpenVLA	变分自编码器	引入向量量化的动作词元，实现平滑的控制
TinyVLA（Wen等，2024b）	LLaVA	扩散式	证明高性能VLA无需大规模机器人预训练
pi-0（Black等，2024）	PaliGemma	流匹配	结合VLM和基于流匹配的动作专家模型
pi-0-FAST（Pertsch等，2025）	pi-0	自回归	提出基于DCT的动作词元，高效自回归训练
pi-0.5（Black等，2025）	PaliGemma	流匹配	将高层提示转换为更细粒度的子任务预测
ForceVLA（Yu等，2025）	pi-0	扩散式	通过混合专家模型处理力传感模态信息
SmolVLA（Shukor等，2025）	SmolVLM-2	流匹配	采用SmolVLM-2和流匹配构建轻量级VLA
GR-3（Cheang等，2025）	Qwen2.5-VL	流匹配	结合VLM数据和少量轨迹标注，实现鲁棒操作
Villa-X（Chen等，2025d）	PaliGemma	流匹配	在扩散过程中集成本体感知隐变量信息
GraspVLA（Deng等，2025）	InternLM2	流匹配	利用合成数据实现仿真到开放世界的抓取

), ArticleFig(id=1322960905297863008, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, language=CN, label=表4, caption=

双系统模型VLA方法及其特点

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	System2 骨干网络	System1 学习方式	特点
DP-VLA（Han等，2024）	OpenVLA	回归	提出双系统架构用于机器人操作
RoboDual（Bu等，2024）	OpenVLA	扩散式	引入用于控制的DiT专家模型
LCB（Shentu等，2024）	LLaVA	扩散式	新增特殊词元编码并作为策略条件
GR00T N1（Bjorck等，2025）	Eagle-2	流匹配	结合VLM和DiT用于人形机器人操作
CogACT（Li等，2024a）	OpenVLA	扩散式	将动作扩散过程融入VLA
HiRT（Zhang等，2024a）	InstructCLIP	回归	提出双系统模型，System 2以较低频率运行
Fast-in-Slow（Chen等，2025a）	Prismatic	扩散式、自回归	在基于VLM的推理器中提高执行效率
OpenHelix（Cui等，2025）	LLaVA	扩散式	对连接VLM和策略的词元进行辅助训练
ChatVLA-1（Zhou等，2025c）	Qwen2-VL	扩散式	用于独立感知和控制的共享注意力机制
ChatVLA-2（Zhou等，2025b）	Qwen2-VL	扩散式	动态路由和开放世界机器人推理
Diffusion-VLA（Wen等，2025a）	Qwen2-VL	扩散式	通过FiLM将Qwen2-VL推理与扩散动作融合
TriVLA（Liu等，2025d）	Eagle-2	扩散式	将动力学感知模块作为System 3补充静态感知
GF-VLA（Li等，2025b）	LLaVA-v1.5	回归	通过人类视频的信息实现可解释的双手操作
RationalVLA（Song等，2025b）	LLaMA2	扩散式	引入可学习的隐式接口，实现鲁棒操作
VQ-VLA（Wang等，2025g）	OpenVLA	变分自编码器	引入向量量化的动作词元，实现平滑的控制
TinyVLA（Wen等，2024b）	LLaVA	扩散式	证明高性能VLA无需大规模机器人预训练
pi-0（Black等，2024）	PaliGemma	流匹配	结合VLM和基于流匹配的动作专家模型
pi-0-FAST（Pertsch等，2025）	pi-0	自回归	提出基于DCT的动作词元，高效自回归训练
pi-0.5（Black等，2025）	PaliGemma	流匹配	将高层提示转换为更细粒度的子任务预测
ForceVLA（Yu等，2025）	pi-0	扩散式	通过混合专家模型处理力传感模态信息
SmolVLA（Shukor等，2025）	SmolVLM-2	流匹配	采用SmolVLM-2和流匹配构建轻量级VLA
GR-3（Cheang等，2025）	Qwen2.5-VL	流匹配	结合VLM数据和少量轨迹标注，实现鲁棒操作
Villa-X（Chen等，2025d）	PaliGemma	流匹配	在扩散过程中集成本体感知隐变量信息
GraspVLA（Deng等，2025）	InternLM2	流匹配	利用合成数据实现仿真到开放世界的抓取

), ArticleFig(id=1322960905381749089, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, language=EN, label=Table 5, caption=Hierarchical-system VLA methods and their characteristics, figureFileSmall=null, figureFileBig=null, tableContent=

方法	骨干网络	学习方式	特点
MoManipVLA（Wu等，2025c）	OpenVLA-7B	模仿学习（IM）	预测路径点并优化全身轨迹
ManipLVM-R1（Song等，2026）	Qwen2.5-VL-3B	强化学习（RL）	对轨迹进行GRPO调优，分布外场景中表现鲁棒
PaLM-E（Driess等，2023）	PaLM	监督微调（SFT）	训练具备通用机器人操作指令生成能力的VLM
Embodied-Reasoner（Zhang等，2025k）	Qwen2-VL-7B	监督微调（SFT）	构建VLM和开源数据集用于规划、推理和反思
RoboPoint（Yuan等，2024b）	Vicuna-v1.5-13B	监督微调（SFT）	以路径点形式实现VLM空间感知决策
Reinforced Planning（Wu等， 2025a）	Qwen2.5-VL-7B	监督微调（SFT）、强化学习（RL）	在微调模型上进行GRPO，提升泛化能力
CoM（Wang等，2025a）	Gemini 1.5 Pro	API接口	从演示中通过序列多模态提示提取力感知操作
RoVI（Li等，2025e）	GPT-4 /LLaVA-13B	监督微调（SFT）	通过层次化结构实现精确操作
ReLEP（Liu等，2024b）	LLaVA-1.6-7B	监督微调（SFT）	具有隐式逻辑推理和幻觉缓解的规划框架
ViLA（Hu等，2023）	GPT-4V	API接口	提供感知、推理集成的规划器
RoboBrain（Ji等，2025）	LLaVA	监督微调（SFT）	设计规划、可操作性和轨迹的层次化VLA
HiRobot（Shi等，2025）	PaliGemma-3B/pi-0	监督微调（SFT）、模仿学习（IM）	具有可解释性和复杂任务处理能力
DexVLA（Wen等，2025b）	Qwen2-VL	监督微调（SFT）、模仿学习（IM）	结合VLM和扩散模块，采用3阶段训练方案
Instruct2Act（Huang等，2023b）	ChatGPT	监督微调（SFT）	生成调用API的程序，实现从指令到动作的映射
RoboMatrix（Mao等，2024）	Vicuna-v1.5	监督微调（SFT）、模仿学习（IM）	具有模块化调度层、技能层和硬件层的VLA
PointVLA（Li等，2026）	Qwen2-VL	API接口	附加点云编码器以获得空间感知
A0（Xu等，2025）	Qwen2.5-7B	监督微调（SFT）、API	具有关键点预测的分层扩散模块
RoBridge（Zhang等，2025j）	GPT-4o	模仿学习（IM）、强化学习（RL）	通过VLM与RL动作预测，提高模型迁移性
RT-H（Belkhale等，2024）	PaLI-X 55B	监督微调（SFT）、模仿学习（IM）	动作层次结构，使用语言驱动的动作表征
ReKep（Huang等，2024b）	GPT-4o	API接口	通过关键点约束实现无训练的操作轨迹生成
HiBerNAC（Wu等，2025b）	PaLM2	监督微调（SFT）	提出模仿大脑分层结构的异步模型
LLARVA（Niu等，2024）	LLaMa2 7B	监督微调（SFT）、模仿学习（IM）	视觉—动作指令调优，结合大型指令调优数据集
VLA-Touch（Bi等，2025）	GPT-4o	模仿学习（IM）	基于扩散的模仿学习将触觉传感集成到VLA中

), ArticleFig(id=1322960905448857954, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, language=CN, label=表5, caption=

层次化系统模型VLA方法及其特点

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	骨干网络	学习方式	特点
MoManipVLA（Wu等，2025c）	OpenVLA-7B	模仿学习（IM）	预测路径点并优化全身轨迹
ManipLVM-R1（Song等，2026）	Qwen2.5-VL-3B	强化学习（RL）	对轨迹进行GRPO调优，分布外场景中表现鲁棒
PaLM-E（Driess等，2023）	PaLM	监督微调（SFT）	训练具备通用机器人操作指令生成能力的VLM
Embodied-Reasoner（Zhang等，2025k）	Qwen2-VL-7B	监督微调（SFT）	构建VLM和开源数据集用于规划、推理和反思
RoboPoint（Yuan等，2024b）	Vicuna-v1.5-13B	监督微调（SFT）	以路径点形式实现VLM空间感知决策
Reinforced Planning（Wu等， 2025a）	Qwen2.5-VL-7B	监督微调（SFT）、强化学习（RL）	在微调模型上进行GRPO，提升泛化能力
CoM（Wang等，2025a）	Gemini 1.5 Pro	API接口	从演示中通过序列多模态提示提取力感知操作
RoVI（Li等，2025e）	GPT-4 /LLaVA-13B	监督微调（SFT）	通过层次化结构实现精确操作
ReLEP（Liu等，2024b）	LLaVA-1.6-7B	监督微调（SFT）	具有隐式逻辑推理和幻觉缓解的规划框架
ViLA（Hu等，2023）	GPT-4V	API接口	提供感知、推理集成的规划器
RoboBrain（Ji等，2025）	LLaVA	监督微调（SFT）	设计规划、可操作性和轨迹的层次化VLA
HiRobot（Shi等，2025）	PaliGemma-3B/pi-0	监督微调（SFT）、模仿学习（IM）	具有可解释性和复杂任务处理能力
DexVLA（Wen等，2025b）	Qwen2-VL	监督微调（SFT）、模仿学习（IM）	结合VLM和扩散模块，采用3阶段训练方案
Instruct2Act（Huang等，2023b）	ChatGPT	监督微调（SFT）	生成调用API的程序，实现从指令到动作的映射
RoboMatrix（Mao等，2024）	Vicuna-v1.5	监督微调（SFT）、模仿学习（IM）	具有模块化调度层、技能层和硬件层的VLA
PointVLA（Li等，2026）	Qwen2-VL	API接口	附加点云编码器以获得空间感知
A0（Xu等，2025）	Qwen2.5-7B	监督微调（SFT）、API	具有关键点预测的分层扩散模块
RoBridge（Zhang等，2025j）	GPT-4o	模仿学习（IM）、强化学习（RL）	通过VLM与RL动作预测，提高模型迁移性
RT-H（Belkhale等，2024）	PaLI-X 55B	监督微调（SFT）、模仿学习（IM）	动作层次结构，使用语言驱动的动作表征
ReKep（Huang等，2024b）	GPT-4o	API接口	通过关键点约束实现无训练的操作轨迹生成
HiBerNAC（Wu等，2025b）	PaLM2	监督微调（SFT）	提出模仿大脑分层结构的异步模型
LLARVA（Niu等，2024）	LLaMa2 7B	监督微调（SFT）、模仿学习（IM）	视觉—动作指令调优，结合大型指令调优数据集
VLA-Touch（Bi等，2025）	GPT-4o	模仿学习（IM）	基于扩散的模仿学习将触觉传感集成到VLA中

), ArticleFig(id=1322960905524355427, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, language=EN, label=Table 6, caption=Performance of different VLA methods on LIBERO dataset, figureFileSmall=null, figureFileBig=null, tableContent=

模型	参数量	成功率/%
		Spatial	Object	Goal	Long
Diffusion Policy（Chi等，2025）	-	78.3	92.5	68.3	50.5
MaIL（Jia等，2024）	24.7 M	74.3	90.1	81.8	78.6
MDT（Reuss等，2024）	75 M	78.5	87.5	73.5	64.8
Octo（Ghosh等，2024）	93 M	78.9	85.7	84.6	51.1
VLA-OS（Gao等，2025）	0.5 B	87.0	96.5	92.7	66.0
VLA-Adapter（Wang等，2026）	0.5 B	97.8	99.2	97.2	95.0
GraspVLA（Deng等，2025）	1.8 B	-	94.1	91.2	82.0
GR00T N1（Bjorck等，2025）	2 B	94.4	97.6	93.0	90.6
SmolVLA（Shukor等，2025）	2.2 B	93.0	94.0	91.0	77.0
NORA（Hung等，2025）	3 B	92.2	95.4	89.4	74.6
pi-0-FAST（Pertsch等，2025）	3 B	96.4	96.8	88.6	60.2
pi-0（Black等，2024）	3 B	96.8	98.8	95.8	85.2
SpatialVLA（Qu等，2025）	4 B	88.2	89.9	78.6	55.5
4D-VLA（Zhang等，2025f）	4 B	88.9	95.2	90.9	79.1
PD-VLA（Song等，2025a）	7 B	95.5	96.7	94.9	91.7
ThinkAct（Huang等，2025a）	7 B	88.3	91.4	87.1	70.9
MolmoAct（Lee等，2025）	7 B	87.0	95.4	87.6	77.2
TraceVLA（Zheng等，2025b）	7 B	84.6	85.2	75.1	54.1
WorldVLA（Cen等，2025）	7 B	87.6	96.2	83.4	60.0
CoT-VLA（Zhao等，2025a）	7 B	87.5	91.6	87.6	69.0
UniVLA（Bu等，2025b）	7 B	96.5	96.8	95.6	92.0
OpenVLA（Kim等，2024）	7 B	84.7	88.4	79.2	53.7
LAPA（Ye等，2025）	7 B	73.8	74.6	58.8	55.4
UnifiedVLA（Wang等，2025f）	8.5 B	95.4	98.8	93.6	94.0
FlowVLA（Zhong等，2025b）	8.5 B	93.2	95.0	91.6	72.6

), ArticleFig(id=1322960905604047204, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, language=CN, label=表6, caption=

不同VLA方法在LIBERO数据集的性能

, figureFileSmall=null, figureFileBig=null, tableContent=

模型	参数量	成功率/%
		Spatial	Object	Goal	Long
Diffusion Policy（Chi等，2025）	-	78.3	92.5	68.3	50.5
MaIL（Jia等，2024）	24.7 M	74.3	90.1	81.8	78.6
MDT（Reuss等，2024）	75 M	78.5	87.5	73.5	64.8
Octo（Ghosh等，2024）	93 M	78.9	85.7	84.6	51.1
VLA-OS（Gao等，2025）	0.5 B	87.0	96.5	92.7	66.0
VLA-Adapter（Wang等，2026）	0.5 B	97.8	99.2	97.2	95.0
GraspVLA（Deng等，2025）	1.8 B	-	94.1	91.2	82.0
GR00T N1（Bjorck等，2025）	2 B	94.4	97.6	93.0	90.6
SmolVLA（Shukor等，2025）	2.2 B	93.0	94.0	91.0	77.0
NORA（Hung等，2025）	3 B	92.2	95.4	89.4	74.6
pi-0-FAST（Pertsch等，2025）	3 B	96.4	96.8	88.6	60.2
pi-0（Black等，2024）	3 B	96.8	98.8	95.8	85.2
SpatialVLA（Qu等，2025）	4 B	88.2	89.9	78.6	55.5
4D-VLA（Zhang等，2025f）	4 B	88.9	95.2	90.9	79.1
PD-VLA（Song等，2025a）	7 B	95.5	96.7	94.9	91.7
ThinkAct（Huang等，2025a）	7 B	88.3	91.4	87.1	70.9
MolmoAct（Lee等，2025）	7 B	87.0	95.4	87.6	77.2
TraceVLA（Zheng等，2025b）	7 B	84.6	85.2	75.1	54.1
WorldVLA（Cen等，2025）	7 B	87.6	96.2	83.4	60.0
CoT-VLA（Zhao等，2025a）	7 B	87.5	91.6	87.6	69.0
UniVLA（Bu等，2025b）	7 B	96.5	96.8	95.6	92.0
OpenVLA（Kim等，2024）	7 B	84.7	88.4	79.2	53.7
LAPA（Ye等，2025）	7 B	73.8	74.6	58.8	55.4
UnifiedVLA（Wang等，2025f）	8.5 B	95.4	98.8	93.6	94.0
FlowVLA（Zhong等，2025b）	8.5 B	93.2	95.0	91.6	72.6

), ArticleFig(id=1322960905671156069, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, language=EN, label=Table 7, caption=Inference efficiency of different VLA models, figureFileSmall=null, figureFileBig=null, tableContent=

模型	执行效率/Hz	时延/s
OpenVLA（Kim等，2024）	4.2	0.239 6
OpenVLA-OFT（Kim等，2025b）	71.4	0.112 0
VLA-Adapter（Wang等，2026）	219.2	0.036 5

), ArticleFig(id=1322960905746653542, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960879691637014, language=CN, label=表7, caption=

不同VLA模型的推理效率

, figureFileSmall=null, figureFileBig=null, tableContent=

模型	执行效率/Hz	时延/s
OpenVLA（Kim等，2024）	4.2	0.239 6
OpenVLA-OFT（Kim等，2025b）	71.4	0.112 0
VLA-Adapter（Wang等，2026）	219.2	0.036 5

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