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Vision Language Action Models in Robotic Manipulation: A Systematic Review

Muhayy Ud Din, Waseem Akram, Lyes Saad Saoud, Jan Rosell, Irfan Hussain

arXiv:2507.10672v1cs.ROcs.CV

TL;DR

Robotic manipulation research still faces gaps in multimodal datasets and reliable transfer across embodiments and from simulation to the physical world. This review organizes VLA architectures, datasets, simulation, and evaluation, finding rapid progress alongside persistent challenges in scalable pretraining, transfer, and safety-critical transparency.

  • Problem

    VLA development is constrained by the difficulty of combining long-horizon, multi-skill control with exhaustive multimodal inputs, and by limits on transfer across embodiments and from simulation to real-world conditions.

    Source: §5.4 · p. 17, §8.1 · p. 21, §8.2 · p. 22

  • Method

    The review systematically organizes VLA research across architectures, dataset ecosystems, simulation platforms, and evaluation methodologies, including a quantitative benchmark of datasets by task complexity and modality richness.

    Source: §1 · p. 3, §9 · p. 23

  • Results

    The review finds rapid evolution toward multimodal, instruction-conditioned agents capable of generalizing across tasks, embodiments, and environments, while scalable pretraining, physical-world transfer, and safety-critical transparency remain challenges.

    Source: §9 · p. 23

  • Takeaways & Limitations

    The review synthesizes architectural principles, learning paradigms, and deployment infrastructures while outlining open problems for future VLA research.

    Source: §9 · p. 23

    The dataset benchmark sets all task-complexity and modality-sensitivity weights to one, although the weights can be tuned.

    Source: §5.3 · p. 16

Abstract

from arXiv · show

Vision Language Action (VLA) models represent a transformative shift in robotics, with the aim of unifying visual perception, natural language understanding, and embodied control within a single learning framework. This review presents a comprehensive and forward-looking synthesis of the VLA paradigm, with a particular emphasis on robotic manipulation and instruction-driven autonomy. We comprehensively analyze 102 VLA models, 26 foundational datasets, and 12 simulation platforms that collectively shape the development and evaluation of VLAs models. These models are categorized into key architectural paradigms, each reflecting distinct strategies for integrating vision, language, and control in robotic systems. Foundational datasets are evaluated using a novel criterion based on task complexity, variety of modalities, and dataset scale, allowing a comparative analysis of their suitability for generalist policy learning. We introduce a two-dimensional characterization framework that organizes these datasets based on semantic richness and multimodal alignment, showing underexplored regions in the current data landscape. Simulation environments are evaluated for their effectiveness in generating large-scale data, as well as their ability to facilitate transfer from simulation to real-world settings and the variety of supported tasks. Using both academic and industrial contributions, we recognize ongoing challenges and outline strategic directions such as scalable pretraining protocols, modular architectural design, and robust multimodal alignment strategies. This review serves as both a technical reference and a conceptual roadmap for advancing embodiment and robotic control, providing insights that span from dataset generation to real world deployment of generalist robotic agents.

1. Introduction

VLA models aim to unite visual perception, natural-language understanding, and embodied control, while relying on diverse datasets and simulation to support instruction-following robotic autonomy. This review addresses the need for a systematic synthesis by organizing architectures, benchmarking datasets, reviewing simulation platforms, and outlining research directions.

1. Introduction

  • VLA models combine vision, language, and action to pursue autonomous execution of natural-language instructions in dynamic environments.The introduction contrasts this aim with task-specific robotic systems, which struggle in dynamic and unstructured environments.Source: §1 · p. 1
  • Real-world data collection is costly and limited in diversity, while datasets and simulation support training across varied tasks and embodiments.Open X-Embodiment combines data from 22 embodiments and more than 500 tasks using a shared action space; simulation can scale data generation across settings and agent embodiments.Source: §1 · p. 2
  • The review addresses a gap in systematic synthesis of VLA architectures, datasets, simulation platforms, and evaluation protocols.It aims to contextualize the state of the art and identify patterns, limitations, and opportunities.Source: §1 · p. 2
  • The review classifies VLA architectures by how they integrate perception, natural-language understanding, and embodied control.Its dataset benchmark uses task-complexity and modality-richness measures to reveal a shortage of datasets combining very high complexity with comprehensive multimodalities.Source: §1 · pp. 2–3
  • The review examines simulation platforms’ sensory modalities and applications, including their role in large-scale, reproducible experiments and data generation.It also identifies challenges and future directions in architectural modularity, scalable data generation, differentiable contact modeling, and language-grounding APIs.Source: §1 · p. 3
  • The paper proceeds from literature selection and model foundations through architectures, datasets, simulation, applications, evaluation, challenges, and conclusions.Figure 1 also traces models, datasets, and contributing institutions from 2022 to 2025.Source: §1 · pp. 2–3, Figure 1 · p. 2

2. Literature Search and Selection Criteria

The review searches research databases and arXiv for work on VLA models, datasets, and simulation tools relevant to robotic manipulation. It supplements database searches with language-model queries, then merges, de-duplicates, and manually validates candidate lists.

2. Literature Search and Selection Criteria

  • The search covered IEEE Xplore, Elsevier, Springer Nature, MDPI, Wiley, and arXiv, with topic-specific keywords and criteria for original research.Included work had to propose or evaluate a VLA model, dataset, or simulator for robotic manipulation or manipulation data generation, and present a novel model, dataset, or simulator.Source: §2 · pp. 3–4
  • Language-model queries supplemented database searches with targeted lists of models, datasets, and simulation tools.The queries covered VLA architectures, diffusion-based action decoders, manipulation datasets, and robotic simulators.Source: §2 · p. 3
  • Figure 3 charts annual counts of new VLA models and datasets from 2022 to 2025, showing faster model growth, particularly in 2025, alongside steady dataset growth.Green bars denote new models; purple bars denote novel dataset releases.Source: Figure 3 · p. 4
  • Candidate lists were merged with database results, de-duplicated, and manually validated before inclusion in the review.The validation produced the final set of models, datasets, and simulators.Source: §2 · p. 4

3. Background Concepts

The section introduces Transformers and the components used to process multimodal inputs and generate outputs. It describes self-attention, embeddings, and the distinct roles of the encoder and decoder.

3.1. Transformer

  • The Transformer comprises embedding, encoder, and decoder components, while self-attention lets sequence tokens combine information from one another.Transformers support parallelization and scalability in sequence modeling.Source: §3.1 · p. 4

3.1.1. Self-Attention

  • Self-attention projects inputs into queries, keys, and values, then uses query-key comparisons to weight values into context-aware token representations.Scaled dot-product attention scales the query-key dot product by the key dimension before applying softmax.Source: §3.1.1 · p. 4
  • Multi-head attention computes attention in parallel across learned representation subspaces, concatenating the head outputs before a final linear projection.Each head can focus on different aspects of input relationships.Source: §3.1.1 · p. 4

3.1.2. Embeddings

  • Embeddings map discrete inputs such as words, image patches, or action tokens into a dense latent space, with positional encodings supplying sequence order.Input embeddings provide a unified format for structured data, while output embeddings decode latent representations into action or token spaces.Source: §3.1.2 · pp. 4–5

3.1.3. Encoder

  • The encoder stacks self-attention and position-wise feedforward sublayers, with residual connections and layer normalization in each layer.The first layer uses input embeddings; deeper layers derive their inputs from the preceding layer’s output.Source: §3.1.3 · p. 5

3.1.4. Decoder

  • The decoder combines masked self-attention, encoder-decoder attention, and feedforward layers to generate outputs autoregressively using encoder information.A final linear transformation and softmax produce next-token probabilities.Source: §3.1.4 · p. 5

3.2. Vision Transformers

The Vision Transformer treats image patches as input tokens and uses a Transformer encoder to form visual representations for prediction.

3.2. Vision Transformers

  • ViTs split images into non-overlapping patches, linearly project them into embeddings, add learnable positional encodings, and process them with a Transformer encoder.A classification token is appended, and its final representation feeds an MLP prediction head.Source: §3.2 · p. 5
  • The classification token’s final representation is used for prediction through a multi-layer perceptron head.The architecture also uses learnable positional encodings on the patch embeddings.Source: §3.2 · p. 5
  • ViTs achieve competitive performance on benchmarks such as ImageNet, highlighting attention’s capacity to learn global visual representations.Source: §3.2 · p. 5

3.3. Large Language Models

The section distinguishes language-model architectures by how they process or generate text, then describes vision-language models that align visual and textual inputs. It outlines VLM training objectives and an encoder-decoder captioning example.

3.3. Large Language Models

  • Encoder-only models use bidirectional self-attention and masked language modeling to represent sentence-level meaning for tasks such as classification and question answering.The cited examples include BERT, RoBERTa, and DeBERTa.Source: §3.3 · p. 6
  • Decoder-only models predict each next token from preceding tokens and are optimized for generation, dialogue, summarization, and other open-ended tasks.The cited examples include GPT-3, GPT-4, PaLM, and LLaMA.Source: §3.3 · p. 6
  • Encoder-decoder models represent an input with an encoder and produce an output with a decoder, supporting instruction grounding and language-conditioned action planning in robotics.The passage also identifies translation, summarization, and code generation as applications.Source: §3.3 · p. 6

3.4. Vision Language Models

  • VLMs combine visual and language processing through dual encoders, encoder-decoder frameworks, or sequence-to-sequence stacks.Training commonly aligns visual and textual inputs with contrastive learning, masked modeling, or image-caption generation.Source: §3.4 · p. 6
  • In the captioning example, image patches and a tokenized prompt pass through a Transformer encoder-decoder stack that generates caption tokens autoregressively.The encoder captures image relationships and the decoder generates output tokens; the passage reports strong performance on Flickr8k and Flickr30k.Source: §3.4 · p. 6
  • Captioning architectures may use Swin Transformers, T5-based decoders, or GPT-based language models, and are used in robotic perception systems paired with language generation or instruction.Source: §3.4 · p. 6

4. Vision Language Action Models

This section surveys VLA architectures and components for connecting visual perception, language understanding, and grounded robotic control in instruction-driven autonomy. It covers architectural trends, common component choices and data sources, and alternative decoders and additions aimed at modular, interactive, safe, generalizable, and efficient control.

4.1. VLA Architecture

  • VLA systems encode scene images, language instructions, and robot state in parallel, then fuse their tokens to generate action embeddings and continuous control commands.Representative systems use transformer-based vision and language backbones with cross-modal attention; action generation may use a diffusion transformer or a direct policy.Source: §4.1 · pp. 6–7

4.2. State-of-the-Art VLA Models

  • The survey compares over a hundred models by end-to-end versus component-focused design, contributions, training datasets, and vision, language, and action components.It describes four core components: training dataset, vision encoder, language encoder, and action decoder.Source: §4.2 · p. 7, Table 1 · p. 8

4.3. Architectural Trends

  • VLA components comprise vision encoders, language encoders, and action decoders; vision choices include CLIP/SigLIP for contrastive visual-text alignment, ViTs for long-range spatial dependencies and high-level semantics, and CNNs such as ResNet and EfficientNet.Source: §4.3 · p. 7
  • Chain-of-Affordance uses sequential affordance reasoning for spatial planning and achieves SOTA on LIBERO, while HiRT separates hierarchical planning and control, doubling execution speed and improving dynamic-task success.The survey also notes lightweight edge-optimized inference and Mamba-based unified VLA reasoning as distinct design directions.Source: §4.3 · p. 9
  • +59.1% success over OpenVLA is reported for CogACT, while OpenVLA-OFT achieves 97.1 % success on LIBERO and 26 time speed-up.The passages describe CogACT as a componentized diffusion action transformer and OpenVLA-OFT as optimized OpenVLA fine-tuning with parallel chunked decoding.Source: §4.3 · p. 9
  • Bi-VLA achieved 83.4% real-world task success as a dual-arm instruction-to-action planner grounded in recipe demonstrations, while Shake-VLA achieved 100% success on cluttered cocktail-mixing tasks.Source: §4.3 · pp. 9–10
  • FAST reports up to 15 times faster inference, while a tactical-reasoning VLA reports 50 times faster inference and human-level success.FAST uses frequency-space action tokenization for general robot control; the tactical system operates in 3D ARPG environments.Source: §4.3 · pp. 9–10
  • Selective layer activation in a multi-stage ViT yields 5.6 time faster inference and +8% task success, while UP-VLA reports +33 % success on CALVIN.UP-VLA is described as enabling precise 3D spatial reasoning.Source: §4.3 · p. 10
  • Models target generalization across embodiments and tasks through zero-shot adaptability, household-task training, open-world manipulation, and real-time CPU or embedded-hardware inference.Gemini Robotics is described as supporting long-horizon dexterous manipulation across embodiments; SmolVLA as enabling CPU inference; Gemini Robotics On-Device as providing low-latency control on embedded hardware.Source: §4.3 · p. 10
  • VLA designs add stepwise chain-of-thought, hierarchical planning and control, or decision tokens that switch dynamically between reasoning and acting.Examples include ECoT’s embodied chain-of-thought planning, Hi Robot’s separation of planning and control, and OneTwoVLA’s decision-token switching.Source: §4.3 · pp. 10–11
  • Integration strategies include early fusion of fine-grained visual tokens, interleaved image-text instructions, and insulation layers between vision-language and action modules.The review also describes model-agnostic interleaving and a wrapper that accelerates training and inference while maintaining generalization.Source: §4.3 · p. 11
  • 2.52 times speed-up without sacrificing control fidelity is reported for the first VLA parallel-decoding method with action chunking.The passage identifies this as LeVERB (Xue et al., 2025).Source: §4.3 · p. 11
  • 58.5% success on sim-to-real humanoid demonstrations and 85% success on peg-in-hole are reported for dual-process humanoid control and language-grounded tactile action, respectively.The passages describe the former as TLA (Hao et al., 2025) and the latter as a language-grounded tactile-action model for high-precision contact tasks.Source: §4.3 · p. 11
  • 3.5 times task success over OpenVLA is reported for visual trace prompting that incorporates spatiotemporal cues.The passage identifies the approach as TraceVLA.Source: §4.3 · p. 11
  • Predictive and efficiency-oriented designs include world models for long-horizon planning, successor-feature representations, and token caching to reuse computation across time steps.The cited approaches also include autoregressive trajectory prediction and latent vision-action alignment for foresight-driven policy steering.Source: §4.3 · pp. 11–12
  • Other approaches structure actions through hierarchical skill decomposition, geometric constraints, or universal action dictionaries for compositional planning and policy transfer.HAMSTER (Li et al., 2025c) uses hierarchical skill decomposition; GeoManip encodes geometric constraints as model interfaces; Universal Actions defines a shared action dictionary.Source: §4.3 · pp. 11–12
  • Other designs add atomic-skill composition, customizable speech-driven control, conversational real-time multimodal feedback, or knowledge-grounded mapping from high-level plans to multimodal actions.These additions target modular, data-efficient action composition and interactive control.Source: §4.3 · p. 12
  • Further variants integrate safe-RL constraint feedback for collision-free manipulation, embodied reasoning for UAV flight planning, or diffusion policies for generalizable manipulation across robot embodiments.The review describes these as safety-aware, cognitive, and multimodal VLA designs, respectively.Source: §4.3 · p. 12
  • Language encoders also span T5-style models, GPT- and Qwen-based encoders, Gemma-2B, and specialized CLIP Text encoders, alongside the widely used LLaMA and Vicuna families.The review associates these alternatives with flexible sequence generation, generalization and compact deployment, or minimal alignment tasks.Source: §4.3 · p. 13
  • Action decoding also uses diffusion for temporally smooth control, autoregressive Transformers for responsiveness, MPC or specialized planning heads for dynamic decisions, and MLP or token predictors for efficient low-level control.Examples include Octo, HybridVLA, and DexGraspVLA for diffusion; Gato, OpenDrive VLA, and GRAPE for autoregressive heads.Source: §4.3 · p. 13
  • The unified VLA data format groups visual observations, language instructions, and action signals by episode and serializes them in JSON, TFRecord, or HDF5 for scalable end-to-end training.Figure 9 presents this standardized storage format as enabling efficient data loading.Source: Figure 9 · p. 14
  • Most surveyed VLAs use CLIP/SigLIP ViT vision backbones, LLaMA-family language encoders, and diffusion Transformer action heads; private manipulation demonstrations are common, while Open X-Embodiment is the most-used public dataset.The review also notes that diffusion-based Transformer heads are popular for modeling complex multimodal action distributions.Source: §4.3 · p. 14

5. VLA Training Datasets

The section surveys VLA training data formats and major datasets, then benchmarks datasets by task complexity and modality richness to identify underrepresented combinations.

5.1. Dataset Format

  • VLA training episodes organize visual, language, and action/control streams together, with formats ranging from JSON metadata to TFRecord and HDF5.Visual data may include RGB, video, depth, and segmentation; language streams pair instructions with tokenization metadata, while action labels may be discrete tokens or continuous control vectors.Source: §5.1 · p. 14

5.2. Major VLA Datasets

  • Dataset collections progress from constrained, discrete decision tasks to longer-horizon tasks and then to multimodal control spanning real-world, contact-rich, and open-ended settings.Early examples include EmbodiedQA and R2R; later collections add synchronized sensory streams, haptic feedback, and diverse sensors for long-horizon tasks.Source: §5.2 · pp. 14, 16, Table 2 · p. 15

5.3. Benchamrk VLA Datasets

  • The benchmark scores task complexity and modality richness, maps them to two axes, and uses bubble area to represent dataset scale.Task complexity incorporates action length, skill diversity, sequential dependency, and linguistic abstraction; modality richness combines modality count, quality, temporal alignment, and reasoning-critical annotations.Source: §5.3 · p. 16, Figure 10 · p. 17

5.4. Benchmarking Analysis

  • Only Kaiwu reaches the extreme of both benchmark axes, while the broader dataset landscape leaves highly complex tasks with extensive multimodal integration underrepresented.The review places most datasets from very low to high task complexity and minimal to rich modality; selected datasets such as Iref-VLA, Robo360, TLA, CALVIN, and Open X-Embodiment reach high complexity and rich modality with distinct emphases.Source: §5.3 · p. 16, §5.4 · p. 17, Figure 10 · p. 17

6. Simulation Tools

The section reviews simulation platforms as scalable, controllable sources of annotated VLA data and compares their sensor support, use cases, capabilities, and associated datasets.

Simulation trade-offs

  • Simulation offers scalable and repeatable annotated data beyond what physical collection can feasibly provide, but reliable transfer to real-world deployment remains a challenge.The section describes modern simulators with realistic rendering, physics, and configurable multimodal sensors.Source: §6 · p. 17

Simulation data generation

  • Simulators can generate large numbers of annotated trajectories and support controlled studies of generalization across lighting, object geometry, and task order.Procedural scenes, randomized object properties, scripted behaviors, and GPU-accelerated backends support data synthesis and rapid dataset-design iteration.Source: §6 · pp. 18–19

Simulation platform comparison

  • The simulator comparison organizes platforms by sensor modalities, use cases, technical capabilities, and the datasets that rely on them.Across fifteen simulators, the review presents the table as a reference for comparing physics realism, scalability, and integration with language and control APIs.Source: Table 3 · p. 18, §6 · p. 19

Simulation platform capabilities

  • Platforms differ in sensor suites and strengths: photorealistic environments serve navigation, while contact-rich simulators support force, torque, and haptic feedback for manipulation.The review also describes platforms with LiDAR, IMU, and multi-robot support for reinforcement learning, sim-to-real transfer, and coordination.Source: Table 3 · p. 18, §6 · p. 19

7. Applications and Evaluation of VLAs

The section groups VLA applications into six domains and evaluates representative models using success, zero-shot generalization, and real-robot validation criteria.

7.1. Application Domains

  • The application domains include manipulation and task generalization, mobility, human assistance, robotic platforms, virtual environments, and edge or low-power deployment.The review characterizes manipulation and task generalization as dominant challenges in VLA research.Source: §7.1 · p. 19

7.2. VLA Model Selection and Evaluation

  • The review selects ten models using task coverage, generalization, real-robot validation, architectural novelty, and computational practicality, then applies three standardized evaluation metrics.Success rate is categorized as high, medium, or low; zero-shot capability is estimated from success on unseen tasks, and deployment records physical-hardware validation.Source: §7.2 · p. 19, Table 5 · p. 20
  • Large generalist models are associated with broad zero-shot generalization, while modular and task-specialized systems target robustness and data efficiency for specific manipulation skills.The review concludes that specialized pipelines remain important for bridging sim-to-real gaps and achieving high-precision real-world performance.Source: §7.2 · pp. 20–21

8. Challenges and Future Directions

The section organizes open challenges around VLA architectures, datasets, and simulation, with the aim of advancing robust and generalizable robotic autonomy.

8.1. Architectural Challenges

  • Architectural challenges include aligning heterogeneous tokens and modalities, transferring across embodiments, and generating smooth, safe manipulation trajectories under real-time constraints.The review also notes asynchronous sensory fusion, domain shift, novel robot models, noisy sensors, and coupling diffusion controllers with high-level language planners as unresolved issues.Source: §8.1 · p. 21

8.2. Dataset Challenges

  • Dataset limitations include narrow task coverage, incomplete or poorly synchronized modalities, costly annotations, and a trade-off between real-world fidelity and simulation scale.The review calls for long-horizon cross-domain benchmarks, synchronized multimodal data, lower-cost annotation, and hybrid simulation-real pipelines.Source: §8.2 · pp. 21–22

8.3. Simulation Challenges

  • Simulators provide scalable, controllable training environments, but ensuring simulated performance transfers reliably to real-world deployment remains a challenge.The review frames this as a core simulation limitation for VLA models.Source: §8.3 · p. 22

1. Physics Accuracy and Contact Modeling: Popular physics engines such as MuJoCo, PyBullet, and NVIDIA

The review identifies limitations in simulation physics, rendering, language grounding, and robot support, then outlines improvements for more realistic, scalable, and transferable VLA training data.

1. Physics Accuracy and Contact Modeling: Popular physics engines such as MuJoCo, PyBullet, and NVIDIA

  • Simplified friction and point-contact models omit deformation, friction variability, and joint compliance, contributing to poor real-world transfer and unstable contact behavior.Reported symptoms include object slip and unexpected torque spikes.Source: §1 · p. 22
  • Photorealistic simulators offer diverse visual assets but incur low frame rates and high GPU demand, while lightweight renderers retain texture, lighting, and occlusion gaps.These trade-offs limit large-scale learning or reduce the effectiveness of domain-randomized policies at deployment.Source: §1 · p. 22
  • Most simulators lack native language-grounding APIs, leaving custom instruction-alignment pipelines that add overhead and fragment data formats.The passage cites ALFRED and TEACh as examples of such pipelines.Source: §1 · p. 22
  • Simulator support for robot models varies: flexible URDF or SDF import contrasts with platforms optimized for specific robot families, complicating cross-platform pretraining and reproducibility.The passage names Isaac Sim and Gazebo as flexible-import examples, and Webots and RoboSuite as robot-family-specific examples.Source: §1 · p. 22

8.4. Future Directions

  • Future models are proposed to use modality-aware tokenizers and dynamic fusion, with hierarchical architectures for long video or text sequences.Suggested fusion mechanisms include gating, mixture-of-experts, and conditional attention; lightweight CNN or RNN frontends can downsample inputs before sparse Transformers.Source: §8.4 · pp. 22–23
  • Diffusion-based trajectory generators paired with differentiable safety and collision-avoidance filters are proposed to produce smooth, compliant motions aligned with high-level plans.The passage presents these components as a future direction for trajectory generation.Source: §8.4 · p. 23
  • Proposed dataset directions include procedural long-horizon scenarios, synchronized multimodal capture, automated annotation, and hybrid synthetic-real pipelines.The suggested methods include self-supervised or weakly supervised extraction and neural rendering with physics-aware domain randomization.Source: §8.4 · p. 23
  • Simulator improvements proposed include calibrated multi-scale contact physics, hybrid rendering, language-grounding APIs, and shared multi-robot protocols.The passage describes combining high-throughput rasterization with neural or ray-traced rendering and supporting URDF/SDF autoimport.Source: §8.4 · p. 23

9. Conclusion

The conclusion organizes VLA research across architectures, datasets, simulation, and evaluation, noting progress alongside persistent challenges in scaling, transfer, and safety-critical transparency. It calls for composable learning, data-efficient adaptation, and standardized benchmarking and deployment.

9. Conclusion

  • VLA research is evolving toward multimodal, instruction-conditioned agents that generalize across tasks, embodiments, and environments.The review organizes the landscape across architectural design, dataset ecosystems, simulation platforms, and evaluation methodologies.Source: §9 · p. 23
  • Scalable pretraining, reliable simulation-to-physical transfer, and transparent decisions in safety-critical contexts remain critical challenges.The conclusion describes these as continuing challenges despite significant progress.Source: §9 · p. 23
  • The review calls for composable learning systems, data-efficient adaptation, and standardized benchmarking and deployment procedures.It frames these as needed to address the stated limitations and advance real-world applications.Source: §9 · p. 23

Declaration

The authors disclose using ChatGPT during preparation of the work and state that they reviewed and edited the content and take full responsibility.

Declaration

  • The authors report using ChatGPT while preparing the work.The stated purposes were refining English and searching for VLA model names.Source: Declaration · p. 23
  • The authors say they reviewed and edited the content after using the tool.Source: Declaration · p. 23
  • The authors state that they take full responsibility for the publication's content.Source: Declaration · p. 23

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