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REVIEW 4 major objections 5 minor 31 references

VANE: Reliable Test-Time Training for Vision-Language-Action Models via Future Visual Representation Prediction

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read VANE claims that VLA test-time training becomes reliable when prompt updates are gated by attention and validated against future visual frames before deployment.

desk verdict A genuinely careful TTT-for-VLA paper whose headline gain is probably within measurement noise; worth refereeing, but the central claim needs seeds, error bars, and ideally code before it can support the 'reliable' label. read the letter →

arxiv 2608.09448 v1 pith:Y4GJXI3S submitted 2026-08-10 cs.RO cs.CV

Hongjin Ji , Guoyang Xia , Luoyang Sun , Fangxiang Feng , Lei Ren This is my paper · ORCID
classification cs.ROcs.CV
keywords test-timetrainingvision-language-actionmodelslatentpromptoptimizationfuturevisualrepresentationpredictionrobotmanipulationclosed-loopadaptationattentiongatingvalidation-drivenupdates
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that test-time training of a frozen vision–language–action policy can be made dependable in closed-loop manipulation, not just feasible. The proposed VANE framework adapts only a small bank of latent prompts, predicts how the visual scene will look after the robot acts, and refuses to keep an update unless later observations confirm that it helped. If the central claim is right, robots deployed in new settings could improve from ordinary unlabeled video streams instead of requiring new demonstrations, fine-tuning runs, or reward labels. The headline evidence is on SimplerEnv WidowX, where VANE reaches 71.2% average success, 3.2 points above its matched TTT baseline and 3.8 points above the published TTT-VLA result; the Google Robot results are task-dependent, so the authors frame the gain as scoped rather than universal.

What carries the argument

The load-bearing object is a latent-prompt bank of eight prompt vectors with a frozen linear router that selects the top two prompts per observation, so the effective prompt is a sparse, context-dependent composition rather than a single shared prompt. WPI uses a frozen video-representation encoder to produce present and delayed visual tokens; a Latent-Action DiT decodes the present tokens into the delayed representation using shared attention with action tokens, yielding a label-free future-prediction loss that is optimized only through the prompt bank at deployment. AGV-TTT monitors cross-layer action-to-VLM and action-to-predictive-latent attention; an anomalous change triggers one shadow AdamW step on a focused loss over high-attention visual tokens, and acceptance requires that over a validation horizon the focused loss improves, the global loss does not degrade, and a majority of matched future pairs favor the candidate. The live prompt and optimizer state are restored until atomic commit, which makes the adaptation selective and reversible.

What would settle it

Run the AGV-TTT protocol on WidowX with the validation criterion inverted, accepting candidates whose future-prediction loss worsens and rolling back those that improve, while keeping the same event-gated shadow proposals. If success does not drop below the 69.0% result of the validated protocol, the future-prediction signal is not doing the work attributed to it.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central discovery is that three standard failure modes of VLA test-time training—shared prompts entangling incompatible tasks, same-time proxies ignoring interaction outcome, and online updates that degrade the live policy before being evaluated—can be addressed by one integrated design. A context-routed Mixture of Latent Prompts (MoLP) keeps a shared bank of latent prompts and composes the effective prompt by top-2 routing on the current vision–language context. The World-Predictive Interface (WPI) replaces state grounding with prediction of delayed visual representations from a frozen encoder, so the proxy is label-free, cross-time, and action-conditioned. Attention-Gated and Validation-Driven TTT (AGV-TTT) proposes a prompt update on a shadow copy only when cross-layer attention redistributes at an interaction transition, then commits it only if, over matched future pairs, the focused prediction loss improves, the global loss does not degrade, and a majority of pairs favor it. The reported result is 71.2% average success on WidowX for QwenWPI + MoLP + AGV-TTT, with the explicit caveat that on Google Robot the same configuration does not consistently beat state grounding, which the paper treats as evidence that deployment-time gains remain task- and embodiment-dependent.

Load-bearing premise

The load-bearing premise is that reducing the error between the prompt's predicted future visual representation and the actual delayed observation is a trustworthy proxy for improving task success; if future-prediction quality does not track action quality, the entire commit-or-rollback validation collapses.

Editorial extensions

If this is right

  • Deployment-time adaptation of frozen VLA policies no longer requires task-specific fine-tuning or an externally supplied task identifier: the router composes corrections from a shared prompt bank and the future-validation step filters them.
  • Future validation is selective: on the single-prompt QwenWPI study only 19.4% of proposed updates are accepted, so most candidates never reach the live controller.
  • Event gating cuts backward optimization dramatically, from 45,824 to 612 backward passes per checkpoint evaluation on the studied configuration, making online adaptation feasible inside a closed loop.
  • The Google Robot scope test implies that gains from predictive-prompt TTT are not guaranteed across embodiments and task families, so deployment should include a task- and embodiment-specific check rather than assuming uniform improvement.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, the shadow-proposal plus future-validation protocol may be the portable contribution: it could be paired with any label-free proxy, such as state grounding, optical flow, or action-free video embeddings, and the WPI target might not be essential to the validation gain.
  • Beyond the paper, the acceptance rule implicitly assumes that future-prediction loss is monotonically related to task success; a testable extension would calibrate the validation threshold on a small labeled set or reweight validation pairs by per-token uncertainty.
  • Beyond the paper, the paired-randomness construction isolates whether a candidate prompt is genuinely better than the live prompt rather than lucky, which could be reused as a general diagnostic for online policy updates.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces VANE, a test-time training (TTT) framework for vision-language-action (VLA) policies, combining a context-routed Mixture of Latent Prompts (MoLP), a future visual representation prediction objective called the World-Predictive Interface (WPI), and an attention-gated, validation-driven update protocol (AGV-TTT). The central claim is that on the SimplerEnv WidowX benchmark, the complete VANE configuration improves average success by 3.2 percentage points over the corresponding TTT baseline (71.2% vs. 68.0% for QwenWPI + FP-LP + TTT in Table 3), and by 3.8 points over the published TTT-VLA result. On Google Robot, the paper reports that gains are task- and embodiment-dependent and frames the results as a boundary/scope test. The paper explicitly separates diagnostic observations from efficacy claims and averages over four checkpoints without success-based checkpoint selection.

Significance. If the WidowX improvement were statistically robust, VANE would represent a useful step toward more reliable test-time adaptation of VLA policies: the idea of composing task-dependent corrections from a shared prompt bank, using frozen future representations as label-free supervision, and gating updates by attention events is well motivated and experimentally tractable. The controlled factorial comparison (Table 6), the explicit separation of diagnostic and efficacy evidence, and the checkpoint-averaging without selection are examples of good experimental hygiene. However, the load-bearing success claim currently rests on point estimates from a single evaluation pass per checkpoint without confidence intervals, multiple seeds, or a common-random-number rollout protocol. The validation mechanism also evaluates candidates under a fixed zero-action anchor rather than under the candidate's own actions, which weakens the mechanistic interpretation of 'future validation.' These issues must be addressed before the headline claims can be considered reliable.

major comments (4)
  1. [§4.2.1, Tables 3 and 6] The central WidowX claim (71.2% vs. 68.0%, a 3.2-point gain) is presented as a single point estimate per condition. Section 4.2.1 explicitly states that no common-random-number rollout protocol is enforced across methods. For a four-task benchmark with typically tens of episodes per task, the standard error of a 70% success rate is on the order of 3-7 percentage points per task, so the reported difference is plausibly within evaluation noise. Please report per-checkpoint success rates, bootstrap confidence intervals, or results from multiple independent rollout seeds, and ideally use seed-matched rollouts for the paired comparisons (e.g., WPI-MoLP-AGV vs. WPI-FP-LP-TTT). This is load-bearing because the abstract and conclusion rest on this 3.2-point difference.
  2. [§3.4, Eq. (8) and Appendix C] The validation criterion accepts a candidate only when it improves the WPI loss (Eq. 5), i.e., the same objective used to propose the update. This creates a circularity: the mechanism selects candidates that optimize the proxy, not necessarily task success. More importantly, the validation is performed by feeding both old and candidate prompts the same observed inputs and a fixed 'zero action anchor,' not by letting the candidate generate actions (Appendix C, 'Paired future validation'). Thus the 'future evidence' does not reflect the candidate's effect on the closed-loop trajectory. The paper should either provide direct evidence that WPI loss improvement on held-out future pairs correlates with task success (e.g., across checkpoints or prompt variants), or substantially soften the claim that AGV-TTT validates candidates based on their future consequences.
  3. [§4.2.3, Table 6] The controlled factorial comparison shows that, for the WPI-MoLP configuration, AGV-TTT improves over the No-TTT baseline by only 0.5 points (70.7% to 71.2%). The headline 3.2-point gain over 'the corresponding TTT baseline' (Table 3) is therefore largely attributable to the MoLP prompt structure and the WPI interface at training time, not to the test-time training protocol itself. The paper does acknowledge this in the text, but the abstract's phrasing is likely to be misread. Please clarify in the abstract and conclusion that the improvement over the TTT baseline is an end-to-end comparison that changes both the prompt structure and the update protocol, and that the isolated contribution of AGV-TTT is considerably smaller.
  4. [§4.2.4, Table 8] The ablation isolating future validation reports a 1.7-point gain (67.3% for Event+Accept-All vs. 69.0% for AGV-TTT), but this is again a single point estimate without confidence intervals or seed-matched rollouts. Given the small absolute difference, the conclusion that 'future validation' filters unfavorable candidates is not statistically established. Please provide variance information or additional seeds for this key ablation.
minor comments (5)
  1. [Abstract] The abstract contains spacing artifacts (e.g., 'T raining' in the first sentence). Please proofread the final PDF rendering.
  2. [§3.4] The attention-gated proposal mechanism depends on hyperparameters such as the top-K token count (stated as 32 in Appendix C) and the validation horizon Hv = 4. These are important for reproducibility; consider stating them in the main text rather than only in the appendix.
  3. [§3.3, Eq. (5)] The WPI loss is defined as a mean squared Frobenius norm but the future offset k is not explicitly included in the normalization; clarify whether the loss is averaged over the k-step difference or over the single delayed frame.
  4. [§4.1.3] The MMD analysis is labeled as descriptive and not used to predict TTT gains. This is appropriate, but the connection to the paper's claims is weak; consider shortening or moving it to supplementary material unless it is used to bound the generalizability claim.
  5. [§5] The conclusion states that AGV-TTT is 'the only evaluated protocol that improves checkpoint-averaged performance across all four proxy–prompt configurations.' This is true only for the WidowX benchmark; the Google Robot table (Table 7) shows a degradation for State-MoLP. Please qualify the statement to avoid overgeneralization.

Circularity Check

1 steps flagged · score 2.0 of 10

Partial self-referential validation loop in AGV-TTT; the headline success claims remain externally grounded, so the paper is not significantly circular.

  1. other [Section 3.4, Eqs. (7)-(8), using e_{t,n} from Eq. (5)]
    "With token-wise future-prediction error e_{t,n}, the proposal loss is L_{focus,t} = Σ_{n∈K_t} F_{t,n} e_{t,n}, P_{cand} = Update(P_{old}; L_{focus,t}) (7). ... Let R_{focus} and R_{global} denote their relative improvements in the focused and all-token prediction losses, respectively. A candidate is accepted only when R_{focus} > 0, R_{global} ≥ 0, Σ_j I[R^{(j)}_{focus} > 0] > H_v/2 (8)."

    The same token-wise future-prediction error e_{t,n} defines the WPI training loss (Eq. 5), the proposal loss (Eq. 7), and the acceptance metrics (Eq. 8). Thus the 'future validation' that decides whether a candidate is committed is a self-consistency check on the proxy objective, not an independent source of evidence about task success. A candidate is accepted precisely when it continues to minimize the loss it was trained on, on held-out future pairs. This is a mild self-referential loop: the validation criterion does not add information beyond the proxy's generalization. It is not load-bearing for the headline 3.2-point success gain, which comes from external SimplerEnv rollouts, so this is partial rather than fatal circularity.

full rationale

The paper's central success claims are benchmark rollouts in SimplerEnv WidowX and Google Robot, produced by executing the adapted policy and measuring task completion; they are not outputs of the WPI loss. The WPI objective is used both to propose prompt updates and to gate commits, which is a self-consistency check, but the claimed 3.2-point improvement over the TTT baseline is an external measurement. The paper does not lean on a load-bearing self-citation: references to TTT-VLA, V-JEPA 2, and StarVLA-alpha are external prior work with no author overlap, and the latent-prompt visibility strategy is transparently borrowed rather than presented as a derived theorem. Methodological caveats—no common-random-number rollout protocol, point estimates without confidence intervals, and task-dependent Google Robot results—are statistical-robustness concerns, not circularity. The proxy-relevance assumption (future-representation prediction transfers to success) is treated empirically through matched comparisons and is openly bounded on Google Robot, so the derivation chain does not reduce to its inputs by construction.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

No new physical entities such as particles, forces, or dimensions are postulated. The MoLP prompt bank, WPI interface, and AGV-TTT protocol are method components with external benchmark evidence, not entities pulled from a hat. The main free parameters are hyperparameters of the adaptation space and validation rule, several of which lack sensitivity analysis.

free parameters (7)
  • Latent prompt bank size E=8 and top-k K=2 = E=8, K=2
    Chosen without sensitivity analysis; defines the capacity and sparsity of the adaptation space in Eq. 3.
  • Future prediction offset k = k=8
    Set in Appendix B; controls the delay and difficulty of the future visual target.
  • Validation horizon Hv = Hv=4 for WPI, Hv=3 for state-grounded
    Majority vote over Hv paired comparisons in Eq. 8; no ablation on Hv.
  • Attention focus token count and layer set = Top 32 tokens; last 4 or 8 DiT layers
    Defines the focused proposal loss in Eq. 7; values are design choices.
  • WPI and load-balancing loss weights = lambda_wpi=0.1, lambda_lb=0.01
    Weights in Eq. 11; no reported sensitivity analysis.
  • Test-time learning rates = Offline 1e-5, Online/AGV 1e-7
    Prompt-only AdamW step sizes; chosen per protocol without reported tuning curves.
  • Event gate robust thresholds = Not specified numerically
    Eq. 12 requires median/MAD robust thresholds plus an absolute threshold, but Appendix C does not give the numeric values.
assumptions (5)
  • domain assumption Optimizing only the latent prompt bank, with router and policy frozen, can meaningfully adapt a VLA policy at deployment.
    Invoked throughout Sec. 3.2 and Appendix B; if prompt-only updates cannot alter action-relevant behavior, the entire TTT mechanism fails.
  • domain assumption Minimizing future V-JEPA visual representation prediction error (Eq. 5) is a policy-relevant proxy that transfers to task success.
    Core proxy assumption in Sec. 3.3; the validation rule in Eq. 8 uses the same loss.
  • domain assumption Cross-layer attention redistribution detects interaction-relevant moments worth proposing an update.
    Sec. 3.4 Eq. 6; the paper itself calls the supporting visualization illustrative, not a precision/recall benchmark (Sec. 4.1.2).
  • domain assumption Future validation with identical zero action anchors and WPI loss predicts whether a candidate is safe to deploy.
    Appendix C: no expert action is available, so both branches use the same zero action anchor; this ignores action quality differences.
  • standard math Standard flow-matching training objective (Eqs. 9-10) and AdamW optimizer behave as expected for prompted policy optimization.
    Background machinery adopted from prior work; not derived in this paper.

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Cite this review

Pith. "Pith review of VANE: Reliable Test-Time Training for Vision-Language-Action Models via Future Visual Representation Prediction." pith.science (2026). https://pith.science/paper/Y4GJXI3S

@misc{pith2026260809448,
  author       = {Pith},
  title        = {Pith review of: VANE: Reliable Test-Time Training for Vision-Language-Action Models via Future Visual Representation Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y4GJXI3S}},
  note         = {Machine review of arXiv:2608.09448}
}
abstract

Test-time training (TTT) offers a lightweight way to adapt vision--language--action (VLA) policies from unlabeled deployment streams, but it remains difficult to use reliably in closed-loop manipulation. A shared adaptation space can mix incompatible task corrections, while an online update can alter subsequent actions before its consequences are known. We introduce a reliable TTT framework for VLA policies (VANE). VANE conditions prompt adaptation on the current vision--language context and learns from the future visual consequences of executed actions. Candidate updates are isolated from the live policy, evaluated on subsequent observations, and committed only when supported by future evidence, making adaptation selective and reversible. On SimplerEnv WidowX, VANE improves average success by $3.2$ percentage points over the corresponding TTT baseline. Results on Google Robot further show that deployment-time gains remain task- and embodiment-dependent. Together, these results demonstrate a constrained, evidence-based approach to adapting VLA policies during interaction.

Figures

Figures reproduced from arXiv: 2608.09448 by the authors.

Figure 1
Figure 1. Overview of VANE. (a) MoLP composes a context-dependent latent prompt from a shared prompt bank [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Policy-internal cross-layer attention changes around interaction transitions. (a) RGB observations (upper [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

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Works this paper leans on

31 extracted references · 9 canonical work pages

  1. [1]

    RT-2: Vision-language-action models transfer web knowledge to robotic control,

    A. Brohanet al., “RT-2: Vision-language-action models transfer web knowledge to robotic control,”arXiv preprint arXiv:2307.15818, 2023

    arXiv 2023

  2. [2]

    OpenVLA: An open-source vision-language-action model,

    M. J. Kimet al., “OpenVLA: An open-source vision-language-action model,”arXiv preprint arXiv:2406.09246, 2024

    arXiv 2024

  3. [3]

    π0: A vision-language-action flow model for general robot control,

    K. Blacket al., “ π0: A vision-language-action flow model for general robot control,”arXiv preprint arXiv:2410.24164, 2024

    arXiv 2024

  4. [4]

    StarVLA-α: Reducing complexity in vision-language-action systems,

    J. Ye, N. Gao, S. Yang, J. Zheng, Z. Wang, Y . Chen, P. Chen, Y . Chen, S. Liu, and J. Jia, “StarVLA-α: Reducing complexity in vision-language-action systems,”arXiv preprint arXiv:2604.11757, 2026

    arXiv 2026

  5. [5]

    Test-time training with self-supervision for generalization under distribution shifts,

    Y . Sun, X. Wang, Z. Liu, J. Miller, A. A. Efros, and M. Hardt, “Test-time training with self-supervision for generalization under distribution shifts,” inProceedings of the 37th International Conference on Machine Learning, 2020, pp. 9229–9248

    work page 2020

  6. [6]

    TTT-VLA: Test-Time Latent Prompt Optimization for Vision-Language-Action Models

    W. Zhang, J. Li, S. Yang, S. Chen, J. Liu, L. Liu, and X. Ma, “TTT-VLA: Test-time latent prompt optimization for vision-language-action models,”arXiv preprint arXiv:2606.03127, 2026

    work page Pith review arXiv 2026

  7. [7]

    RT-1: Robotics transformer for real-world control at scale,

    A. Brohanet al., “RT-1: Robotics transformer for real-world control at scale,”arXiv preprint arXiv:2212.06817, 2022

    arXiv 2022

  8. [8]

    Open X-Embodiment: Robotic learning datasets and RT-X models,

    Open X-Embodiment Collaboration, A. O’Neillet al., “Open X-Embodiment: Robotic learning datasets and RT-X models,”arXiv preprint arXiv:2310.08864, 2023

    arXiv 2023

Show all 31 references
  1. [9]

    Octo: An open-source generalist robot policy,

    Octo Model Team, D. Ghoshet al., “Octo: An open-source generalist robot policy,”arXiv preprint arXiv:2405.12213, 2024

    2024 arXiv

  2. [10]

    V-JEPA 2: Self-supervised video models enable understanding, prediction and planning,

    M. Assranet al., “V-JEPA 2: Self-supervised video models enable understanding, prediction and planning,”arXiv preprint arXiv:2506.09985, 2025

    2025 arXiv

  3. [11]

    FutureVLA: Joint visuomotor prediction for vision-language-action model,

    X. Xu, H. Li, J. Ye, Y . Chen, J. Zeng, X. Chen, L. Xu, D. Lin, W. Li, and J. Pang, “FutureVLA: Joint visuomotor prediction for vision-language-action model,”arXiv preprint arXiv:2603.10712, 2026

    2026

  4. [12]

    Causal world modeling for robot control,

    L. Li, Q. Zhang, Y . Luo, S. Yang, R. Wang, F. Han, M. Yu, Z. Gao, N. Xue, X. Zhu, Y . Shen, and Y . Xu, “Causal world modeling for robot control,”arXiv preprint arXiv:2601.21998, 2026

    2026 arXiv

  5. [13]

    Tent: Fully test-time adaptation by entropy minimization,

    D. Wang, E. Shelhamer, S. Liu, B. Olshausen, and T. Darrell, “Tent: Fully test-time adaptation by entropy minimization,”arXiv preprint arXiv:2006.10726, 2020

    2006 arXiv

  6. [14]

    Continual test-time domain adaptation,

    Q. Wang, O. Fink, L. Van Gool, and D. Dai, “Continual test-time domain adaptation,”arXiv preprint arXiv:2203.13591, 2022

    2022 arXiv

  7. [15]

    Efficient test-time model adaptation without forgetting,

    S. Niu, J. Wu, Y . Zhang, Y . Chen, S. Zheng, P. Zhao, and M. Tan, “Efficient test-time model adaptation without forgetting,”arXiv preprint arXiv:2204.02610, 2022

    2022 arXiv

  8. [16]

    Test-time training on nearest neighbors for large language models,

    M. Hardt and Y . Sun, “Test-time training on nearest neighbors for large language models,” inInternational Conference on Learning Representations, 2024

    2024

  9. [17]

    Test-time prompt tuning for zero-shot generalization in vision-language models,

    M. Shu, W. Nie, D.-A. Huang, Z. Yu, T. Goldstein, A. Anandkumar, and C. Xiao, “Test-time prompt tuning for zero-shot generalization in vision-language models,” inAdvances in Neural Information Processing Systems, 2022. 12 V ANE

    2022

  10. [18]

    Test-time training for visual foresight vision-language- action models,

    S. Park, W. Kim, Y . In, S. Kim, H. Kang, and C. Park, “Test-time training for visual foresight vision-language- action models,”arXiv preprint arXiv:2605.08215, 2026

    2026 arXiv

  11. [19]

    On-the-fly VLA adaptation via test-time reinforcement learning,

    C. Liu, Y . Liu, T. Wang, Q. Zhuang, J. C. Liang, W. Yang, R. Xu, Q. Wang, D. Liu, and C. Han, “On-the-fly VLA adaptation via test-time reinforcement learning,”arXiv preprint arXiv:2601.06748, 2026

    2026 arXiv

  12. [20]

    BridgeData V2: A dataset for robot learning at scale,

    H. Walke, K. Black, A. Lee, M. J. Kim, M. Du, C. Zheng, T. Zhao, P. Hansen-Estruch, Q. Vuong, A. He, V . Myers, K. Fang, C. Finn, and S. Levine, “BridgeData V2: A dataset for robot learning at scale,”arXiv preprint arXiv:2308.12952, 2023

    2023 arXiv

  13. [21]

    Evaluating real-world robot manipulation policies in simulation,

    X. Li, K. Hsu, J. Gu, K. Pertsch, O. Mees, H. R. Walke, C. Fu, I. Lunawat, I. Sieh, S. Kirmani, S. Levine, J. Wu, C. Finn, H. Su, Q. Vuong, and T. Xiao, “Evaluating real-world robot manipulation policies in simulation,”arXiv preprint arXiv:2405.05941, 2024

    2024 arXiv

  14. [22]

    SpatialVLA: Exploring spatial representations for vision-language-action model,

    D. Qu, H. Song, Q. Chen, Y . Yao, X. Ye, Y . Ding, Z. Wang, J. Gu, B. Zhao, D. Wang, and X. Li, “SpatialVLA: Exploring spatial representations for vision-language-action model,”arXiv preprint arXiv:2501.15830, 2025

    2025 arXiv

  15. [23]

    Magma: A foundation model for multimodal AI agents,

    J. Yang, R. Tan, Q. Wu, R. Zheng, B. Peng, Y . Liang, Y . Gu, M. Cai, S. Ye, J. Jang, Y . Deng, and J. Gao, “Magma: A foundation model for multimodal AI agents,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025, pp. 14 203–14 214

    2025

  16. [24]

    Towards generalist robot policies: What matters in building vision-language-action models,

    X. Li, P. Li, M. Liu, D. Wang, J. Liu, B. Kang, X. Ma, T. Kong, H. Zhang, and H. Liu, “Towards generalist robot policies: What matters in building vision-language-action models,”arXiv preprint arXiv:2412.14058, 2024

    2024 arXiv

  17. [25]

    InstructVLA: Vision-language-action instruction tuning from understanding to manipulation,

    S. Yang, H. Li, B. Wang, Y . Chen, Y . Tian, T. Wang, H. Wang, F. Zhao, Y . Liao, and J. Pang, “InstructVLA: Vision-language-action instruction tuning from understanding to manipulation,”arXiv preprint arXiv:2507.17520, 2025

    2025

  18. [26]

    CogACT: A foundational vision-language-action model for synergizing cognition and action in robotic manipulation,

    Q. Li, Y . Liang, Z. Wang, L. Luo, X. Chen, M. Liao, F. Wei, Y . Deng, S. Xu, Y . Zhang, X. Wang, B. Liu, J. Fu, J. Bao, D. Chen, Y . Shi, J. Yang, and B. Guo, “CogACT: A foundational vision-language-action model for synergizing cognition and action in robotic manipulation,”ar...

    2024 arXiv

  19. [27]

    ThinkAct: Vision-language-action reasoning via reinforced visual latent planning,

    C.-P. Huang, Y .-H. Wu, M.-H. Chen, Y .-C. F. Wang, and F.-E. Yang, “ThinkAct: Vision-language-action reasoning via reinforced visual latent planning,” inAdvances in Neural Information Processing Systems, 2025

    2025

  20. [28]

    TraceVLA: Visual trace prompting enhances spatial-temporal awareness for generalist robotic policies,

    R. Zheng, Y . Liang, S. Huang, J. Gao, H. Daumé III, A. Kolobov, F. Huang, and J. Yang, “TraceVLA: Visual trace prompting enhances spatial-temporal awareness for generalist robotic policies,” inInternational Conference on Learning Representations, 2025

    2025

  21. [29]

    Emma-X: An embodied multimodal action model with grounded chain of thought and look-ahead spatial reasoning,

    Q. Sun, P. Hong, T. D. Pala, V . Toh, U.-X. Tan, D. Ghosal, and S. Poria, “Emma-X: An embodied multimodal action model with grounded chain of thought and look-ahead spatial reasoning,”arXiv preprint arXiv:2412.11974, 2024

    2024 arXiv

  22. [30]

    FAST: Efficient action tokenization for vision-language-action models,

    K. Pertsch, K. Stachowicz, B. Ichter, D. Driess, S. Nair, Q. Vuong, O. Mees, C. Finn, and S. Levine, “FAST: Efficient action tokenization for vision-language-action models,” inProceedings of Robotics: Science and Systems, 2025

    2025

  23. [31]

    GR00T N1: An open foundation model for generalist humanoid robots,

    NVIDIA, J. Bjorck, F. Castaneda, N. Cherniadev, X. Da, R. Ding, L. Fan, Y . Fang, D. Foxet al., “GR00T N1: An open foundation model for generalist humanoid robots,”arXiv preprint arXiv:2503.14734, 2025. 13 V ANE APPENDIX A. Notation Symbol Definition ot, ℓRGB observation and l...

    2025 arXiv

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