Pith. sign in

REVIEW 4 major objections 6 minor 38 references

Spatial Traces: Enhancing VLA Models with Spatial-Temporal Understanding

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Spatial Traces overlays keypoint motion on predicted depth maps and reports a 4% mean success-rate gain over SpatialVLA in SimplerEnv with only 52 training trajectories.

desk verdict Useful low-cost visual prompting trick, but the headline gain is within the noise of a four-task evaluation with no error bars and internally inconsistent numbers. read the letter →

arxiv 2508.09032 v1 pith:XT75VE5S submitted 2025-08-12 cs.CV cs.AIcs.RO

Maxim A. Patratskiy , Alexey K. Kovalev , Aleksandr I. Panov This is my paper · ORCID
classification cs.CVcs.AIcs.RO
keywords vision-language-actionmodelsspatial-temporalunderstandingvisualpromptingdepthmapskeypointtrajectoriesrobotmanipulationfew-shotfine-tuningSimplerEnv
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 show that a Vision-Language-Action (VLA) model can gain spatial and temporal awareness at once by overlaying tracked keypoint motion onto predicted depth maps. Prior methods either supply depth or motion traces, but not both; the proposed ST-VLA combines them into a single visual prompt. If the claim holds, robot manipulation policies can improve without large fine-tuning datasets, since the reported gains come from only 52 real-robot trajectories. The central evidence is a mean success rate of 38.1% in SimplerEnv, against 34.1% for SpatialVLA and 19.9% for TraceVLA, with mean goal-condition success rising to 64.8%.

What carries the argument

The load-bearing object is the depth-trace composite: a predicted depth map onto which the 2D trajectories of tracked key points are painted, each trace pixel taking the depth of the nearest object in the current frame. This single image carries both where objects are (depth) and where the manipulator has been (trajectory), so the model can read spatial structure and motion history from one visual prompt. The trace predictor CoTracker produces keypoint sequences over the observation buffer, ZoeDepth supplies the metric depth map, and the Ego3D positional encoder, originally from SpatialVLA, embeds the composite so that adding it to the SigLIP observation embedding injects spatial-temporal information into the policy.

What would settle it

On the same SimplerEnv tasks and seeds, run ST-VLA with the trace overlay corrupted (for example, random trace positions) and with the depth channel replaced by a constant, keeping the identical 52-trajectory fine-tuning; if mean success does not drop from 38.1% toward the SpatialVLA level of 34.1%, the depth-trace prompt is not the operative ingredient. A complementary check is to remove only the trace overlay while keeping depth, which should reproduce the drop reported in the paper's Table IV.

Watch

Extended reading notes

Core claim

The paper claims that projecting keypoint trajectories onto depth maps gives a VLA policy both spatial structure and motion history from one image, and that this improves manipulation performance. Concretely, ST-VLA tracks key points over a buffer of up to 30 previous observations with CoTracker, estimates a depth map with ZoeDepth, and paints each trace onto the depth map so that trace pixels take the depth of the nearest object in the current frame. This depth-trace image is encoded with the Ego3D positional encoder from SpatialVLA and summed with the SigLIP observation embedding before being fed to a PaliGemma2-based VLA model. After fine-tuning on 52 Bridge trajectories with LoRA, ST-VLA reaches 38.1% mean success rate in SimplerEnv versus 34.1% for SpatialVLA and 19.9% for TraceVLA. Ablations show the gain comes from the trace prompting itself, that longer trace history (30 frames) is more reliable, and that rendering traces at object depth beats rendering them at the manipulator's historical depth.

Load-bearing premise

The method only helps when the off-the-shelf keypoint tracker and depth estimator that generate its visual prompt give informative signals; when consecutive frames barely change, traces become short or absent and the claimed spatial-temporal benefit disappears.

Editorial extensions

If this is right

  • On SimplerEnv Bridge tasks, adding spatial traces raises mean success rate to 38.1% from 34.1% for SpatialVLA and from 19.9% for TraceVLA, with mean goal-condition success at 64.8%.
  • The improvement is achieved with a minimal fine-tuning set: 52 Bridge trajectories, 1,969 steps, and two hours of LoRA training on a single GPU.
  • The trace prompting itself, not the Bridge fine-tuning, drives the gain: direct fine-tuning without traces lowers mean success by 10%, while zero-shot traces lower it by only 6.8%.
  • Longer interaction history helps: a 30-image observation buffer gives more consistent results than 7 or 15, especially on spatially demanding tasks such as stacking blocks.
  • Trace rendering method matters: assigning trace pixels the depth of the nearest object in the current frame outperforms assigning them their historical manipulator depth.

Reading between the lines

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

  • Because the method only changes the visual input, the same depth-trace overlay could plausibly be applied to other VLA architectures that consume RGB or RGB-D images, not only the PaliGemma2-based SpatialVLA used here.
  • The identified failure mode (short or absent traces during slow gripper rotation) suggests that an adaptive trace generator that lengthens traces when motion between frames is small could close the remaining gap in rotation-heavy tasks.
  • If depth estimation improves, the approach might benefit disproportionately, since trace pixels inherit their values from the current depth map; uncertain depth may be a main ceiling on the reported 38.1% result.
  • The 52-trajectory result implies that for spatial-temporal VLA fine-tuning the bottleneck is the format of the visual prompt rather than data volume; that is testable by comparing trace-based prompting against equal fine-tuning budgets on other task suites.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes ST-VLA, a modification of a Vision-Language-Action (VLA) model in which keypoint traces computed by CoTracker are overlaid onto a depth map estimated by ZoeDepth, and the resulting depth-traces embedding is added to the RGB observation embedding before action prediction. The model is initialized from SpatialVLA/PaliGemma2 and fine-tuned with LoRA on only 52 Bridge trajectories. On four SimplerEnv tasks, the authors report a mean success rate of 38.1% for ST-VLA versus 34.1% for SpatialVLA and 19.9% for TraceVLA, and they present ablations over buffer size (Q2), trace rendering variant (Q3), and fine-tuning attribution (Q4). The central claim is that overlaying visual traces on depth maps simultaneously improves spatial and temporal understanding with very little fine-tuning data.

Significance. If the reported improvement is robust, the method is a simple and practically attractive visual-prompting addition to existing VLA models: it uses off-the-shelf components, requires only a small amount of fine-tuning data, and the paper includes several useful control experiments. The strengths are the clear architectural idea, the explicit hyperparameters and training setup, and the attempt to isolate the contribution of traces from fine-tuning in Q4. However, the current evidence is not sufficient to support the headline claim. There are no error bars or repeated-seed results, the effect size is comparable to one or two successful episodes on this evaluation grid, and the paper contains internally inconsistent aggregate numbers. The contribution is therefore interesting and worth pursuing, but the empirical case needs substantial revision before the claimed gains can be accepted.

major comments (4)
  1. [VI (Table I vs. Figure 4)] The aggregate numbers are internally inconsistent. Averaging the TraceVLA column in Table I gives a mean GCS of 59.1% and a mean SR of 22.7%, but Figure 4 reports 51.1% and 19.9% for TraceVLA. If Table I is correct, the reported improvement over TraceVLA shrinks from about 18 percentage points to about 15 percentage points in SR; if Figure 4 is correct, Table I is wrong. Such an inconsistency undermines the reliability of the headline comparison and must be resolved by the authors.
  2. [V-C and VI (Tables I and IV)] No repeated seeds or confidence intervals are reported, and the evaluation is too coarse to resolve the claimed effect. In Table I, per-task success rates are multiples of 9.1, which implies about 11 episodes per task; one additional successful episode changes a task's SR by 9.1 points and the four-task mean by about 2.3 points. The reported +4.0 mean SR over SpatialVLA therefore corresponds to roughly two successful episodes total. Without multiple seeds or a per-episode breakdown, the central claim is statistically indistinguishable from sampling noise.
  3. [VI, Q2/Q3 (Tables II and III)] The headline configuration appears to have been selected on the same four evaluation tasks used for the final comparison. Buffer size 30 and rendering variant C were chosen after inspecting results on these tasks in Tables II and III, making the reported 4% gain a selected maximum rather than an unbiased estimate. The authors should report results for all configurations across multiple seeds, or reserve a validation set or held-out tasks for configuration selection, and disclose the selection procedure.
  4. [VI, Q4 (Table IV)] The attribution experiment is not defined precisely enough to support the conclusion that the gain 'stems from the trace-based visual prompting' rather than from fine-tuning. The rows 'Base model', 'Finetuned', and 'Traces 0-shot' do not state which base model is used, whether 'Finetuned' means fine-tuning without traces, and whether 'Traces 0-shot' uses the same CoTracker/ZoeDepth pipeline without any fine-tuning. A 2x2 comparison (fine-tune or not, traces or not) with explicit definitions is needed to justify the Q4 claim.
minor comments (6)
  1. [Abstract and I] The phrase 'increased for 4%' should be 'increased by 4%'; the same wording appears in Section I and the conclusion.
  2. [III-A] The training objective is written as 'arg max ... MSE ...', but maximizing MSE is the opposite of minimizing a prediction error; this should be 'arg min' with the appropriate sign.
  3. [III-B (Algorithm 1)] The symbol P_D is used for two different operations: predicting a depth map from an observation (line 3) and predicting depth embeddings from a rendered depth-traces map (line 5). Use distinct names to avoid confusion.
  4. [VI, Q2 (Table II)] The 'Stack blocks' GCS for buffer size 30 is listed as 90.0 in Table II but as 90.9 in Tables I and III; the discrepancy should be reconciled.
  5. [VI, Q3 (Figure 7)] The text states that variant B was excluded from testing, but Figure 7 illustrates all three variants; clarify whether B is shown only for illustration.
  6. [V-C] The text says 'we kept consistent random seeds in all experiments', but no seed values are reported; listing the seeds would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper's contribution is an empirical visual-prompting combination evaluated against external baselines; no prediction is analytically derived from, or fitted to, its inputs.

full rationale

The paper proposes overlaying CoTracker keypoint traces onto ZoeDepth depth maps via the SpatialVLA Ego3D encoder and fine-tunes PaliGemma2 on 52 Bridge trajectories. The resulting ST-VLA is then compared with SpatialVLA, TraceVLA and other baselines in SimplerEnv. There is no first-principles derivation whose conclusion is assumed in the premises: action prediction remains a learned VLA mapping, and the reported success rates are direct benchmark measurements. The depth and trace predictors are off-the-shelf external tools, and the Ego3D encoder is taken from a cited external method, not from an unverified self-citation. The paper's own ablations (Tables II-IV) test buffer length, trace-rendering variant, and fine-tuning versus zero-shot traces, which is a standard control structure rather than a circular step. The authors' self-citations in the introduction and related work (e.g., refs. 2, 3, 5, 6, 9, 10, 12) are contextual and carry no load in the empirical argument. Concerns that the buffer size and variant were selected after inspecting the same four-task benchmark, or that the +4% SR gain is within sampling noise, are statistical validity and evaluation-protocol risks, not circularity: they do not make the reported quantity equal to an input by construction. The paper also explicitly acknowledges trace failure modes in its limitations. Accordingly, no circular step can be quoted, and the correct finding is no significant circularity.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities, forces, or conserved quantities. The free parameters are design choices (buffer length, trace rendering variant, warmup steps), all selected with reference to the same benchmark used for the headline claim. The axioms are domain assumptions about the reliability of the external trackers, depth estimators, and the evaluation protocol.

free parameters (3)
  • Observation buffer size b = 30
    Buffer size determines how many past frames are used for traces and history. The paper reports experiments with 7, 15, and 30 and selects 30 based on the same SimplerEnv evaluation used for the headline result.
  • Trace rendering variant = C (nearest-object depth)
    The paper tests variants A and C (B excluded) and selects C after observing its performance on the same benchmark, so the headline result uses the best-performing variant on the test set.
  • No-trace warmup steps = 5
    To handle CoTracker's failure to initialize traces on near-identical frames, the model is not given traces for the first five steps; this is a hand-chosen heuristic.
assumptions (3)
  • domain assumption CoTracker keypoint trajectories, when overlain on depth maps, provide useful temporal information for manipulation policies.
    The entire method rests on the assumption that the visual traces are informative in the target environment; the paper itself notes cases where traces are short or absent (Section VI Q1).
  • domain assumption ZoeDepth produces depth maps accurate enough for the depth-with-traces prompt to preserve spatial structure in SimplerEnv.
    The method depends on depth quality; the paper mentions dependency on accurate depth predictions as a limitation (Section VII).
  • domain assumption SimplerEnv with four selected Bridge tasks is a representative and stable evaluation of VLA manipulation performance.
    The headline gains are measured on four tasks with a small number of episodes; no error bars are given, so the benchmark's stability is assumed.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Spatial Traces: Enhancing VLA Models with Spatial-Temporal Understanding." pith.science (2026). https://pith.science/paper/XT75VE5S

@misc{pith2026250809032,
  author       = {Pith},
  title        = {Pith review of: Spatial Traces: Enhancing VLA Models with Spatial-Temporal Understanding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XT75VE5S}},
  note         = {Machine review of arXiv:2508.09032}
}
read the original abstract

Vision-Language-Action models have demonstrated remarkable capabilities in predicting agent movements within virtual environments and real-world scenarios based on visual observations and textual instructions. Although recent research has focused on enhancing spatial and temporal understanding independently, this paper presents a novel approach that integrates both aspects through visual prompting. We introduce a method that projects visual traces of key points from observations onto depth maps, enabling models to capture both spatial and temporal information simultaneously. The experiments in SimplerEnv show that the mean number of tasks successfully solved increased for 4% compared to SpatialVLA and 19% compared to TraceVLA. Furthermore, we show that this enhancement can be achieved with minimal training data, making it particularly valuable for real-world applications where data collection is challenging. The project page is available at https://ampiromax.github.io/ST-VLA.

Figures

Figures reproduced from arXiv: 2508.09032 by the authors.

Figure 1
Figure 1. The integration of the Spatial Traces mechanism into the VLA model results in the ST-VLA architecture. The model receives a textual instruction [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Representative examples of tasks from SimplerEnv. All episodes [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Representative examples of tasks from the Bridge dataset. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Example of successful and failed execution of “put carrot on plate” [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 4
Figure 4. Figure 4: Average GCS and SR achieved by each method. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 7
Figure 7. Figure 7: Comparison of trace rendering strategies: (A) depth from the trace’s [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Visualization of depth map processing in the Spatial-Traces [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

38 extracted references · 13 canonical work pages

  1. [1]

    Inner monologue: Embodied reasoning through planning with language models,

    W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y . Chebotar, P. Sermanet, N. Brown, T. Jack- son, L. Luu, S. Levine, K. Hausman, and B. Ichter, “Inner monologue: Embodied reasoning through planning with language models,” 2022

    2022

  2. [2]

    Application of pretrained large language models in embodied artificial intelligence,

    A. K. Kovalev and A. I. Panov, “Application of pretrained large language models in embodied artificial intelligence,” in Doklady Mathematics, vol. 106, no. Suppl 1. Springer, 2022, pp. S85–S90

    2022

  3. [3]

    Evaluation of pretrained large language models in embodied planning tasks,

    C. Sarkisyan, A. Korchemnyi, A. K. Kovalev, and A. I. Panov, “Evaluation of pretrained large language models in embodied planning tasks,” in International Conference on Artificial General Intelligence . Springer, 2023, pp. 222–232

    2023

  4. [4]

    Palm- e: An embodied multimodal language model,

    D. Driess, F. Xia, M. S. M. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, W. Huang, Y . Chebotar, P. Sermanet, D. Duckworth, S. Levine, V . Vanhoucke, K. Hausman, M. Toussaint, K. Greff, A. Zeng, I. Mordatch, and P. Florence, “Palm- e: An embodied multimodal language model,” 2023

    work page 2023

  5. [5]

    Common sense plan verification with large language models,

    D. S. Grigorev, A. K. Kovalev, and A. I. Panov, “Common sense plan verification with large language models,” in International Conference on Hybrid Artificial Intelligence Systems . Springer, 2024, pp. 224– 236

    work page 2024

  6. [6]

    Verifyllm: Llm- based pre-execution task plan verification for robots,

    D. S. Grigorev, A. Kovalev, and A. I. Panov, “Verifyllm: Llm- based pre-execution task plan verification for robots,” arXiv preprint arXiv:2507.05118, 2025

    arXiv 2025

  7. [7]

    Llm-planner: Few-shot grounded planning for embodied agents with large language models,

    C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y . Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” in CVPR, 2023, pp. 2998–3009

    work page 2023

  8. [8]

    Look before you leap: Unveiling the power of gpt-4v in robotic vision-language planning,

    Y . Hu, F. Lin, T. Zhang, L. Yi, and Y . Gao, “Look before you leap: Unveiling the power of gpt-4v in robotic vision-language planning,” arXiv:2311.17842, 2023

    arXiv 2023

Show all 38 references
  1. [9]

    Lookplangraph: Embodied instruction following method with VLM graph augmentation,

    A. Onishchenko, A. Kovalev, and A. Panov, “Lookplangraph: Embodied instruction following method with VLM graph augmentation,” in Workshop on Reasoning and Planning for Large Language Models , 2025. [Online]. Available: https://openreview.net/forum?id=B47cCZfJFa

    2025

  2. [10]

    Lera: Replanning with visual feedback in instruction following,

    S. Pchelintsev, M. Patratskiy, A. Onishchenko, A. Korchemnyi, A. Medvedev, U. Vinogradova, I. Galuzinsky, A. Postnikov, A. K. Kovalev, and A. I. Panov, “Lera: Replanning with visual feedback in instruction following,” 2025

    2025

  3. [11]

    Vima: General robot manip- ulation with multimodal prompts,

    Y . Jiang, A. Gupta, Z. Zhang, G. Wang, Y . Dou, Y . Chen, L. Fei-Fei, A. Anandkumar, Y . Zhu, and L. Fan, “Vima: General robot manip- ulation with multimodal prompts,” arXiv preprint arXiv:2210.03094 , 2022

    2022 arXiv

  4. [12]

    Fine-tuning multi- modal transformer models for generating actions in virtual and real environments,

    A. Staroverov, A. S. Gorodetsky, A. S. Krishtopik, U. A. Izmesteva, D. A. Yudin, A. K. Kovalev, and A. I. Panov, “Fine-tuning multi- modal transformer models for generating actions in virtual and real environments,” Ieee Access, vol. 11, pp. 130 548–130 559

  5. [13]

    Octo: An open-source generalist robot policy,

    Octo Model Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, C. Xu, J. Luo, T. Kreiman, Y . Tan, L. Y . Chen, P. Sanketi, Q. Vuong, T. Xiao, D. Sadigh, C. Finn, and S. Levine, “Octo: An open-source generalist robot policy,” in Proceedings of Robotic...

    2024

  6. [14]

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

    M. Kim, K. Pertsch, S. Karamcheti, T. Xiao, A. Balakrishna, S. Nair, R. Rafailov, E. Foster, G. Lam, P. Sanketi, Q. Vuong, T. Kollar, B. Burchfiel, R. Tedrake, D. Sadigh, S. Levine, P. Liang, and C. Finn, “Openvla: An open-source vision-language-action model,” arXiv:2406.09246, 2024

    2024 arXiv

  7. [15]

    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, et al. , “Cogact: A foundational vision- language-action model for synergizing cognition and action in robotic manipulation,” arXiv:2411.19650, 2024

    2024 arXiv

  8. [16]

    Spatialvla: Exploring spatial representations for visual-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 visual-language-action model,” 2025. [Online]. Available: https://arxiv.org/abs/2501.15830

    2025 arXiv

  9. [17]

    Robovqa: Multimodal long-horizon reasoning for robotics,

    P. Sermanet, T. Ding, J. Zhao, F. Xia, D. Dwibedi, K. Gopalakrish- nan, C. Chan, G. Dulac-Arnold, S. Maddineni, N. J. Joshi, et al. , “Robovqa: Multimodal long-horizon reasoning for robotics,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024...

    2024

  10. [18]

    Mastering long-context multi-task reasoning with transformers and recurrent memory,

    A. Bulatov, Y . Kuratov, and M. Burtsev, “Mastering long-context multi-task reasoning with transformers and recurrent memory,”Optical Memory and Neural Networks , vol. 33, no. Suppl 3, pp. S466–S474, 2024

    2024

  11. [19]

    Babilong: Testing the limits of llms with long con- text reasoning-in-a-haystack,

    Y . Kuratov, A. Bulatov, P. Anokhin, I. Rodkin, D. Sorokin, A. Sorokin, and M. Burtsev, “Babilong: Testing the limits of llms with long con- text reasoning-in-a-haystack,” NeurIPS, vol. 37, pp. 106 519–106 554, 2024

    2024

  12. [20]

    Rt-trajectory: Robotic task generalization via hindsight trajectory sketches,

    J. Gu, S. Kirmani, P. Wohlhart, Y . Lu, M. G. Arenas, K. Rao, W. Yu, C. Fu, K. Gopalakrishnan, Z. Xu, P. Sundaresan, P. Xu, H. Su, K. Hausman, C. Finn, Q. Vuong, and T. Xiao, “Rt-trajectory: Robotic task generalization via hindsight trajectory sketches,” 2023. [Online]. Availa...

    2023 arXiv

  13. [21]

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

    R. Zheng, Y . Liang, S. Huang, J. Gao, H. D. III, A. Kolobov, F. Huang, and J. Yang, “Tracevla: Visual trace prompting enhances spatial-temporal awareness for generalist robotic policies,” 2024. [Online]. Available: https://arxiv.org/abs/2412.10345

    2024 arXiv

  14. [22]

    Rt-affordance: Affordances are versatile intermediate representations for robot manipulation,

    S. Nasiriany, S. Kirmani, T. Ding, L. Smith, Y . Zhu, D. Driess, D. Sadigh, and T. Xiao, “Rt-affordance: Affordances are versatile intermediate representations for robot manipulation,” 2024. [Online]. Available: https://arxiv.org/abs/2411.02704

    2024 arXiv

  15. [23]

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

    O. X.-E. Collaboration, “Open X-Embodiment: Robotic learning datasets and RT-X models,” https://arxiv.org/abs/2310.08864, 2023

    2023 arXiv

  16. [24]

    Sem: Enhancing spatial understanding for robust robot manipulation,

    X. Lin, T. Lin, L. Huang, H. Xie, Y . Jin, K. Li, and Z. Su, “Sem: Enhancing spatial understanding for robust robot manipulation,” arXiv:2505.16196, 2025

    2025

  17. [25]

    Robotap: Tracking arbitrary points for few-shot visual imitation,

    M. Vecerik, C. Doersch, Y . Yang, T. Davchev, Y . Aytar, G. Zhou, R. Hadsell, L. Agapito, and J. Scholz, “Robotap: Tracking arbitrary points for few-shot visual imitation,” 2023. [Online]. Available: https://arxiv.org/abs/2308.15975

    2023 arXiv

  18. [26]

    Tapir: Tracking any point with per-frame initialization and retrospection,

    J. Luiten, T. Phillips, T. Sharp, et al., “Tapir: Tracking any point with per-frame initialization and retrospection,” arXiv:2306.08685, 2023

    2023 arXiv

  19. [27]

    Cotracker v3: Joint tracking of multiple points via transformer,

    Y . Yang, Y . Zou, H. Zhao, et al. , “Cotracker v3: Joint tracking of multiple points via transformer,” arXiv:2405.05941, 2024

    2024 arXiv

  20. [28]

    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, L. Liden, and J. Gao, “Magma: A foundation model for multimodal ai agents,” 2025. [Online]. Available: https://arxiv.org/abs/2502.13130

    2025 arXiv

  21. [29]

    Sigmoid loss for language image pre-training,

    X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer, “Sigmoid loss for language image pre-training,” pp. 11 975–11 986, 2023

    2023

  22. [30]

    Cotracker: It is better to track together,

    N. Karaev, I. Rocco, B. Graham, N. Neverova, A. Vedaldi, and C. Rupprecht, “Cotracker: It is better to track together,” in Proc. ECCV, 2024

    2024

  23. [31]

    Zoedepth: Zero-shot transfer by combining relative and metric depth,

    S. F. Bhat, R. Birkl, D. Wofk, P. Wonka, and M. Müller, “Zoedepth: Zero-shot transfer by combining relative and metric depth,” arXiv preprint arXiv:2302.12288, 2023

    2023 arXiv

  24. [32]

    Paligemma 2: A family of versatile vlms for transfer,

    A. Steiner, A. S. Pinto, M. Tschannen, D. Keysers, X. Wang, Y . Bitton, A. Gritsenko, M. Minderer, A. Sherbondy, S. Long, S. Qin, R. Ingle, E. Bugliarello, S. Kazemzadeh, T. Mesnard, I. Alabdulmohsin, L. Beyer, and X. Zhai, “Paligemma 2: A family of versatile vlms for transfer...

    2024 arXiv

  25. [33]

    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,” 2024

    2024

  26. [34]

    robosuite: A modular simulation framework and benchmark for robot learning,

    Y . Zhu, J. Wong, A. Mandlekar, R. Martín-Martín, A. Joshi, K. Lin, S. Nasiriany, and Y . Zhu, “robosuite: A modular simulation framework and benchmark for robot learning,” in arXiv:2009.12293, 2020

    2009 arXiv

  27. [35]

    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:2412.14058, 2024

    2024 arXiv

  28. [36]

    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,” in CoRL, 2023

    2023

  29. [37]

    Lora: Low-rank adaptation of large language models

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, W. Chen,et al., “Lora: Low-rank adaptation of large language models.” ICLR, vol. 1, no. 2, p. 3, 2022

    2022

  30. [38]

    Decoupled weight decay regularization,

    I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” 2019

    2019

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.