Pith. sign in

REVIEW 2 major objections 5 minor 56 references

LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation

T0 review · 2 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read The paper claims that a lightweight 0.5B-parameter world-action model, trained end-to-end on one 24GB GPU, reaches 90.48% average success across 50 RoboTwin tasks and 97.1% on LIBERO, outperforming much larger policies.

desk verdict Valuable, concrete lightweight WAM with a real question about whether the foresight loss is world modeling or regularization. read the letter →

arxiv 2608.03701 v1 pith:T4B32UYL submitted 2026-08-04 cs.RO cs.AI

Fan Yang , Yuting Su , Xiaobo Wang , Yuncheng You , Fugui Fan , Yuting Wu , Minghui Wu , Chenxu Zhao
show 2 more authors
JiaHong Ning Peiguang Jing
This is my paper
classification cs.ROcs.AI
keywords world-actionmodelroboticmanipulationlatentreasoningfuture-statepredictionVisualTransitionTokenflowmatchingDINOv3featuressingle-GPUtraining
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 claims that a robot policy can learn useful world models without large backbones or pixel-space video generation. The authors propose LiLa-WAM, a 0.5B-parameter model (0.2B trainable) that predicts future states and actions together in a compact latent space and can be trained end-to-end on a single 24GB GPU. On 50 RoboTwin 2.0 tasks it reports 90.48% average success, above the 8B Motus and 5B GigaWorld-Policy baselines, and on LIBERO it reaches a 97.1% average. The central bets are that future-feature supervision in a frozen DINOv3 feature space is a strong training signal, and that a language-free Visual Transition Token—the mean difference between the final and initial frame embeddings of demonstrations—can replace text instructions. If true, foresight in manipulation no longer requires multi-stage training or video-generation-scale compute.

What carries the argument

The Foresight-Aware Action Expert is a stack of Diffusion-Transformer blocks that takes one token sequence containing compressed visual tokens, proprioceptive tokens, the VTT task token, and noised action tokens, and reads out both the action velocity and the future latent from the same stream. A training-only Q-Former-style decoder maps the future latent back to DINOv3 patch-feature space, where a cosine loss supervises it against features of the true future frame. The Visual Transition Token is the mean difference between the global embeddings of the last and first frames of demonstration episodes, giving each task a fixed direction in visual feature space that requires neither text nor a

What would settle it

Record the cosine similarity between LiLa-WAM's predicted future features and the DINOv3 features of the true next observation on held-out RoboTwin episodes, and compare it with the similarity to random future frames. If the true future frame does not rank reliably above random alternatives, the foresight loss is not encoding action-conditioned scene evolution. A second check: replace the foresight loss with a constant target and retrain; if the 10-task success rate stays near 70.0%, the ablation gain is not caused by future prediction.

Watch

Extended reading notes

Core claim

LiLa-WAM's central claim is that future-state prediction can be folded directly into action generation in a shared latent stream, so the same tokens produce both an action velocity and a prediction of the future latent, supervised by a per-token cosine loss against DINOv3 features of the true next observation. Because the future is predicted in a compact latent space rather than in pixels, no image or video decoder is needed at inference; the decoder is used only during training and then discarded. The paper also introduces the Visual Transition Token (VTT), a per-task vector computed as the mean difference between the final- and first-frame global embeddings of demonstration episodes, which

Load-bearing premise

The load-bearing premise is that the cosine foresight loss genuinely teaches the model to predict action-conditioned future scenes, rather than collapsing to a nearly constant feature vector that lowers the loss without carrying information.

Editorial extensions

If this is right

  • Lightweight world-action models with future-feature supervision can match or beat much larger VLA and WAM baselines on RoboTwin and LIBERO, suggesting that raw parameter count is not the main driver of control performance.
  • Foresight supervision is a transferable training signal: removing it costs 15.6 points on the 10-task RoboTwin ablation and 8 points on the real-robot average.
  • The VTT provides a language-free task interface that improves by 8.6 points over CLIP-encoded language conditioning while requiring no text or goal image at deployment.
  • A frozen DINOv3 visual encoder with multi-level features outperforms a 2.2B Qwen3VL backbone by 9 points under the same training budget, pointing to control-relevant pretraining over model scale.
  • Training the full 50-task model takes about 110 GPU hours on one RTX 5090, and inference runs in 85 ms on an RTX 4090, fitting within modest compute budgets.

Reading between the lines

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

  • If future-feature supervision is genuinely action-conditioned, then a model trained with LiLa-WAM's objective should rank the true future feature above randomly sampled alternative futures; that rank is a direct, testable monitor for the world-modeling claim.
  • Because VTTs are additive directions in feature space, tasks with related transition directions (for example, placing different objects on the same surface) may be composable by interpolating or adding their VTT vectors, enabling zero-shot task combinations; the t-SNE clustering in the appendix points toward such structure but does not test composition.
  • The same single-stream latent-foresight design may transfer to other settings where a future observation is available at training time, such as navigation or bimanual coordination, since the method does not depend on language or goal images; testing on longer-horizon tasks would expose any failure of the future-prediction signal to stay control-relevant.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 5 minor

Summary. The paper proposes LiLa-WAM, a 0.5B-parameter (0.2B trainable) world-action model for robotic manipulation. The model is built on a frozen DINOv3 visual encoder, compresses its patch features into a small set of query tokens, and trains a DiT-based action expert jointly with a future-feature cosine loss (foresight loss) and a flow-matching action loss. Task specification is given by a Visual Transition Token (VTT), a language-free per-task vector computed as the mean difference between final- and initial-frame embeddings of demonstrations. The authors report a 90.48% success rate across 50 RoboTwin 2.0 tasks, a 97.1% LIBERO average, and a real-robot self-comparison of 82.0% with the foresight loss versus 74.0% without it, all with training on a single GPU. Ablations examine the foresight objective, VTT versus CLIP-language conditioning, backbone choice, adapter query count, and feature-layer selection.

Significance. If the central claims hold, the paper demonstrates that a lightweight, visual-backbone-only world-action model with future-feature supervision can be competitive with much larger VLA/WAM policies, and that a language-free transition-direction task token is a viable alternative to text conditioning. The strengths include extensive ablation studies, a real-robot comparison that isolates the foresight loss, per-task results over 50 RoboTwin tasks, a publicly promised code release, and the fact that the headline numbers are tied to external benchmarks rather than to any fitted post-hoc parameters. The main risk is mechanistic: the paper attributes a 15.6-point ablation gain to future-state reasoning, but the evidence that the foresight loss is non-degenerate and action-conditioned is currently thin. This concern is load-bearing for the paper's explanatory claim, though not necessarily for the empirical benchmark numbers.

major comments (2)
  1. [§3.4, Eq. (5); §4.5, Fig. 6] The claim that the foresight loss contributes through genuine world modeling is not yet supported. L_ff is a per-token cosine loss in frozen DINOv3 feature space; because cosine similarity is scale-invariant and no diversity or scale constraint is imposed, the loss can in principle be minimized by predicting a constant or static-context-dominated vector. The paper reports no achieved loss, no cosine similarity between predicted and true future features, and no retrieval/rank metric. The only direct probe of action-conditioning, the perturbation experiment in Fig. 6, shows that replacing the entire action chunk reduces decoder-output cosine similarity only to about 0.85 at s=1, with near-insensitivity earlier. This is weak evidence for action-conditioned dynamics and is in tension with the 15.6-point gain in Table 3 (70.0 vs 54.4). Please add (i) absolute prediction-quality metrics agains
  2. [§3.3, Eq. (4)] The VTT is a per-task mean of (g_T - g_0) over demonstration episodes, and at deployment the corresponding per-task vector is selected. In all benchmark experiments the task identity is known in advance, so the reported gains of VTT over CLIP-language conditioning do not distinguish a task-specific embedding from a task ID. The claim that VTT is a language-free task representation would be stronger with an experiment on a held-out task, e.g., computing u_tau from a few demonstrations of an unseen task and evaluating the policy without additional fine-tuning, or at least a quantitative measure showing that VTT separates tasks by transition structure rather than by memorized task identity. Without such an experiment, the generalization benefit of VTT over a one-hot task embedding is unestablished.
minor comments (5)
  1. [§4.1] The paper says 'single 24 GB GPU' but names the NVIDIA RTX 5090, which has 32 GB VRAM. Clarify whether training was run under a 24 GB memory cap or on a 32 GB card; the reported training memory of 18.0 GB fits either.
  2. [§3.2, Eq. (3) and Fig. 2] The token-stream notation is inconsistent between the equation and the figure. Specify which token positions produce the future latent \hat{z}_{t+\Delta} and how the 'Foresight Tokens' in Fig. 2 relate to Z_v.
  3. [§4.5 and Fig. 6] The text uses s for the denoising timestep while Fig. 6 uses t; unify the notation to avoid confusion.
  4. [Table 3] The check/cross marks in the table header are not self-explanatory. Add a legend or explicit boolean columns for 'Foresight' and 'Task Cond.' with definitions of the symbols.
  5. [Appendix A and §4.6] The robot name is spelled 'Piper' in the main text and 'PiPer' in the appendix; standardize the spelling.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the VTT is a data-derived conditioning vector, the foresight loss is supervised against frozen-encoder features of true future frames, and headline results are pinned to external benchmarks.

full rationale

The paper's derivation chain does not reduce to its own inputs. The Visual Transition Token (Eq. 4) is defined as the mean difference between initial- and final-frame embeddings of demonstration episodes; it is a fixed, data-derived conditioning vector supplied to the model, not a quantity the model is asked to predict. The foresight loss L_ff (Eq. 5 and Section 3.4) trains the decoder-mapped predicted future latent against DINOv3 features extracted from the ground-truth future observation o_{t+Delta}, so the supervision signal is an external, frozen-encoder target rather than a function of the model's own output. The reported ablations (Table 3) compare models with and without L_ff on held-out rollouts; removing the objective lowers success from 70.0% to 54.4%, which is an empirical comparison, not a fitted parameter renamed as a prediction. The action-perturbation probe in Section 4.5 is an auxiliary diagnostic, not a claimed prediction; its interpretation may be debatable (cosine similarity ~0.85 could indicate limited action sensitivity), but that is a validity concern, not circularity. The paper cites no prior work by the same authors and invokes no uniqueness theorem or self-citation chain to force its design choices. Benchmark numbers are compared against external baselines (RoboTwin 2.0, LIBERO, real-robot rollouts), and the backbone (DINOv3) and benchmarks are independent of the paper's fitted values. Therefore no circular step is present.

Assumptions & free parameters 5 free parameters · 5 assumptions · 1 invented entities

The trained system rests on a frozen DINOv3 backbone treated as a given, a cosine-similarity target in that frozen feature space whose non-degeneracy is asserted rather than measured, hand-set hyperparameters (lambda_ff, Nq, Delta, feature layers) selected on the 10-task ablation subset, per-task VTT vectors computed from demonstrations, and the unverified assumption that quoted baseline numbers come from comparable data and evaluation protocols. None of these is a standard mathematical axiom; they are domain assumptions or tuned quantities.

free parameters (5)
  • Foresight loss weight lambda_ff = 0.5
    Hand-set weight in Eq. 5; no sensitivity sweep is reported.
  • Adapter query count Nq = 64
    Selected by ablation on the 10-task subset (Table 4): 66.2, 65.2, 70.0, 67.6 for Nq = 8, 32, 64, 96.
  • DINOv3 feature layers = 17 and 21
    Selected by ablation on the 10-task subset (Table 5): success rises from 48.0 (layer 24) to 70.0 (layers 17 and 21).
  • Look-ahead horizon Delta = 32
    Set equal to the action chunk length without ablation or sensitivity analysis.
  • Per-task VTT vectors u_tau = Not reported (50 task vectors on RoboTwin, plus LIBERO and real-robot sets)
    Computed as the mean first-last frame embedding difference over demonstrations (Eq. 4); these data-derived vectors constitute the task conditioning at test time, so their size and composition are part of the method's input.
assumptions (5)
  • domain assumption DINOv3 patch features are an adequate perceptual substrate for fine-grained manipulation
    The entire pipeline freezes DINOv3 and compresses its features (Section 3.2); adequacy is supported only by the layer ablation and one backbone comparison (Table 3), not by a survey of encoders.
  • domain assumption Cosine similarity in frozen DINOv3 feature space is a non-degenerate target for future-state prediction
    Eq. 5 and Section 3.4 use a per-token cosine loss against features of the true future frame; the paper does not show that this target avoids the collapse failure mode noted for Being-H0.7.
  • standard math Conditional flow matching with linear interpolation is a valid objective for action chunk generation
    Eq. 6 follows the standard diffusion-policy / flow-matching convention, adopted without modification.
  • domain assumption The demonstrations collected per task, 50 clean plus 500 randomized on RoboTwin 2.0, are comparable to the data behind the quoted baseline numbers
    Section 4.2 says 'we collect' the demos; since baseline numbers are quoted from other papers, a matched data protocol is assumed rather than verified.
  • domain assumption Task identity is available at deployment to select the correct VTT
    Section 4.1: 'the VTT corresponding to the target task is selected'; the method cannot infer the VTT from an observation, so it is task-indexed conditioning.
invented entities (1)
  • Visual Transition Token (VTT) independent evidence
    purpose: A per-task, language-free condition vector defined as the mean difference between final-frame and initial-frame global embeddings across demonstrations (Eq. 4).
    The token makes a checkable structural prediction (transition directions cluster semantically, Figures 9 and 10) and its utility is tested on real-robot tasks outside the main benchmark; however, it must be precomputed per task and selected by task identity at deploy time.

how reviews work

0 comments
Cite this review

Pith. "Pith review of LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation." pith.science (2026). https://pith.science/paper/T4B32UYL

@misc{pith2026260803701,
  author       = {Pith},
  title        = {Pith review of: LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T4B32UYL}},
  note         = {Machine review of arXiv:2608.03701}
}
read the original abstract

World-action modeling has emerged as a promising paradigm for robotic control, as it empowers models to go beyond reacting to observations and anticipate how a scene will evolve. However, existing WAMs often incur substantial computational overhead. Pixel-space methods often allocate substantial capacity to visual details that may not be directly relevant to control, while some latent-space methods require multi-stage training to construct the reasoning space. The resulting training cost can make such methods difficult to train under modest computational budgets. In this work, we propose LiLa-WAM, a lightweight world-action model that reasons about the future in a compact latent space and can be trained end-to-end on a single 24GB GPU. Its core design is a compact latent reasoning space jointly shaped by future-state prediction and action generation, which keeps the model lightweight while remaining well aligned with control. For task specification, we further propose the Visual Transition Token(VTT), a language-free task representation that encodes each task as a direction in visual feature space. Experiments on RoboTwin~2.0, LIBERO, and real-robot tasks demonstrate LiLa-WAM's effectiveness, achieving 90.48\% success across 50 RoboTwin tasks with single-GPU training.

Figures

Figures reproduced from arXiv: 2608.03701 by the authors.

Figure 1
Figure 1. Average success rate versus model size on RoboTwin 2.0 and LIBERO, for methods with reported model [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overview of LiLa-WAM. The Foresight-Aware Action Expert unifies reasoning tokens, the VTT, propriocep [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The Visual Transition Token and its embedding structure on RoboTwin 2.0. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Attention maps of the VTT and action tokens. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Per-layer attention maps of the VTT and action tokens. Compared with the action tokens, the VTT attends [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 7
Figure 7. Figure 7: Attention dynamics across denoising steps on the [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: Execution process of the four real-robot tasks. [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: t-SNE visualization of per-episode visual transition embeddings on the real-robot demonstration set, where [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: t-SNE visualization of per-episode visual transition embeddings on LIBERO-goal. Each point represents the [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

56 extracted references · 9 canonical work pages

  1. [1]

    Video prediction policy: A generalist robot policy with predictive visual representations.arXiv preprint arXiv:2412.14803, 2024

    Yucheng Hu, Yanjiang Guo, Pengchao Wang, Xiaoyu Chen, Yen-Jen Wang, Jianke Zhang, Koushil Sreenath, Chaochao Lu, and Jianyu Chen. Video prediction policy: A generalist robot policy with predictive visual representations.arXiv preprint arXiv:2412.14803, 2024

    arXiv 2024

  2. [2]

    Learning universal policies via text-guided video generation.Advances in neural information processing systems, pages 9156–9172, 2023

    Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Josh Tenenbaum, Dale Schuurmans, and Pieter Abbeel. Learning universal policies via text-guided video generation.Advances in neural information processing systems, pages 9156–9172, 2023

    work page 2023

  3. [3]

    Predictive inverse dynamics models are scalable learners for robotic manipulation

    Yang Tian, Sizhe Yang, Jia Zeng, Ping Wang, Dahua Lin, Hao Dong, and Jiangmiao Pang. Predictive inverse dynamics models are scalable learners for robotic manipulation. InInternational Conference on Learning Representations, pages 92033–92052, 2025

    work page 2025

  4. [4]

    Wall-wm: Carving world action modeling at the event joints.arXiv preprint arXiv:2606.01955, 2026

    Shalfun Li, Victor Yao, Charles Yang, Truth Qu, Regis Cheng, Ryan Yu, Howard Lu, Newton V on, Vincent Chen, Yohann Tang, et al. Wall-wm: Carving world action modeling at the event joints.arXiv preprint arXiv:2606.01955, 2026

    arXiv 2026

  5. [5]

    AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing

    Jisong Cai, Long Ling, Shiwei Chu, Zhongshan Liu, Jiayue Kang, Zhixuan Liang, Wenjie Xu, Yinan Mao, Weinan Zhang, Xiaokang Yang, et al. Aha-wam: Asynchronous horizon-adaptive world-action modeling with observation-guided context routing.arXiv preprint arXiv:2606.09811, 2026

    work page Pith review arXiv 2026

  6. [6]

    World action models: The next frontier in embodied ai.arXiv preprint arXiv:2605.12090, 2026

    Siyin Wang, Junhao Shi, Zhaoyang Fu, Xinzhe He, Feihong Liu, Chenchen Yang, Yikang Zhou, Zhaoye Fei, Jingjing Gong, Jinlan Fu, et al. World action models: The next frontier in embodied ai.arXiv preprint arXiv:2605.12090, 2026

    arXiv 2026

  7. [7]

    Imagewam: Do world action models really need video generation, or just image editing?arXiv preprint arXiv:2606.19531, 2026

    Yuyang Zhang, Wenyao Zhang, Zekun Qi, He Zhang, Haitao Lin, Jingbo Zhang, Yao Mu, Xiaokang Yang, Wenjun Zeng, and Xin Jin. Imagewam: Do world action models really need video generation, or just image editing?arXiv preprint arXiv:2606.19531, 2026

    arXiv 2026

  8. [8]

    pi0.7: a steerable generalist robotic foundation model with emergent capabilities.arXiv preprint arXiv:2604.15483, 2026

    Physical Intelligence, Bo Ai, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Greg Balke, Kevin Black, George Bokinsky, Shihao Cao, Thomas Charbonnier, et al. pi0.7: a steerable generalist robotic foundation model with emergent capabilities.arXiv preprint arXiv:2604.15483, 2026

    arXiv 2026

Show all 56 references
  1. [9]

    Cosmos policy: Fine-tuning video models for visuomotor control and planning

    Moo Jin Kim, Yihuai Gao, Tsung-Yi Lin, Yen-Chen Lin, Yunhao Ge, Grace Lam, Percy Liang, Shuran Song, Ming-Yu Liu, Chelsea Finn, et al. Cosmos policy: Fine-tuning video models for visuomotor control and planning. arXiv preprint arXiv:2601.16163, 2026

    2026 arXiv

  2. [10]

    Causal world modeling for robot control.arXiv preprint arXiv:2601.21998, 2026

    Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, Nan Xue, Xing Zhu, et al. Causal world modeling for robot control.arXiv preprint arXiv:2601.21998, 2026

    2026 arXiv

  3. [11]

    Fast-wam: Do world action models need test-time future imagination?arXiv preprint arXiv:2603.16666, 2026

    Tianyuan Yuan, Zibin Dong, Yicheng Liu, and Hang Zhao. Fast-wam: Do world action models need test-time future imagination?arXiv preprint arXiv:2603.16666, 2026

    2026 arXiv

  4. [12]

    Latent action pretraining from videos

    Seonghyeon Ye, Joel Jang, Byeongguk Jeon, Se June Joo, Jianwei Yang, Baolin Peng, Ajay Mandlekar, Reuben Tan, Yu-Wei Chao, Bill Yuchen Lin, et al. Latent action pretraining from videos. InInternational Conference on Learning Representations, pages 28213–28239, 2025

    2025

  5. [13]

    Flare: Robot learning with implicit world modeling.arXiv preprint arXiv:2505.15659, 2025

    Ruijie Zheng, Jing Wang, Scott Reed, Johan Bjorck, Yu Fang, Fengyuan Hu, Joel Jang, Kaushil Kundalia, Zongyu Lin, Loic Magne, et al. Flare: Robot learning with implicit world modeling.arXiv preprint arXiv:2505.15659, 2025

    2025 arXiv

  6. [14]

    Frappe: Infusing world modeling into generalist policies via multiple future representation alignment.arXiv preprint arXiv:2602.17259, 2026

    Han Zhao, Jingbo Wang, Wenxuan Song, Shuai Chen, Yang Liu, Yan Wang, Haoang Li, and Donglin Wang. Frappe: Infusing world modeling into generalist policies via multiple future representation alignment.arXiv preprint arXiv:2602.17259, 2026

    2026

  7. [15]

    World guidance: World modeling in condition space for action generation.arXiv preprint arXiv:2602.22010, 2026

    Yue Su, Sijin Chen, Haixin Shi, Mingyu Liu, Zhengshen Zhang, Ningyuan Huang, Weiheng Zhong, Zhengbang Zhu, Yuxiao Liu, and Xihui Liu. World guidance: World modeling in condition space for action generation.arXiv preprint arXiv:2602.22010, 2026. 11

    2026

  8. [16]

    Being-h0.7: A latent world-action model from egocentric videos.arXiv preprint arXiv:2605.00078, 2026

    Hao Luo, Wanpeng Zhang, Yicheng Feng, Sipeng Zheng, Haiweng Xu, Chaoyi Xu, Ziheng Xi, Yuhui Fu, and Zongqing Lu. Being-h0.7: A latent world-action model from egocentric videos.arXiv preprint arXiv:2605.00078, 2026

    2026 arXiv

  9. [17]

    Openvla: An open-source vision-language-action model.arXiv preprint arXiv:2406.09246, 2024

    Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. Openvla: An open-source vision-language-action model.arXiv preprint arXiv:2406.09246, 2024

    2024 arXiv

  10. [18]

    Octo: An open-source generalist robot policy.arXiv preprint arXiv:2405.12213, 2024

    Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, et al. Octo: An open-source generalist robot policy.arXiv preprint arXiv:2405.12213, 2024

    2024 arXiv

  11. [19]

    pi0: A vision-language-action flow model for general robot control

    Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. pi0: A vision-language-action flow model for general robot control. arXiv preprint arXiv:2410.24164, 2024

    2024 arXiv

  12. [20]

    Diffusion policy: Visuomotor policy learning via action diffusion.The International Journal of Robotics Research, (10-11):1684–1704, 2025

    Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion.The International Journal of Robotics Research, (10-11):1684–1704, 2025

    2025

  13. [21]

    Rdt-1b: a diffusion foundation model for bimanual manipulation.arXiv preprint arXiv:2410.07864, 2024

    Songming Liu, Lingxuan Wu, Bangguo Li, Hengkai Tan, Huayu Chen, Zhengyi Wang, Ke Xu, Hang Su, and Jun Zhu. Rdt-1b: a diffusion foundation model for bimanual manipulation.arXiv preprint arXiv:2410.07864, 2024

    2024 arXiv

  14. [22]

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

    Qixiu Li, Yaobo Liang, Zeyu Wang, Lin Luo, Xi Chen, Mozheng Liao, Fangyun Wei, Yu Deng, Sicheng Xu, Yizhong Zhang, et al. Cogact: A foundational vision-language-action model for synergizing cognition and action in robotic manipulation.arXiv preprint arXiv:2411.19650, 2024

    2024 arXiv

  15. [23]

    Tinyvla: Towards fast, data-efficient vision-language-action models for robotic manipulation

    Junjie Wen, Yichen Zhu, Jinming Li, Minjie Zhu, Zhibin Tang, Kun Wu, Zhiyuan Xu, Ning Liu, Ran Cheng, Chaomin Shen, et al. Tinyvla: Towards fast, data-efficient vision-language-action models for robotic manipulation. IEEE Robotics and Automation Letters, 2025

    2025

  16. [24]

    Smolvla: A vision-language-action model for affordable and efficient robotics.arXiv preprint arXiv:2506.01844, 2025

    Mustafa Shukor, Dana Aubakirova, Francesco Capuano, Pepijn Kooijmans, Steven Palma, Adil Zouitine, Michel Aractingi, Caroline Pascal, Martino Russi, Andres Marafioti, et al. Smolvla: A vision-language-action model for affordable and efficient robotics.arXiv preprint arXiv:2506...

    2025 arXiv

  17. [25]

    Evo-1: Lightweight vision-language-action model with preserved semantic alignment

    Tao Lin, Yilei Zhong, Yuxin Du, Jingjing Zhang, Jiting Liu, Yinxinyu Chen, Encheng Gu, Ziyan Liu, Hongyi Cai, Yanwen Zou, et al. Evo-1: Lightweight vision-language-action model with preserved semantic alignment. In Proceedings of the IEEE/CVF conference on computer vision and ...

    2026

  18. [26]

    mimic-video: Video-action models for generalizable robot control beyond vlas.arXiv preprint arXiv:2512.15692, 2025

    Jonas Pai, Liam Achenbach, Victoriano Montesinos, Benedek Forrai, Oier Mees, and Elvis Nava. mimic-video: Video-action models for generalizable robot control beyond vlas.arXiv preprint arXiv:2512.15692, 2025

    2025 arXiv

  19. [27]

    Vidar: Embodied video diffusion model for generalist manipulation.arXiv preprint arXiv:2507.12898, 2025

    Yao Feng, Hengkai Tan, Xinyi Mao, Chendong Xiang, Guodong Liu, Shuhe Huang, Hang Su, and Jun Zhu. Vidar: Embodied video diffusion model for generalist manipulation.arXiv preprint arXiv:2507.12898, 2025

    2025 arXiv

  20. [28]

    Genie envisioner: A unified world foundation platform for robotic manipulation.arXiv preprint arXiv:2508.05635, 2025

    Yue Liao, Pengfei Zhou, Siyuan Huang, Donglin Yang, Shengcong Chen, Yuxin Jiang, Yue Hu, Jingbin Cai, Si Liu, Jianlan Luo, et al. Genie envisioner: A unified world foundation platform for robotic manipulation.arXiv preprint arXiv:2508.05635, 2025

    2025 arXiv

  21. [29]

    Worldvla: Towards autoregressive action world model.arXiv preprint arXiv:2506.21539, 2025

    Jun Cen, Chaohui Yu, Hangjie Yuan, Yuming Jiang, Siteng Huang, Jiayan Guo, Xin Li, Yibing Song, Hao Luo, Fan Wang, et al. Worldvla: Towards autoregressive action world model.arXiv preprint arXiv:2506.21539, 2025

    2025 arXiv

  22. [30]

    Unified video action model.arXiv preprint arXiv:2503.00200, 2025

    Shuang Li, Yihuai Gao, Dorsa Sadigh, and Shuran Song. Unified video action model.arXiv preprint arXiv:2503.00200, 2025

    2025 arXiv

  23. [31]

    Motus: A unified latent action world model.arXiv preprint arXiv:2512.13030, 2025

    Hongzhe Bi, Hengkai Tan, Shenghao Xie, Zeyuan Wang, Shuhe Huang, Haitian Liu, Ruowen Zhao, Yao Feng, Chendong Xiang, Yinze Rong, et al. Motus: A unified latent action world model.arXiv preprint arXiv:2512.13030, 2025

    2025 arXiv

  24. [32]

    Gigaworld-policy: An efficient action-centered world–action model.arXiv preprint arXiv:2603.17240, 2026

    Angen Ye, Boyuan Wang, Chaojun Ni, Guan Huang, Guosheng Zhao, Hao Li, Hengtao Li, Jie Li, Jindi Lv, Jingyu Liu, et al. Gigaworld-policy: An efficient action-centered world–action model.arXiv preprint arXiv:2603.17240, 2026

    2026

  25. [33]

    Wan: Open and advanced large-scale video generative models.arXiv preprint arXiv:2503.20314, 2025

    Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianx- iao Yang, et al. Wan: Open and advanced large-scale video generative models.arXiv preprint arXiv:2503.20314, 2025

    2025 arXiv

  26. [34]

    Lawam: Latent world action models for efficient dynamics-aware robot policies.arXiv preprint arXiv:2606.15768, 2026

    Jialei Chen, Kai Wang, Kang Chen, Shuaihang Chen, Feng Gao, Wenhao Tang, Zhiyuan Li, Weilin Liu, Zhuyu Yao, Boxun Li, et al. Lawam: Latent world action models for efficient dynamics-aware robot policies.arXiv preprint arXiv:2606.15768, 2026. 12

    2026

  27. [35]

    Act2goal: From world model to general goal-conditioned policy.arXiv preprint arXiv:2512.23541, 2025

    Pengfei Zhou, Liliang Chen, Shengcong Chen, Di Chen, Wenzhi Zhao, Rongjun Jin, Guanghui Ren, and Jianlan Luo. Act2goal: From world model to general goal-conditioned policy.arXiv preprint arXiv:2512.23541, 2025

    2025

  28. [36]

    Goal-vla: Image-generative vlms as object-centric world models empowering zero-shot robot manipulation.arXiv preprint arXiv:2506.23919, 2025

    Haonan Chen, Jingxiang Guo, Bangjun Wang, Tianrui Zhang, Xuchuan Huang, Boren Zheng, Yiwen Hou, Chenrui Tie, Jiajun Deng, and Lin Shao. Goal-vla: Image-generative vlms as object-centric world models empowering zero-shot robot manipulation.arXiv preprint arXiv:2506.23919, 2025

    2025

  29. [37]

    Gevrm: Goal-expressive video generation model for robust visual manipulation.arXiv preprint arXiv:2502.09268, 2025

    Hongyin Zhang, Pengxiang Ding, Shangke Lyu, Ying Peng, and Donglin Wang. Gevrm: Goal-expressive video generation model for robust visual manipulation.arXiv preprint arXiv:2502.09268, 2025

    2025 arXiv

  30. [38]

    Rt-sketch: Goal-conditioned imitation learning from hand-drawn sketches

    Priya Sundaresan, Quan Vuong, Jiayuan Gu, Peng Xu, Ted Xiao, Sean Kirmani, Tianhe Yu, Michael Stark, Ajinkya Jain, Karol Hausman, et al. Rt-sketch: Goal-conditioned imitation learning from hand-drawn sketches. In 8th Annual Conference on Robot Learning, 2024

    2024

  31. [39]

    Vip: Vision instructed pre-training for robotic manipulation.arXiv preprint arXiv:2410.07169, 2024

    Zhuoling Li, Liangliang Ren, Jinrong Yang, Yong Zhao, Xiaoyang Wu, Zhenhua Xu, Xiang Bai, and Hengshuang Zhao. Vip: Vision instructed pre-training for robotic manipulation.arXiv preprint arXiv:2410.07169, 2024

    2024 arXiv

  32. [40]

    Interleave-vla: Enhancing robot manipulation with interleaved image-text instructions.arXiv preprint arXiv:2505.02152, 2025

    Cunxin Fan, Xiaosong Jia, Yihang Sun, Yixiao Wang, Jianglan Wei, Ziyang Gong, Xiangyu Zhao, Masayoshi Tomizuka, Xue Yang, Junchi Yan, et al. Interleave-vla: Enhancing robot manipulation with interleaved image-text instructions.arXiv preprint arXiv:2505.02152, 2025

    2025

  33. [41]

    Qwen3-vl technical report.arXiv preprint arXiv:2511.21631, 2025

    Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, Wei Ding, Chang Gao, Chunjiang Ge, et al. Qwen3-vl technical report.arXiv preprint arXiv:2511.21631, 2025

    2025 arXiv

  34. [42]

    Paligemma: A versatile 3b vlm for transfer.arXiv preprint arXiv:2407.07726, 2024

    Lucas Beyer, Andreas Steiner, André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, et al. Paligemma: A versatile 3b vlm for transfer.arXiv preprint arXiv:2407.07726, 2024

    2024 arXiv

  35. [43]

    Dinov3.arXiv preprint arXiv:2508.10104, 2025

    Oriane Siméoni, Huy V V o, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, et al. Dinov3.arXiv preprint arXiv:2508.10104, 2025

    2025 arXiv

  36. [44]

    Depth anything: Unleashing the power of large-scale unlabeled data

    Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10371–10381, 2024

    2024

  37. [45]

    Feature pyramid networks for object detection

    Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 2117–2125, 2017

    2017

  38. [46]

    Vision transformer adapter for dense predictions.arXiv preprint arXiv:2205.08534, 2022

    Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, and Yu Qiao. Vision transformer adapter for dense predictions.arXiv preprint arXiv:2205.08534, 2022

    2022 arXiv

  39. [47]

    Decoupled weight decay regularization.International Conference on Learning Representations, 2019

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization.International Conference on Learning Representations, 2019

    2019

  40. [48]

    Robotwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation.arXiv preprint arXiv:2506.18088, 2025

    Tianxing Chen, Zanxin Chen, Baijun Chen, Zijian Cai, Yibin Liu, Zixuan Li, Qiwei Liang, Xianliang Lin, Yiheng Ge, Zhenyu Gu, et al. Robotwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation.arXiv preprint ar...

    2025 arXiv

  41. [49]

    Libero: Benchmarking knowledge transfer for lifelong robot learning.Advances in Neural Information Processing Systems, pages 44776–44791, 2023

    Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning.Advances in Neural Information Processing Systems, pages 44776–44791, 2023

    2023

  42. [50]

    X-vla: Soft-prompted transformer as scalable cross-embodiment vision-language- action model.arXiv preprint arXiv:2510.10274, 2025

    Jinliang Zheng, Jianxiong Li, Zhihao Wang, Dongxiu Liu, Xirui Kang, Yuchun Feng, Yinan Zheng, Jiayin Zou, Yilun Chen, Jia Zeng, et al. X-vla: Soft-prompted transformer as scalable cross-embodiment vision-language- action model.arXiv preprint arXiv:2510.10274, 2025

    2025 arXiv

  43. [51]

    Abot-m0: Vla foundation model for robotic manipulation with action manifold learning.arXiv preprint arXiv:2602.11236, 2026

    Yandan Yang, Shuang Zeng, Tong Lin, Xinyuan Chang, Dekang Qi, Junjin Xiao, Haoyun Liu, Ronghan Chen, Yuzhi Chen, Dongjie Huo, et al. Abot-m0: Vla foundation model for robotic manipulation with action manifold learning.arXiv preprint arXiv:2602.11236, 2026

    2026 arXiv

  44. [52]

    π0.5: a Vision-Language-Action Model with Open-World Generalization.arXiv preprint arXiv:2504.16054, 2025

    Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, et al. π0.5: a Vision-Language-Action Model with Open-World Generalization.arXiv preprint arXiv:2504.16054, 2025

    2025 arXiv

  45. [53]

    Starvla: A lego-like codebase for vision-language-action model developing.arXiv preprint arXiv:2604.05014, 2026

    StarVLA Community. Starvla: A lego-like codebase for vision-language-action model developing.arXiv preprint arXiv:2604.05014, 2026

    2026 arXiv

  46. [54]

    Fine-tuning vision-language-action models: Optimizing speed and success.arXiv preprint arXiv:2502.19645, 2025

    Moo Jin Kim, Chelsea Finn, and Percy Liang. Fine-tuning vision-language-action models: Optimizing speed and success.arXiv preprint arXiv:2502.19645, 2025. 13

    2025 arXiv

  47. [55]

    Gr00t n1: An open foundation model for generalist humanoid robots.arXiv preprint arXiv:2503.14734, 2025

    Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi Fan, Yu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, et al. Gr00t n1: An open foundation model for generalist humanoid robots.arXiv preprint arXiv:2503.14734, 2025

    2025 arXiv

  48. [56]

    Jepa-vla: Video predictive embedding is needed for vla models.arXiv preprint arXiv:2602.11832, 2026

    Shangchen Miao, Ningya Feng, Jialong Wu, Ye Lin, Xu He, Dong Li, and Mingsheng Long. Jepa-vla: Video predictive embedding is needed for vla models.arXiv preprint arXiv:2602.11832, 2026. 14 A Real-Robot Experiment Details Hardware platform.Real-robot experiments are conducted o...

    2026

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.