Pith. sign in

REVIEW 4 major objections 5 minor 66 references

GeniWorld: A Generalizable Interactive World Model for Robotic Manipulation via Visual Actions

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read A robot world model that conditions on rendered visual actions instead of numerical commands generalizes to unseen scenes, evaluates policies, and generates training data.

desk verdict A well-engineered visual-action world model with strong empirical results, though the central generalization claim leans on an untested kinematic-sufficiency assumption. read the letter →

arxiv 2608.06332 v1 pith:UGCKXV2T submitted 2026-08-06 cs.RO

Chenghao Gu , Hanyang Yu , Jingbo Zhang , Haitao Lin , Wenyao Zhang , Jinghe Wang , Hanglei Jin , Shuzhao Xie
show 2 more authors
Jingyan Jiang Zhi Wang
This is my paper
classification cs.RO
keywords worldmodelsrobotmanipulationvisualactionsURDFrenderingvideodiffusionout-of-distributiongeneralizationpolicyevaluationdatasynthesis
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 a robot world model can generalize to unseen environments without large-scale data, provided the robot's actions are fed to the video generator as images—rendered frames of the robot's own articulated body—rather than as numerical vectors. The authors argue that numerical actions entangle robot motion with scene appearance, causing overfitting to the training scene, whereas a rendered "visual action" is spatially grounded and scene-agnostic, so the model learns to predict interaction-driven changes instead of memorizing backgrounds. They report that a model trained only on fixed plain-scene demonstrations produces high-fidelity future frames on randomized unseen scenes, that its predicted success rates track real-world policy success, and that its synthetic trajectories improve downstream policies trained from only 25 demonstrations per task. If correct, this makes a single fixed-scene dataset enough to build a reusable imagination space for robot learning and evaluation.

What carries the argument

The load-bearing device is the visual-action representation. Numerical joint commands are mapped through the robot's URDF model and forward kinematics into dense rendered frames of the robot's articulated body from the target camera viewpoint, with objects and background excluded; a URDF is the standard file format describing a robot's links and joints. Those action frames are encoded by the same causal 3D VAE used for observations and concatenated channel-wise with the noisy video latents, producing a 96-channel input (48 observation channels plus 48 action channels) in which every action pixel is spatially aligned with the scene location it is meant to affect. The combined latent goes into a causal diffusion transformer trained with flow matching, and a causal attention mask forces each predicted frame to depend only on the current action and past context. This design is what carries the decoupling argument: the model sees the robot's motion as pixels, so scene-specific appearance is not required to know what will change, and the pretrained video prior is preserved with only a widened patch-embedding layer.

What would settle it

Set up a scene where a target object moves while the robot arm is completely still—for example, a ball rolling off a ramp after being released—condition GeniWorld on a static-arm action sequence, and check whether the generated frames show the ball moving; if the model leaves the object motionless, the embodiment-kinematics assumption is falsified.

Watch

Extended reading notes

Core claim

The central claim is that conditioning a pretrained video diffusion world model on rendered visual robot motion—rather than on numerical actions, end-effector poses, or skeletons—is what lets it transfer from a single fixed scene to diverse unseen scenes. Concretely, the paper shows that when trained only on 2,250 clean tabletop episodes from the RoboTwin benchmark, GeniWorld predicts future observations in the randomized "Random" episodes with FID 13.08 and FVD 20.15, while the strongest comparison methods degrade to FID 174.52 and FVD 191.26 in the same setting. The same model is then used as an offline evaluator: across four real-world manipulation tasks, simulated policy success rates correlate with physical success rates, including under visual distractors. Finally, the paper claims that GeniWorld can generate 130 synthetic trajectories per task from 25 real demonstrations, and that adding those trajectories raises average real-world policy success from 40.8% to 69.0% across standard, spatial-rearrangement, distractor, novel-instance, and lighting-shift settings.

Load-bearing premise

The load-bearing premise is that everything visible in a manipulation video is driven by how the robot's own body moves; if an object moves by itself, changes appearance, or responds to contact in ways not captured by the robot's rendered motion, the model has no input channel for that and will predict the wrong next frame.

Editorial extensions

If this is right

  • A model trained on a single clean tabletop scene predicts high-fidelity manipulation outcomes in randomized unseen scenes without any adaptation.
  • The same simulator can rank policy checkpoints, because its simulated success rates track real-world success rates even when visual distractors are added.
  • With only 25 real demonstrations per task, GeniWorld-generated trajectories improve downstream policy success in spatial rearrangement, novel object instances, distractors, and lighting shifts.
  • Reducing flow-matching sampling from 50 steps to 5 steps costs about 2% in FVD, enabling roughly 8 Hz closed-loop interaction.

Reading between the lines

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

  • The decoupling recipe is architecture-agnostic: because the only change to the video backbone is a widened patch embedding plus channel-wise action latents, the same visual-action conditioning should transfer to other pretrained video diffusion models.
  • The method inherits a boundary from its URDF renderer: robots without accurate kinematic models, or scenes with fluids, soft bodies, or objects that move independently, would need an extra conditioning channel, so a learned embodiment renderer is a natural next step.
  • The diversity ceiling of the synthetic-data pipeline is set by the image-editing model used to create new initial frames; replacing that editor with a physics-aware scene editor could push the generated trajectories further out of distribution.
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

4 major / 5 minor

Summary. The paper introduces GeniWorld, a visual-action-conditioned world model for robot manipulation. The method converts numerical action sequences into dense visual motion sequences by rendering the robot's URDF model and forward kinematics from the target camera viewpoint, then feeds these visual actions as clean, spatially concatenated latents into a pretrained video diffusion backbone trained with flow matching and causal attention. The authors claim that, even when trained on limited fixed-scene demonstrations, GeniWorld achieves superior in-domain generation quality and robust zero-shot generalization to randomized unseen scenes on the RoboTwin benchmark; that it serves as a reliable offline policy evaluator whose success predictions correlate with real-world performance under perturbations; and that it can synthesize diverse manipulation trajectories from limited real-world demonstrations, improving downstream VLA policy performance under spatial, instance, distractor, and lighting shifts.

Significance. If the central claims are substantiated, this paper makes a useful contribution: it offers a concrete mechanism, URDF-based visual action conditioning, for injecting robot motion into pretrained video generative models, and it evaluates the resulting world model in three downstream-oriented roles (generative simulator, policy evaluator, data synthesizer). The paper's strengths include a clear method formulation, a broad set of ablations over action representations, evaluation on a standard benchmark (RoboTwin) and a real dual-arm platform, and a detailed real-world policy-improvement protocol. However, the key innovation also introduces a serious evaluation confound: the conditioning signal contains a near-ground-truth rendering of the robot's future arm motion, which may make the prediction problem considerably easier for the arm itself and inflate full-frame metrics even if object dynamics are not modeled. This confound, together with untested assumptions about the sufficiency of kinematic conditioning and the absence of quantitative correlation statistics for the policy-evaluation claim, means that the current evidence does not yet support the paper's strongest claims.

major comments (4)
  1. [Section III-B-a and Table I] The conditioning signal m_{t+1:t+H} is a URDF-based rendering of the robot's own future motion from the target camera viewpoint, so the model is given a near-ground-truth depiction of the arm's trajectory. The full-frame metrics PSNR, SSIM, and LPIPS in Table I therefore largely measure how faithfully the model copies the rendered arm over a scene, rather than whether it predicts interaction-driven object dynamics. A model that simply composites the rendered robot arm over the initial frame (or over a slowly updated background) could score well on these metrics without modeling contact, deformation, or object transport. The paper does not include such a 'copy-arm' or 'render-only' baseline, nor does it report object-focused metrics that exclude the robot region. I request that the authors add such a baseline and report metrics restricted to moving objects/manipulated objects, or provide a decomposition of the metrics by static and dynamic regions, to establish that the reported gains reflect interaction modeling rather than conditioning leakage.
  2. [Section III-B-a] The assumption that 'the physical dynamics in manipulation videos are predominantly driven by embodiment kinematics' is load-bearing for the paper's zero-shot generalization claim, but it is not tested. In the RoboTwin Clean-to-Random setting, the Random episodes vary scene appearance, object instances, placements, and layouts, but physical parameters (mass, friction, stiffness) are likely sampled from a narrow distribution centered on the training distribution. The real-world tasks, however, include deformable towel folding and drawer friction, exactly regimes where the object response is not determined by the robot's motion alone. A world model that simply hallucinates plausible interactions from visual priors could achieve the reported FVD/LPIPS scores without modeling the physical coupling. I recommend the authors either (i) provide evidence that the model predicts object motion correctly under varied physical parameters (e.g., by perturbing mass/friction in a simulator and measuring prediction error), (ii) ablate the effect of withholding the exact arm trajectory, or (iii) explicitly discuss and bound the underdetermination. Without this, the generalization claim is not supported beyond the narrow distribution tested.
  3. [Section IV-C and Fig. 7] The claim that GeniWorld serves as a reliable policy evaluator rests on Fig. 7(a), which shows a qualitative correlation between world-model success rates and real-world success rates for four tasks, and on qualitative comparisons in Fig. 7(b). No correlation coefficient, confidence interval, or error bar is reported, and the number of points (four tasks) is tiny. Moreover, the 'success rates' in the world model are judged by human evaluators and a VLM, but the paper does not report inter-evaluator agreement or VLM-human agreement. I request that the authors report a quantitative correlation (e.g., Spearman or Pearson with confidence intervals) across tasks, report per-task numbers in a table, and provide the VLM prompt and a validation of the automated judge against human labels. Otherwise the 'reliable policy evaluator' claim is unsupported.
  4. [Section IV-D-b and IV-D-c] The policy-improvement experiment conflates two sources of novelty: scene diversity introduced by external image-editing models (GPT-Image, Qwen-Image) and dynamics generated by the world model. The diverse initial frames are produced by editing the original scene, and trajectories are generated by replaying recorded actions or by teleoperation through the world model. It is therefore not established that the performance gains in Fig. 9 and Table III come from the world model's ability to synthesize physically consistent interaction trajectories; they may come from simply increasing the visual diversity of training scenes. I request an additional control regime that uses the same edited initial frames and the same action replays but replaces the world model with a non-predictive compositor (e.g., overlaying the rendered robot arm on the edited frame) to isolate the contribution of the world-model dynamics. Without this control, the claim that 'GeniWorld generates diverse manipulation trajectories that improve downstream policy performance' is confounded.
minor comments (5)
  1. [Table I] No error bars, confidence intervals, or number of random seeds are reported for any of the six metrics. The differences between some entries (e.g., EWMScore Clean-to-Clean: 61.80 vs. 58.74) are small enough that variance across seeds may change the ranking; please report mean±std over at least three seeds.
  2. [Section III-B-d, Eq. (1)] The notation in Eq. (1) is unclear: the expectation subscript appears as 'E_{t,s,z_{t+1},\epsilon}' with the loss integrand written as 'v_theta(...) - \dot{z}^{(s)}_{t+1}' without an explicit norm notation; please fix the formatting and clarify the dependence of the velocity field on the language instruction c, which appears in the equation but is not described in the main text or Fig. 2.
  3. [Fig. 1] The figure caption contains duplicated or corrupted text ('InteractiveWorldModel InteractiveWorldModel O!"#'), which appears to be a LaTeX/export artifact; please repair the caption.
  4. [References] Reference [23] is an incomplete citation to a blog post; please replace it with a proper peer-reviewed or archival citation for world models.
  5. [Section II-B-a] The sentence 'directly embedding embodiment motion into future-frame prediction allows the model to faithfully reflect interaction-driven environmental changes' is presented as a conclusion but is the very claim under test; please temper the wording or cite evidence from the ablation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: visual actions are a condition derived from actions, not a fitted prediction of the target observations.

full rationale

The derivation chain is self-contained and externally validated. The world model optimizes the flow-matching objective in Eq. (1) against ground-truth future observation latents; the visual-action condition is computed deterministically from the commanded action sequence through the robot URDF and forward kinematics, independent of the target observations, and only the observation latent is noised and predicted (Section III-D: 'Only the observation latent is noised and predicted; the visual action serves as a clean conditioning signal'). None of the main claims reduces to an input: Clean-to-Random evaluation is on held-out RoboTwin Random episodes, policy-evaluation correlation is compared with real-world success rates, and data-synthesis benefits are measured by real-world policy rollouts. Section III-B-a's kinematic assumption is a genuine correctness risk (object dynamics may be underdetermined by robot motion, especially for deformable or frictional tasks), but it is not circular because the model must still generate the scene response; the assumption is a modeling bet, not a definitional identity. The only evaluation caveat is that the rendered arm supplies part of the future frame, making the task partially inpainting and giving visual actions an advantage over numerical actions on arm pixels; this is a conditioning choice, not a reduction of the claimed prediction to its own inputs. Self-citations are not load-bearing: [9] (KV cache) is an implementation detail and [17] is cited only as background for OOD degradation.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim is an empirical one about a trained model, not a derivation, so the ledger contains no hand-fitted physical constants. The learned neural network weights are estimated by standard flow-matching training and are not ad hoc parameters. The key axioms are the kinematic dominance assumption and the sufficiency of URDF rendering as a conditioning signal.

assumptions (4)
  • domain assumption Physical dynamics in manipulation videos are predominantly driven by embodiment kinematics.
    Invoked in Section III-B-a to justify conditioning on the robot's rendered motion while excluding objects and background; if false, the conditioning signal is insufficient.
  • domain assumption URDF-based forward kinematics rendering from the target camera viewpoint yields a spatially aligned and complete action description.
    Used in Section III-B-a and Fig. 2; the model relies on this rendering to carry all action information.
  • domain assumption Pretrained video generative priors (Wan2.2) are useful for robot-environment interaction modeling.
    Stated in Section III-B-b and related work; the method preserves the pretrained backbone and expects its priors to transfer.
  • standard math Flow matching with a causal mask is a valid training objective for the world model.
    Equation (1) in Section III-B-d; this is standard generative modeling.

how reviews work

0 comments
Cite this review

Pith. "Pith review of GeniWorld: A Generalizable Interactive World Model for Robotic Manipulation via Visual Actions." pith.science (2026). https://pith.science/paper/UGCKXV2T

@misc{pith2026260806332,
  author       = {Pith},
  title        = {Pith review of: GeniWorld: A Generalizable Interactive World Model for Robotic Manipulation via Visual Actions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UGCKXV2T}},
  note         = {Machine review of arXiv:2608.06332}
}
read the original abstract

Generalist robot policies exhibit strong capabilities, but their robustness in complex and unseen environments remains limited. Scaling robot learning and evaluation in diverse real-world environments remains costly and challenging. Action-conditioned world models offer a promising alternative, but they often suffer from limited action controllability and poor generalization to out-of-distribution (OOD) scenarios. To this end, we present GeniWorld, an interactive world model for robots that generalizes robustly across unseen scenarios. Building on pretrained video generative models, we use URDF-based rendering to transform numerical actions into visual action representations, enabling spatially grounded action control. By explicitly decoupling embodiment kinematics from environmental dynamics, our model mitigates scene overfitting and facilitates modeling of robot-environment interactions. To achieve closed-loop control, we construct an autoregressive video prediction model integrated with high-frequency robot kinematic control, enabling interaction with both robot policies and human teleoperators. In our experiments, even when trained solely on limited fixed-scene data, our model achieves superior in-domain performance and robust zero-shot generalization to highly randomized, unseen environments. For downstream applications, GeniWorld serves as a scalable policy evaluator that remains reliable under environmental perturbations. Furthermore, even with limited real-world demonstrations, GeniWorld generates diverse manipulation trajectories within the world model, improving downstream policy performance and robustness in complex environments.

Figures

Figures reproduced from arXiv: 2608.06332 by the authors.

Figure 1
Figure 1. Overview of GeniWorld. GeniWorld is an autoregressive robotic world model that transforms action inputs into visual action representations, enabling closed-loop interaction with human operators and robot policies. Trained on limited scene-specific demonstrations, GeniWorld generalizes to out-of-distribution (OOD) scenarios to produce high-fidelity observation predictions. Furthermore, it synthesizes rich manipulatio… view at source ↗
Figure 2
Figure 2. Overview of the GeniWorld method. We convert robot actions into visual motions via URDF rendering. These motions are subsequently encoded into latent representations and concatenated with noisy video latents. The combined representation serves as input to a causal DiT that predicts future videos via flow matching. During inference, the initial scene image is provided as the first frame. Actions are passed through th… view at source ↗
Figure 3
Figure 3. Qualitative Clean-to-Random world-modeling results. All models are trained on clean tabletop data and evaluated in unseen scenes. GeniWorld preserves the commanded robot motion and produces interaction outcomes that closely match the ground truth, whereas competing methods fail to generate the expected results in out-of-distribution (OOD) scenarios. Furthermore, the ablation results show that our dense visual action… view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: Training convergence across different action representations. Under identical training configurations, we compare generation quality (1− LPIPS; y-axis) against the number of training steps (x-axis) for various action representations. Ours Numerical Actions 50 steps 10 …
Figure 5
Figure 5. Figure 5: Performance with different numbers of flow-matching sampling steps. We evaluate world-modeling quality using 50, 10, and 5 inference steps, where fewer steps correspond to faster inference. Visual action conditioning consistently maintains superior generation quality w…
Figure 6
Figure 6. Figure 6: Real-world experimental setup. Our platform uses a dual-arm Xtrainer robotic system and includes four manipulation tasks. C. World Model for Policy Evaluation We investigate whether GeniWorld can serve as an effec￾tive offline evaluator for downstream policies. By exec…
Figure 8
Figure 8. Figure 8: Teleoperation with the interactive world model. A human oper￾ator teleoperates the robot in real time based on the generated scene, while the model autoregressively generates the corresponding visual responses. b) Diverse Data Synthesis via World Model: Using advanced …
Figure 9
Figure 9. Figure 9: Real-world policy success rates across different settings. Real denotes policies trained strictly on real-world data; Real + Spatial-Gen adds GeniWorld-synthesized data focused on spatial generalization; and Real + Spatial-Gen + Diverse-Gen adds both spatially randomiz…
Figure 11
Figure 11. Figure 11: Performance of policies augmented with GeniWorld￾synthesized data. Even with minimal collected real-world data, policies augmented with GeniWorld-synthesized data demonstrate superior perfor￾mance across diverse OOD conditions. rate from 40.8% to 69.0%. Representative…
Figure 12
Figure 12. Figure 12: Real-world video rollouts under out-of-domain (OOD) conditions. GeniWorld maintains precise motion trajectories and realistic physical interactions even when exposed to unseen object instances, novel backgrounds, and complex environmental distractors [PITH_FULL_IMAGE…
Figure 13
Figure 13. Figure 13: Qualitative results of GeniWorld on in-domain real-world tasks. The predicted video sequences demonstrate accurate spatial trajectories, fine-grained object interaction, and strict visual temporal consistency aligned with ground-truth rollouts [PITH_FULL_IMAGE:figure…
Figure 14
Figure 14. Figure 14: Comparison of real-world policy rollouts. We visualize rollouts of π0 policies trained on real data alone and on real data augmented with GeniWorld-generated trajectories. We found that trained on both real and generated data, π0 shows a better performance on OOD scen…
Figure 15
Figure 15. Figure 15: More visualization of real-world policy rollouts [PITH_FULL_IMAGE:figures/full_fig_p015_15.png]
Figure 16
Figure 16. Figure 16: Qualitative visualizations of generated rollouts on RoboTwin. We show the predictions across Clean-to-Clean (in-domain) and Clean-to￾Random (out-of-distribution) setups. GeniWorld maintains high visual fidelity and precise temporal consistency even under randomized en…
Figure 17
Figure 17. Figure 17: Qualitative comparison of generation rollouts across different action representations [PITH_FULL_IMAGE:figures/full_fig_p017_17.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

66 extracted references · 17 canonical work pages

  1. [1]

    π 0: A vision- language-action flow model for general robot control,

    K. Black, N. Brown, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, L. Groom, K. Hausman, B. Ichteret al., “π 0: A vision- language-action flow model for general robot control,”arXiv preprint arXiv:2410.24164, 2024

    arXiv 2024

  2. [2]

    π ∗ 0.6: A vla that learns from experience,

    P. Intelligence, A. Amin, R. Aniceto, A. Balakrishna, K. Black, K. Conley, G. Connors, J. Darpinian, K. Dhabalia, J. DiCarlo et al., “π ∗ 0.6: A vla that learns from experience,”arXiv preprint arXiv:2511.14759, 2025

    arXiv 2025

  3. [3]

    π 0.7: A steer- able generalist robotic foundation model with emergent capabilities,

    P. Intelligence, B. Ai, A. Amin, R. Aniceto, A. Balakrishna, G. Balke, K. Black, G. Bokinsky, S. Cao, T. Charbonnieret al., “π 0.7: A steer- able generalist robotic foundation model with emergent capabilities,” arXiv preprint arXiv:2604.15483, 2026

    arXiv 2026

  4. [4]

    π 0.5: A vision-language-action model with open-world generalization,

    P. Intelligence, K. Black, N. Brown, J. Darpinian, K. Dhabalia, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusaiet al., “π 0.5: A vision-language-action model with open-world generalization,”arXiv preprint arXiv:2504.16054, 2025

    arXiv 2025

  5. [5]

    Gr00t n1: An open foundation model for generalist humanoid robots,

    J. Bjorck, F. Casta ˜neda, N. Cherniadev, X. Da, R. Ding, L. Fan, Y . Fang, D. Fox, F. Hu, S. Huanget al., “Gr00t n1: An open foundation model for generalist humanoid robots,”arXiv preprint arXiv:2503.14734, 2025

    arXiv 2025

  6. [6]

    Gemini robotics: Bringing ai into the physical world,

    G. R. Team, S. Abeyruwan, J. Ainslie, J.-B. Alayrac, M. G. Arenas, T. Armstrong, A. Balakrishna, R. Baruch, M. Bauza, M. Blokzijlet al., “Gemini robotics: Bringing ai into the physical world,”arXiv preprint arXiv:2503.20020, 2025

    arXiv 2025

  7. [7]

    Posevla: Universal pose pretraining for generalizable vision- language-action policies,

    H. Lin, H. Yu, J. Huang, H. Zhang, Y . Ling, P. Tan, X. Xue, and Y . Fu, “Posevla: Universal pose pretraining for generalizable vision- language-action policies,”arXiv preprint arXiv:2602.19710, 2026

    arXiv 2026

  8. [8]

    Dreamvla: a vision-language-action model dreamed with comprehensive world knowledge,

    W. Zhang, H. Liu, Z. Qi, Y . Wang, X. Yu, J. Zhang, R. Dong, J. He, H. Wang, Z. Zhanget al., “Dreamvla: a vision-language-action model dreamed with comprehensive world knowledge,”Advances in Neural Information Processing Systems, vol. 38, pp. 24 195–24 228, 2026

    work page 2026

Show all 66 references
  1. [9]

    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. Zhuet al., “Causal world modeling for robot control,” arXiv preprint arXiv:2601.21998, 2026

    2026 arXiv

  2. [10]

    World action models are zero- shot policies,

    S. Ye, Y . Ge, K. Zheng, S. Gao, S. Yu, G. Kurian, S. Indupuru, Y . L. Tan, C. Zhu, J. Xianget al., “World action models are zero- shot policies,”arXiv preprint arXiv:2602.15922, 2026

    2026 arXiv

  3. [11]

    Dis- entangled robot learning via separate forward and inverse dynamics pretraining,

    W. Zhang, B. Zhang, Z. Qi, W. Zeng, X. Jin, and L. Zhang, “Dis- entangled robot learning via separate forward and inverse dynamics pretraining,”arXiv preprint arXiv:2604.16391, 2026

    2026 arXiv

  4. [12]

    Fast-wam: Do world action models need test-time future imagination?

    T. Yuan, Z. Dong, Y . Liu, and H. Zhao, “Fast-wam: Do world action models need test-time future imagination?”arXiv preprint arXiv:2603.16666, 2026

    2026 arXiv

  5. [13]

    Libero-pro: Towards robust and fair evaluation of vision-language-action models beyond memorization,

    X. Zhou, Y . Xu, G. Tie, Y . Chen, G. Zhang, D. Chu, P. Zhou, and L. Sun, “Libero-pro: Towards robust and fair evaluation of vision-language-action models beyond memorization,”arXiv preprint arXiv:2510.03827, 2025

    2025 arXiv

  6. [14]

    Libero-plus: In-depth robustness analysis of vision- language-action models,

    S. Fei, S. Wang, J. Shi, Z. Dai, J. Cai, P. Qian, L. Ji, X. He, S. Zhang, Z. Feiet al., “Libero-plus: In-depth robustness analysis of vision- language-action models,”arXiv preprint arXiv:2510.13626, 2025

    2025 arXiv

  7. [15]

    Robotwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation,

    T. Chen, Z. Chen, B. Chen, Z. Cai, Y . Liu, Z. Li, Q. Liang, X. Lin, Y . Ge, Z. Guet al., “Robotwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation,”arXiv preprint arXiv:2506.18088, 2025

    2025 arXiv

  8. [16]

    Qwen-robotmanip technical report: Alignment unlocks scale for robotic manipulation foundation models,

    H. Yuan, Z. Liang, A. Chen, Y . Wang, H. Li, P. Lin, Y . Huang, Z. Lei, T. Zhang, J. Zhanget al., “Qwen-robotmanip technical report: Alignment unlocks scale for robotic manipulation foundation models,” arXiv preprint arXiv:2606.17846, 2026

    2026 arXiv

  9. [17]

    Maskwam: Unifying mask prompting and prediction for world-action models,

    H. Yu, H. Lin, J. Zhang, W. Zhang, C. Gu, H. Li, and P. Tan, “Maskwam: Unifying mask prompting and prediction for world-action models,”arXiv preprint arXiv:2606.13515, 2026

    2026 arXiv

  10. [18]

    Ctrl-World: A control- lable generative world model for robot manipulation,

    Y . Guo, L. X. Shi, J. Chen, and C. Finn, “Ctrl-World: A control- lable generative world model for robot manipulation,”arXiv preprint arXiv:2510.10125, 2025

    2025 arXiv

  11. [19]

    IRASim: A fine-grained world model for robot manipulation,

    F. Zhu, H. Wu, S. Guo, Y . Liu, C. Cheang, and T. Kong, “IRASim: A fine-grained world model for robot manipulation,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2025

    2025

  12. [20]

    WorldEval: World model as real-world robot policies evaluator,

    Y . Li, Y . Zhu, J. Wen, C. Shen, and Y . Xu, “WorldEval: World model as real-world robot policies evaluator,”arXiv preprint arXiv:2505.19017, 2025

    2025 arXiv

  13. [21]

    WorldGym: World model as an environment for policy evaluation,

    J. Quevedo, A. K. Sharma, Y . Sun, V . Suryavanshi, P. Liang, and S. Yang, “WorldGym: World model as an environment for policy evaluation,”arXiv preprint arXiv:2506.00613, 2025

    2025

  14. [22]

    Interactive world simulator for robot policy training and evaluation,

    Y . Wang, R. Syed, F. Wu, M. Zhang, A. Onol, J. Barreiros, H. Nayyeri, T. Dear, H. Zhang, and Y . Li, “Interactive world simulator for robot policy training and evaluation,”arXiv preprint arXiv:2603.08546, 2026

    2026

  15. [23]

    World Models - David Ha, J ¨urgen Schmidhuber

    E. Goodman, “World Models - David Ha, J ¨urgen Schmidhuber.”

  16. [24]

    Hafner, T

    D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi. Dream to Control: Learning Behaviors by Latent Imagination

  17. [25]

    B. Hou, G. Li, J. Jia, T. An, X. Guo, S. Leng, H. Geng, Y . Ze, T. Harada, P. Torr, O. Mees, M. Pollefeys, Z. Liu, J. Wu, P. Abbeel, J. Malik, Y . Du, and J. Yang. World Model for Robot Learning: A Comprehensive Survey

  18. [26]

    Genie: Generative interactive environments,

    J. Bruce, M. D. Dennis, A. Edwards, J. Parker-Holder, Y . Shi, E. Hughes, M. Lai, A. Mavalankar, R. Steigerwald, C. Appset al., “Genie: Generative interactive environments,” inForty-first Interna- tional Conference on Machine Learning, 2024

    2024

  19. [27]

    V-jepa 2: Self- supervised video models enable understanding, prediction and plan- ning,

    M. Assran, A. Bardes, D. Fan, Q. Garrido, R. Howes, M. Muckley, A. Rizvi, C. Roberts, K. Sinha, A. Zholuset al., “V-jepa 2: Self- supervised video models enable understanding, prediction and plan- ning,”arXiv preprint arXiv:2506.09985, 2025

    2025 arXiv

  20. [28]

    Agarwal, A

    NVIDIA, N. Agarwal, A. Ali, M. Bala, Y . Balaji, E. Barker, T. Cai, P. Chattopadhyay, Y . Chen, Y . Cui, Y . Ding, D. Dworakowski, J. Fan, M. Fenzi, F. Ferroni, S. Fidler, D. Fox, S. Ge, Y . Ge, J. Gu, S. Gururani, E. He, J. Huang, J. Huffman, P. Jannaty, J. Jin, S. W. Kim, G....

  21. [29]

    [Online]

    [2512.15840] Large Video Planner Enables Generalizable Robot Control. [Online]. Available: https://arxiv.org/abs/2512.15840

    arXiv

  22. [30]

    C. Zhu, R. Yu, S. Feng, B. Burchfiel, P. Shah, and A. Gupta. Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets

  23. [31]

    ivideogpt: Interactive videogpts are scalable world models,

    J. Wu, S. Yin, N. Feng, X. He, D. Li, J. Hao, and M. Long, “ivideogpt: Interactive videogpts are scalable world models,”Advances in Neural Information Processing Systems, vol. 37, pp. 68 082–68 119, 2024

    2024

  24. [32]

    Evaluating gemini robotics policies in a veo world simulator,

    G. R. Team, K. Choromanski, C. Devin, Y . Du, D. Dwibedi, R. Gao, A. Jindal, T. Kipf, S. Kirmani, I. Lealet al., “Evaluating gemini robotics policies in a veo world simulator,”arXiv preprint arXiv:2512.10675, 2025

    2025

  25. [33]

    Learning interactive real-world simulators,

    S. Yang, Y . Du, K. Ghasemipour, J. Tompson, L. Kaelbling, D. Schu- urmans, and P. Abbeel, “Learning interactive real-world simulators,” arXiv preprint arXiv:2310.06114, 2023

    2023 arXiv

  26. [34]

    Sword: Style-robust world models as simulators via dynamic latent bootstrapping for vla policy post-training,

    J. Gao, Y . Guo, Z. Guan, W. Huang, W. Ma, X. Xiao, J. Xiong, and S. Wen, “Sword: Style-robust world models as simulators via dynamic latent bootstrapping for vla policy post-training,”arXiv preprint arXiv:2605.07288, 2026

    2026 arXiv

  27. [35]

    Wovr: World models as reliable simulators for post-training vla policies with rl,

    Z. Jiang, S. Zhou, Y . Jiang, Z. Huang, M. Wei, Y . Chen, T. Zhou, Z. Guo, H. Lin, Q. Zhanget al., “Wovr: World models as reliable simulators for post-training vla policies with rl,”arXiv preprint arXiv:2602.13977, 2026

    2026 arXiv

  28. [36]

    Wmpo: World model-based policy optimization for vision-language-action models,

    F. Zhu, Z. Yan, Z. Hong, Q. Shou, X. Ma, and S. Guo, “Wmpo: World model-based policy optimization for vision-language-action models,” arXiv preprint arXiv:2511.09515, 2025

    2025

  29. [37]

    EnerVerse-AC: Envisioning embodied environments with action con- dition,

    Y . Jiang, S. Chen, S. Huang, L. Chen, P. Zhou, Y . Liaoet al., “EnerVerse-AC: Envisioning embodied environments with action con- dition,”arXiv preprint arXiv:2505.09723, 2025

    2025 arXiv

  30. [38]

    Ge-sim 2.0: A roadmap towards comprehensive closed-loop video world simulators for robotic manipulation,

    B. Qiu, L. Chen, Y . Liao, N. Wang, L. Wang, J. Luo, W. Zhao, S. Chen, D. Chen, Y . Liet al., “Ge-sim 2.0: A roadmap towards comprehensive closed-loop video world simulators for robotic manipulation,”arXiv preprint arXiv:2605.27491, 2026

    2026 arXiv

  31. [39]

    Precise action-to-video generation through visual action prompts,

    Y . Wang, C. Wen, H. Guo, S. Peng, M. Qin, H. Baoet al., “Precise action-to-video generation through visual action prompts,” inProceed- ings of the IEEE/CVF International Conference on Computer Vision, 2025, pp. 12 713–12 724

    2025

  32. [40]

    OSCAR: Omni-embodiment action-conditioned world model for robotics,

    Z. Wu and J. Gao, “OSCAR: Omni-embodiment action-conditioned world model for robotics,”arXiv preprint arXiv:2606.04463, 2026

    2026 arXiv

  33. [41]

    BridgeV2W: Bridging video generation models to embodied world models via embodiment masks,

    Y . Chen, P. Li, J. Yang, K. He, X. Wu, Y . Xuet al., “BridgeV2W: Bridging video generation models to embodied world models via embodiment masks,”arXiv preprint arXiv:2602.03793, 2026

    2026

  34. [42]

    Kinema4d: Kinematic 4d world modeling for spatiotemporal embodied simula- tion,

    M. Xu, T. Zhang, T. Liu, Z. Chen, X. Han, and Z. Liu, “Kinema4d: Kinematic 4d world modeling for spatiotemporal embodied simula- tion,”arXiv preprint arXiv:2603.16669, 2026

    2026

  35. [43]

    Wan-move: Motion-controllable video generation via latent trajectory guidance,

    R. Chu, Y . He, Z. Chen, S. Zhang, X. Xu, D. W ANG, H. Yi, X. Liu, H. Zhao, Y . Liuet al., “Wan-move: Motion-controllable video generation via latent trajectory guidance,”Advances in Neural Information Processing Systems, vol. 38, pp. 404–432, 2026

    2026

  36. [44]

    Robogsim: A real2sim2real robotic gaussian splatting simulator,

    X. Li, J. Li, Z. Zhang, R. Zhang, F. Jia, T. Wang, H. Fan, K.-K. Tseng, and R. Wang, “Robogsim: A real2sim2real robotic gaussian splatting simulator,”arXiv preprint arXiv:2411.11839, 2024

    2024 arXiv

  37. [45]

    High-fidelity simulated data generation for real-world zero-shot robotic manipulation learning with gaussian splatting,

    H. Zhao, C. Zeng, L. Zhuang, Y . Zhao, S. Xue, H. Wang, X. Zhao, Z. Li, K. Li, S. Huanget al., “High-fidelity simulated data generation for real-world zero-shot robotic manipulation learning with gaussian splatting,”IEEE Robotics and Automation Letters, 2026

    2026

  38. [46]

    Novel demonstration generation with gaussian splatting enables ro- bust one-shot manipulation,

    S. Yang, W. Yu, J. Zeng, J. Lv, K. Ren, C. Lu, D. Lin, and J. Pang, “Novel demonstration generation with gaussian splatting enables ro- bust one-shot manipulation,”arXiv preprint arXiv:2504.13175, 2025

    2025 arXiv

  39. [47]

    Igen: Scalable data generation for robot learning from open-world images,

    C. Gu, H. Kang, J. Lin, J. Wang, D. Wu, S. Xie, F. Huang, J. Ge, Z. Gong, L. Liet al., “Igen: Scalable data generation for robot learning from open-world images,”arXiv preprint arXiv:2512.01773, 2025

    2025 arXiv

  40. [48]

    Roboengine: Plug-and-play robot data augmentation with semantic robot segmen- tation and background generation,

    C. Yuan, S. Joshi, S. Zhu, H. Su, H. Zhao, and Y . Gao, “Roboengine: Plug-and-play robot data augmentation with semantic robot segmen- tation and background generation,” in2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2025, pp. 7622–7629

    2025

  41. [49]

    Anchordream: Repurposing video dif- fusion for embodiment-aware robot data synthesis,

    J. Ye, R. Xue, B. Van Hoorick, P. Tokmakov, M. Z. Irshad, Y . Wang, and V . Guizilini, “Anchordream: Repurposing video dif- fusion for embodiment-aware robot data synthesis,”arXiv preprint arXiv:2512.11797, 2025

    2025 arXiv

  42. [50]

    Cosmos-transfer1: Conditional world generation with adaptive multimodal control,

    H. A. Alhaija, J. Alvarez, M. Bala, T. Cai, T. Cao, L. Cha, J. Chen, M. Chen, F. Ferroni, S. Fidleret al., “Cosmos-transfer1: Conditional world generation with adaptive multimodal control,”arXiv preprint arXiv:2503.14492, 2025

    2025 arXiv

  43. [51]

    Dream- Dojo: A generalist robot world model from large-scale human videos,

    S. Gao, W. Liang, K. Zheng, A. Malik, S. Ye, S. Yuet al., “Dream- Dojo: A generalist robot world model from large-scale human videos,” arXiv preprint arXiv:2602.06949, 2026

    2026 arXiv

  44. [52]

    Dreamgen: Unlocking generaliza- tion in robot learning through video world models,

    J. Jang, S. Ye, Z. Lin, J. Xiang, J. Bjorck, Y . Fang, F. Hu, S. Huang, K. Kundalia, Y .-C. Linet al., “Dreamgen: Unlocking generaliza- tion in robot learning through video world models,”arXiv preprint arXiv:2505.12705, 2025

    2025 arXiv

  45. [53]

    G. R. Team, K. Choromanski, C. Devin, Y . Du, D. Dwibedi, R. Gao, A. Jindal, T. Kipf, S. Kirmani, I. Leal, F. Liu, A. Majumdar, A. Mar- mon, C. Parada, Y . Rubanova, D. Shah, V . Sindhwani, J. Tan, F. Xia, T. Xiao, S. Yang, W. Yu, and A. Zhou. Evaluating Gemini Robotics Polici...

  46. [54]

    Wan: Open and advanced large-scale video generative models,

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

    2025 arXiv

  47. [55]

    Self forcing: Bridging the train-test gap in autoregressive video diffusion,

    X. Huang, Z. Li, G. He, M. Zhou, and E. Shechtman, “Self forcing: Bridging the train-test gap in autoregressive video diffusion,”Advances in Neural Information Processing Systems, vol. 38, pp. 167 283– 167 308, 2026

    2026

  48. [56]

    Flow matching for generative modeling,

    Y . Lipman, R. T. Chen, H. Ben-Hamu, M. Nickel, and M. Le, “Flow matching for generative modeling,”arXiv preprint arXiv:2210.02747, 2022

    2022 arXiv

  49. [57]

    WorldArena: A unified benchmark for evaluating perception and functional utility of embodied world models,

    Y . Shang, Z. Li, Y . Ma, W. Su, X. Jin, Z. Wanget al., “WorldArena: A unified benchmark for evaluating perception and functional utility of embodied world models,”arXiv preprint arXiv:2602.08971, 2026

    2026

  50. [58]

    Image quality assessment: from error visibility to structural similarity,

    Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,”IEEE transactions on image processing, vol. 13, no. 4, pp. 600–612, 2004

    2004

  51. [59]

    The unreasonable effectiveness of deep features as a perceptual metric,

    R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in 2018 IEEE/CVF conference on computer vision and pattern recogni- tion. IEEE, 2018, pp. 586–595

    2018

  52. [60]

    Gans trained by a two time-scale update rule converge to a local nash equilibrium,

    M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,”Advances in neural information processing systems, vol. 30, 2017

    2017

  53. [61]

    Towards accurate generative models of video: A new metric & challenges,

    T. Unterthiner, S. Van Steenkiste, K. Kurach, R. Marinier, M. Michal- ski, and S. Gelly, “Towards accurate generative models of video: A new metric & challenges,”arXiv preprint arXiv:1812.01717, 2018

    2018 arXiv

  54. [62]

    Adding conditional control to text-to-image diffusion models,

    L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” in2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023, pp. 3813–3824

    2023

  55. [63]

    Qwen3-vl technical report,

    S. Bai, Y . Cai, R. Chen, K. Chen, X. Chen, Z. Cheng, L. Deng, W. Ding, C. Gao, C. Geet al., “Qwen3-vl technical report,”arXiv preprint arXiv:2511.21631, 2025

    2025 arXiv

  56. [64]

    Gpt-4 technical report,

    J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkatet al., “Gpt-4 technical report,”arXiv preprint arXiv:2303.08774, 2023

    2023 arXiv

  57. [65]

    Introducing our latest image generation model in the API,

    OpenAI, “Introducing our latest image generation model in the API,” https://openai.com/index/image-generation-api/, Apr. 2025

    2025

  58. [66]

    Qwen-image technical report,

    C. Wu, J. Li, J. Zhou, J. Lin, K. Gao, K. Yan, S.-m. Yin, S. Bai, X. Xu, Y . Chenet al., “Qwen-image technical report,”arXiv preprint arXiv:2508.02324, 2025. APPENDIXI TRAININGDETAILS A. Model Architecture & Training Setup Our world model backbone is built upon the Wan2.2- TI2...

    2025 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.