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RoboTALES turns video generators into robot policies by anchoring imagined futures with LLM subgoals and VLM rewards

2026-07-08 19:13 UTC pith:RHFWO573

load-bearing objection Clean systems recipe for LLM-guided, VLM-scored video futures in robot learning; the anti-drift claim hangs on an unshown VLM–progress link. the 3 major comments →

arxiv 2607.06018 v1 pith:RHFWO573 submitted 2026-07-07 cs.RO

RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures

Hanan Gani , Tejal Kulkarni , Madhoolika Chodavarapu , Nicklas Hansen , Manmohan Chandraker This is my paper
classification cs.RO
keywords robot learningvideo generative modelsvisuomotor controlhierarchical planningvision-language modelstask-aligned futureslong-horizon manipulationRoboCasa
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Pretrained video generative models can imagine what a robot might see next, but those imagined futures often wander away from the task and do not reliably tell the robot what action to take. RoboTALES is a single-stage framework that fixes both problems. A hierarchical language model first breaks a complex instruction into ordered subgoals that steer the generator's imagination. A vision-language model then scores the imagined video clips and feeds those scores back as rewards so the generator stays locked on task progress. The resulting task-aligned simulated futures are used to train a robot policy. On the RoboCasa and LIBERO10 manipulation suites the method consistently beats prior approaches, with the largest gains on long-horizon tasks that require multi-step reasoning.

Core claim

RoboTALES shows that a video generator can be turned into a reliable source of robot policies when it is jointly guided by hierarchical LLM subgoal planning and VLM-based reward feedback. The two signals keep the generator's rollouts temporally consistent and goal-focused, so the actions extracted from them succeed on diverse long-horizon manipulation tasks.

What carries the argument

The dual-anchor loop: a hierarchical LLM planner that decomposes the task into subgoals and a VLM critic that scores imagined futures with task-progress rewards. Together they keep the video generator's internal representations and rollouts aligned with the intended goal.

Load-bearing premise

The method assumes that a vision-language model's reward scores on synthetic imagined videos are accurate enough and well enough aligned with true task progress to keep the generator goal-focused and to yield policies that work in real evaluation environments.

What would settle it

Train and evaluate the same generator with the VLM critic ablated or replaced by random rewards; if success rates on long-horizon RoboCasa and LIBERO10 tasks collapse to the level of unguided video-generation baselines, the claim that VLM feedback is what keeps futures task-aligned is falsified.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Robot policies extracted from RoboTALES futures will outperform existing video-generation and imitation baselines on multi-step household manipulation benchmarks.
  • Long-horizon tasks that previously drifted under pure video imagination become solvable once subgoal guidance and reward feedback are added.
  • A single-stage training pipeline can replace multi-stage planning-plus-control pipelines that separately invent futures and then learn actions.
  • Public code and models enable direct reproduction of the gains on RoboCasa and LIBERO10.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same dual-anchor idea could be tried with smaller, domain-specific language and vision models if full LLM/VLM stacks prove too heavy for onboard robots.
  • If VLM reward noise is the main remaining failure mode, calibrating the critic on a small set of real robot videos might further close the sim-to-real gap.
  • Hierarchical subgoal plans may transfer to other generative backbones (diffusion, flow matching) beyond the video models tested here.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. RoboTALES is a single-stage framework that trains robot policies from task-aligned simulated futures produced by a pretrained video generative model. It introduces (1) a hierarchical LLM planner that decomposes tasks into subgoals to condition imagination, and (2) a VLM-based critic that scores imagined rollouts and supplies reward feedback so the generator stays goal-focused and action-conditional. The resulting temporally consistent futures are used to extract policies. The paper reports consistent outperformance over existing methods on RoboCasa and LIBERO10 manipulation benchmarks, with the largest gains on long-horizon tasks, and releases code and models.

Significance. If the results hold under scrutiny, the work offers a practical route to make pretrained video generators usable for visuomotor policy learning by coupling hierarchical language reasoning with VLM reward feedback, addressing drift and weak action-conditioning that currently limit imagination-based control. Public code and models support reproducibility. The contribution is primarily empirical and systems-oriented rather than a new theoretical guarantee; its value depends on whether the VLM critic truly aligns synthetic futures with task progress and whether gains are attributable to the claimed mechanisms rather than planner capacity or backbone scale alone.

major comments (3)
  1. The central anti-drift and task-alignment claim rests on the VLM critic assigning rewards on synthetic video that track true task progress. The manuscript needs an explicit calibration or correlation analysis (VLM scores vs. ground-truth success/progress on held-out real or sim trajectories in the same visual domain) and a control that freezes the hierarchical planner while replacing VLM rewards with constant, noisy, or shuffled scores. Without this, long-horizon gains on RoboCasa/LIBERO10 cannot be attributed to innovation (2) rather than the LLM planner, video-backbone capacity, or VLM idiosyncrasies.
  2. Headline outperformance is asserted for RoboCasa and LIBERO10, especially long-horizon tasks, but the evaluation must report full tables with baselines, sample sizes, seeds/error bars, and ablations that isolate (a) hierarchical LLM subgoals alone, (b) VLM critic alone, and (c) their combination against strong video-policy and hierarchical-planning baselines. Absent these, the claim that the joint framework is necessary and superior remains under-supported.
  3. Action-conditioning of the video generator is stated as a limitation of prior work that RoboTALES fixes, yet the training objective that makes rollouts reliably action-conditional (and how actions are extracted into the final policy) needs a precise statement—loss terms, conditioning interface, and any distillation or inverse-dynamics step—so that the single-stage claim can be verified and compared to two-stage imagination-then-plan pipelines.
minor comments (4)
  1. Define notation for subgoal sequences, VLM reward signals, and the generator conditioning interface early and consistently; the abstract-level description leaves the single-stage training loop underspecified for replication from text alone.
  2. Clarify whether the VLM critic family overlaps any automatic evaluation judges used on RoboCasa/LIBERO10, and report any reward-threshold or weighting choices as free parameters.
  3. Add a short limitations discussion on VLM bias on synthetic frames and failure modes when hierarchical decompositions are incorrect.
  4. Ensure figure captions and method diagrams label the two innovations and the policy-extraction path so readers can map claims to components without relying solely on the abstract.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for a careful and constructive review. The three major comments correctly identify places where the manuscript must more rigorously attribute long-horizon gains to the VLM critic, isolate the hierarchical planner from the critic, and state the action-conditioning objective with enough precision to support the single-stage claim. We agree with the substance of all three points and will revise the paper accordingly: adding VLM–progress calibration and reward-control experiments, expanding evaluation tables with seeds/error bars and the requested ablations, and writing an explicit statement of the conditioning interface, losses, and policy extraction path. We believe these revisions will make the empirical contribution clearer and more attributable without changing the core method.

read point-by-point responses

  1. Referee: The central anti-drift and task-alignment claim rests on the VLM critic assigning rewards on synthetic video that track true task progress. The manuscript needs an explicit calibration or correlation analysis (VLM scores vs. ground-truth success/progress on held-out real or sim trajectories in the same visual domain) and a control that freezes the hierarchical planner while replacing VLM rewards with constant, noisy, or shuffled scores. Without this, long-horizon gains on RoboCasa/LIBERO10 cannot be attributed to innovation (2) rather than the LLM planner, video-backbone capacity, or VLM idiosyncrasies.

    Authors: We agree. Attribution of anti-drift and task alignment to the VLM critic is not yet supported by the analyses the referee requests, and long-horizon gains cannot be cleanly credited to innovation (2) without them. In the revision we will (i) report an explicit calibration/correlation study of VLM reward scores against ground-truth success and intermediate progress on held-out trajectories in the same visual domains used for RoboCasa and LIBERO10, and (ii) add the requested control suite that freezes the hierarchical LLM planner and replaces VLM rewards with constant, noisy, and shuffled scores (and, where informative, a no-critic baseline). We will discuss how strongly VLM scores track true progress and how much of the long-horizon improvement remains under degraded reward signals. These additions directly address the attribution concern. revision: yes

  2. Referee: Headline outperformance is asserted for RoboCasa and LIBERO10, especially long-horizon tasks, but the evaluation must report full tables with baselines, sample sizes, seeds/error bars, and ablations that isolate (a) hierarchical LLM subgoals alone, (b) VLM critic alone, and (c) their combination against strong video-policy and hierarchical-planning baselines. Absent these, the claim that the joint framework is necessary and superior remains under-supported.

    Authors: We agree that the current evaluation presentation under-supports the claim that the joint framework is necessary and superior. The revision will include full result tables for RoboCasa and LIBERO10 with all baselines, sample sizes, multiple seeds, and error bars, with long-horizon subsets highlighted. We will also report the three ablations requested—(a) hierarchical LLM subgoals alone, (b) VLM critic alone, and (c) their combination—alongside strong video-policy and hierarchical-planning baselines already used in the paper, so that the contribution of each component and of the joint system is visible. Where a component is already partially present in the experiments, we will reorganize and complete the comparison rather than claim novelty for missing cells. This should make the necessity and superiority claims empirically checkable. revision: yes

  3. Referee: Action-conditioning of the video generator is stated as a limitation of prior work that RoboTALES fixes, yet the training objective that makes rollouts reliably action-conditional (and how actions are extracted into the final policy) needs a precise statement—loss terms, conditioning interface, and any distillation or inverse-dynamics step—so that the single-stage claim can be verified and compared to two-stage imagination-then-plan pipelines.

    Authors: We agree that the manuscript does not yet state the action-conditioning path with enough precision for the single-stage claim to be verified or fairly compared to two-stage imagination-then-plan pipelines. In the revision we will add a dedicated subsection that specifies: (i) the conditioning interface (how actions and hierarchical subgoals enter the video generator), (ii) the full training objective and loss terms that encourage action-conditional, task-aligned rollouts, including the role of VLM reward feedback, and (iii) exactly how actions are obtained for the final robot policy (e.g., any inverse-dynamics, distillation, or direct readout step) and how this remains a single training stage rather than a separate plan-then-act pipeline. We will also briefly contrast this interface with typical two-stage setups so the single-stage claim is operationally clear. No change to the method is required for this clarification; the text will match the implemented system. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical training-and-evaluate pipeline; reported gains are not forced by definition or by renaming fitted inputs as predictions.

full rationale

RoboTALES is an empirical robotics/ML methods paper. Its claimed chain is: hierarchical LLM subgoal planning plus VLM reward feedback anchors a video generator so that imagined futures stay task-aligned, and policies trained on those futures outperform baselines on RoboCasa and LIBERO10 (especially long-horizon). That chain is a standard learn–evaluate loop against external sim benchmarks and published baselines; success rates are measured outcomes, not quantities that equal the training inputs by construction. None of the six circularity patterns is exhibited: there is no self-definitional identity (X defined as Y then “derived” as Y), no fitted scalar renamed as a prediction of a closely related quantity, no load-bearing uniqueness theorem imported from overlapping authors, no ansatz smuggled solely via self-citation, and no mere renaming of a known empirical pattern. Concerns that the VLM critic may be poorly calibrated on synthetic frames, or that reward shaping may have been tuned on the same suites, are validity/correctness risks about whether the causal story is true—not circularity under the stated criteria. Without a quoteable reduction of a headline result to its own inputs, the honest finding is score 0 with empty steps.

Axiom & Free-Parameter Ledger

3 free parameters · 4 axioms · 1 invented entities

Abstract-only audit. Free parameters are the usual ML knobs plus planner/critic design choices not fixed by theory. Axioms are domain assumptions about LLM decomposition quality, VLM reward fidelity on synthetic video, and usefulness of guided video futures for policy learning. No new physical particles or forces; the named framework is the invented system entity. No parameter-free derivation.

free parameters (3)
  • VLM critic reward weights/thresholds
    Reward-based feedback from the VLM critic requires scoring or weighting choices that shape training; values not specified in the abstract.
  • LLM hierarchical decomposition granularity
    How tasks are split into subgoals (number, abstraction level, replan frequency) is a design choice that can be tuned to the reported benchmarks.
  • video-generator and policy training hyperparameters
    Learning rates, loss weights, horizon lengths, and related training knobs are required for any such pipeline and are not fixed in the abstract.
axioms (4)
  • domain assumption Pretrained video generative models can produce useful action-relevant futures when guided by language subgoals
    Stated motivation and solution premise of the abstract; without it the framework has no backbone.
  • domain assumption Hierarchical LLM planners produce subgoal sequences that correctly capture task structure on the target domains
    Innovation (1) depends on decomposition quality for complex and long-horizon tasks.
  • domain assumption VLM-based critics assign rewards on imagined video that correlate with true task progress
    Innovation (2) and the claim of goal-focused internal representations rest on this correlation.
  • domain assumption Guided simulated futures are sufficient training signal for policies that succeed on RoboCasa/LIBERO10 evaluation
    Links imagination quality to the reported policy outperformance claim.
invented entities (1)
  • RoboTALES single-stage framework no independent evidence
    purpose: Unify hierarchical LLM planning, VLM critic feedback, and video-model futures into one robot policy-learning pipeline
    Named systems contribution. Independent evidence would be external replications or real-robot results beyond the paper’s own benchmarks; not available from the abstract.

pith-pipeline@v0.9.1-grok · 6317 in / 2683 out tokens · 62360 ms · 2026-07-08T19:13:15.986658+00:00 · methodology

0 comments
read the original abstract

Pretrained video generative models are promising backbones for visuomotor control, but their imagined futures often drift from task intent and are not reliably action-conditional. As a result, these models can be difficult to use for planning or policy extraction. To address these limitations, we propose RoboTALES, a single-stage framework that learns task-aligned simulated futures and uses them to train robot policies. Our approach introduces two key innovations: (1) a hierarchical LLM-based planner that breaks complex tasks into a sequence of subgoals to guide the model's imagination; and (2) a VLM-based critic that evaluates these ``imagined'' futures and uses reward-based feedback to keep the model's internal representations focused on the goal. By anchoring the video generator in abstract reasoning, we produce temporally consistent rollouts and more coherent actions. We evaluate RoboTALES on diverse manipulation tasks from RoboCasa and LIBERO10, and show that our method consistently outperforms existing methods, especially in long-horizon tasks. Our code and models are publicly available at https://github.com/hananshafi/RoboTALES.

Figures

Figures reproduced from arXiv: 2607.06018 by Hanan Gani, Madhoolika Chodavarapu, Manmohan Chandraker, Nicklas Hansen, Tejal Kulkarni.

Figure 1
Figure 1. Figure 1: Overview. An LLM Planner decomposes the task instruction (e.g., “make a coffee”) into ordered subtasks that condition the video generator to generate future frames for each milestone. A frozen VLM Critic scores these imagined rollouts against the goal instruction, steering the video generator model toward semantically aligned futures. The resulting goal-conditioned features drive the policy to produce prec… view at source ↗
Figure 2
Figure 2. Figure 2: RoboTALES Architecture and Training. The Planner FP decomposes the task instruction τ into ordered subtasks, forming the augmented plan C ∗ . The video generator Gθ is jointly trained with the Action UNet πϕ in a single stage. Gθ receives a video diffusion loss and reward feedback from a frozen VLM Critic FR, while πϕ is trained with an action diffusion loss conditioned on the decoder features of Gθ. Cruci… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative Comparison of Task Execution [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Impact of the Planner. Plan￾ner provides structured subgoals, improv￾ing performance when trained with the video generator. Naively augmenting plan￾ner with baseline (purple graph) without adaptation results in performance degrada￾tion. (a) Trajectory Quality (b) Robustness (c) Latent jerk [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Analysis.(a) Normalized object-to-end-effector distance over time. Our method shows consistent purposeful behavior and lower variance, (b) Success rate (%) under increasing observation noise. Our method maintains significantly higher performance across all noise levels, demonstrating robust semantic representations. (c) RoboTALES produces smoother latent dynamics with lower jerk, indicating more tem￾porall… view at source ↗
Figure 7
Figure 7. Figure 7: Critic reward predicts task success. Calibration by reward quartile (left) and ROC comparing reward aggregations (right) over 360 rollouts. A.2 Additional Ablations and Analysis Effect of Critic The critic assigns each rollout a scalar reward intended to predict task success. To validate this signal, we evaluate it against ground-truth outcomes over 360 rollouts (overall success rate 0.63). Binning rollout… view at source ↗
Figure 8
Figure 8. Figure 8: Performance comparison of different VLM-based reward models for reasoning guidance. We evaluate the impact of CycleReward [5] versus BLIP-SBERT score [7] on success rates across six challenging manipulation tasks including five Pick-and-Place (PnP) variants and a mug setup task as shown in (a). While performance is task￾dependent, SBERT variant demonstrates slightly superior robustness and a higher overall… view at source ↗
Figure 9
Figure 9. Figure 9: Effect of changing action horizon. Our choice of action horizon 16 performs best overall computed across 3 tasks with 50 demos each. ing critic because it yields a higher average success rate across the full task suite, indicating a more stable semantic representation for evaluating environmental transitions. Effect of changing action horizon. We perform evaluation of our model changing the action horizon … view at source ↗
Figure 10
Figure 10. Figure 10: Joint training vs Stage-wise training: Our proposed joint training setup shows enhanced task-wise success-rates compared to stage-wise training computed across 4 RoboCasa tasks. The Average scores (right) further strengthen our claim. re-planning frequency and action commitment; very short horizons can make control too reactive, while very long horizons increase open-loop drift and error accumulation. Joi… view at source ↗
Figure 11
Figure 11. Figure 11: LLM-judge evaluation of decomposition quality (N=50): planner decomposi￾tions outperform prompt-only (60% vs 38% wins, 2% ties) while using fewer steps (2.80 vs 5.28), indicating better plan efficiency and overall preference. Retaining and Improving General Video Generation Capabilities. To validate whether our joint task-aligned training preserves the underlying genera￾tive video prior, we evaluate the o… view at source ↗
Figure 12
Figure 12. Figure 12: Generative retention on UCF101. We compare the original SVD with our jointly trained video model using 200 matched conditioning samples from UCF101 dat￾set with 30 denoising steps each, evaluated against ground-truth future clips. The radar plot shows absolute quality profiles across alignment and distributional metrics, while the gain-area plot reports per-metric improvements (relative gain % with absolu… view at source ↗
Figure 13
Figure 13. Figure 13: Comparing our method to the baseline across different tasks . We show how our model performs against the baseline on tasks like turning off a faucet, moving items from a stove to a counter, and serving coffee. In each case, the baseline (top rows) loses track of the goal over time, leading to distorted movements or failed actions. Our method (bottom rows) uses high-level planning to stay on track, complet… view at source ↗
Figure 14
Figure 14. Figure 14: Success of our method in various kitchen scenarios . This figure demonstrates our model’s ability to handle different complex tasks, such as setting up a coffee mug, opening drawers, and closing double doors. Our model consistently creates smooth, goal-oriented movements while managing multiple camera views and complex objects. These successful results show that combining smart reasoning with a video gene… view at source ↗

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Reference graph

Works this paper leans on

78 extracted references · 78 canonical work pages · 26 internal anchors

  1. [1]

    Gemini Robotics: Bringing AI into the Physical World

    Abeyruwan, S., Ainslie, e.a.: Gemini robotics: Bringing ai into the physical world. arXiv preprint arXiv:2503.20020 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  2. [2]

    Ada, S.E., Oztop, E., Ugur, E.: Diffusion policies for out-of-distribution gen- eralizationinofflinereinforcementlearning.IEEERoboticsandAutomation Letters9(4), 3116–3123 (2024).https://doi.org/10.1109/LRA.2024. 3363530

    work page doi:10.1109/lra.2024 2024

  3. [3]

    Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

    Ahn, M., et al.: Do as i can, not as i say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691 (2022)

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  4. [4]

    In: International Conference on Machine Learning (ICML)

    Andreas, J., Klein, D., Levine, S.: Modular multitask reinforcement learn- ing with policy sketches. In: International Conference on Machine Learning (ICML). pp. 166–175. PMLR (2017)

    work page 2017

  5. [5]

    In: Proceedings oftheIEEE/CVFInternationalConferenceonComputerVision.pp.22934– 22946 (2025)

    Bahng, H., Chan, C., Durand, F., Isola, P.: Cycle consistency as reward: Learning image-text alignment without human preferences. In: Proceedings oftheIEEE/CVFInternationalConferenceonComputerVision.pp.22934– 22946 (2025)

    work page 2025

  6. [6]

    Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation

    Bharadhwaj, H., Dwibedi, D., Gupta, A., Tulsiani, S., Doersch, C., Xiao, T., Shah, D., Xia, F., Sadigh, D., Kirmani, S.: Gen2act: Human video gen- eration in novel scenarios enables generalizable robot manipulation. arXiv preprint arXiv:2409.16283 (2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  7. [7]

    Training Diffusion Models with Reinforcement Learning

    Black, K., Janner, M., Du, Y., Kostrikov, I., Levine, S.: Training diffusion models with reinforcement learning. arXiv preprint arXiv:2305.13301 (2023)

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  8. [8]

    Blattmann, A., Dockhorn, T., Kulal, S., Mendelevitch, D., Kilian, M., Lorenz, D., Levi, Y., English, Z., Voleti, V., Letts, A., et al.:Stable video dif- fusion:Scalinglatentvideodiffusionmodelstolargedatasets.arXivpreprint arXiv:2311.15127 (2023)

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  9. [9]

    Trends in Cognitive Sciences12(5), 201–208 (2008)

    Botvinick, M.M.: Hierarchical models of behavior and prefrontal function. Trends in Cognitive Sciences12(5), 201–208 (2008)

    work page 2008

  10. [10]

    RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

    Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., et al.: RT-2: Vision-language- action models transfer web knowledge to robotic control. arXiv preprint arXiv:2307.15818 (2023)

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  11. [11]

    Brooks, T., Peebles, B., Holmes, C., DePue, W., Guo, Y., Jing, L., Schnurr, D., Taylor, J., Luhman, T., Luhman, E., Ng, C., Wang, R., Ramesh, A.: Videogenerationmodelsasworldsimulators.https://openai.com/index/ video-generation-models-as-world-simulators/, last accessed 2026- 06-29 (2024)

    work page 2026

  12. [12]

    In: Interna- tional Conference on Machine Learning (ICML)

    Bruce, J., et al.: Genie: Generative interactive environments. In: Interna- tional Conference on Machine Learning (ICML). PMLR (2024)

    work page 2024

  13. [13]

    Confer- ence on Robot Learning (CoRL) (2025) 16 Hanan Gani et al

    Chandra, A.L., Nematollahi, I., Huang, C., Welschehold, T., Burgard, W., Valada, A.: Diwa: Diffusion policy adaptation with world models. Confer- ence on Robot Learning (CoRL) (2025) 16 Hanan Gani et al

    work page 2025

  14. [15]

    Planning with Reasoning using Vision Language World Model

    Chen, D., Moutakanni, T., Chung, W., Bang, Y., Ji, Z., Bolourchi, A., Fung, P.: Planning with reasoning using vision language world model. arXiv preprint arXiv:2509.02722 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  15. [16]

    In: The Eleventh International Conference on Learning Representations (2023)

    Chen,H.,Lu,C.,Ying,C.,Su,H.,Zhu,J.:Offlinereinforcementlearningvia high-fidelity generative behavior modeling. In: The Eleventh International Conference on Learning Representations (2023)

    work page 2023

  16. [17]

    The International Journal of Robotics Research (2024)

    Chi, C., Xu, Z., Feng, S., Cousineau, E., Du, Y., Burchfiel, B., Tedrake, R., Song, S.: Diffusion policy: Visuomotor policy learning via action diffusion. The International Journal of Robotics Research (2024)

    work page 2024

  17. [18]

    Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

    Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al.: Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities. arXiv preprint arXiv:2507.06261 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  18. [19]

    World Models

    Ha, D., Schmidhuber, J.: World models. arXiv preprint arXiv:1803.10122 (2018)

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  19. [20]

    Soft Actor-Critic Algorithms and Applications

    Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., et al.: Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905 (2018)

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  20. [21]

    In: International Conference on Machine Learning (ICML)

    Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., Davidson, J.: Learning latent dynamics for planning from pixels. In: International Conference on Machine Learning (ICML). pp. 2555–2565 (2019)

    work page 2019

  21. [22]

    Mastering Diverse Domains through World Models

    Hafner, D., Pasukonis, J., Ba, J., Lillicrap, T.: Mastering diverse domains through world models. arXiv preprint arXiv:2301.04104 (2023).https:// doi.org/10.48550/arXiv.2301.04104

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2301.04104 2023

  22. [23]

    A Dual Process VLA: Efficient Robotic Manipulation Leveraging VLM

    Han,B.,Kim,J.,Jang,J.:Adualprocessvla:Efficientroboticmanipulation leveraging vlm. arXiv preprint arXiv:2410.15549 (2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  23. [24]

    Hansen, N., Su, H., Wang, X.: Td-mpc2: Scalable, robust world models for continuous control (2024)

    work page 2024

  24. [25]

    Hansen-Estruch, P., Kostrikov, I., Janner, M., Kuba, J.G., Levine, S.: Idql: Implicit q-learning as an actor-critic method with diffusion policies (2023)

    work page 2023

  25. [26]

    DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning

    He, L., Shen, L., Zhang, L., Tan, J., Wang, X.: Diffcps: Diffusion model based constrained policy search for offline reinforcement learning. arXiv preprint arXiv:2310.05333 (2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  26. [27]

    In: Advances in Neural Information Processing Systems (NeurIPS)

    Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems (NeurIPS). vol. 33, pp. 6840–6851 (2020)

    work page 2020

  27. [28]

    GAIA-1: A Generative World Model for Autonomous Driving

    Hu, A., Russell, L., Yeo, H., Murez, Z., Fedoseev, G., Kendall, A., Shotton, J., Corrado, G.: Gaia-1: A generative world model for autonomous driving. arXiv preprint arXiv:2309.17080 (2023)

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  28. [29]

    In: Conference on Robot Learning (CoRL)

    Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., et al.: Inner monologue: Embodied reasoning through planning with language models. In: Conference on Robot Learning (CoRL). PMLR (2022) RoboTALES 17

    work page 2022

  29. [30]

    In: International Conference on Machine Learn- ing (2022)

    Janner, M., Du, Y., Tenenbaum, J.B., Levine, S.: Planning with diffusion for flexible behavior synthesis. In: International Conference on Machine Learn- ing (2022)

    work page 2022

  30. [31]

    In: Agrawal, P., Kroemer, O., Burgard, W

    Jia, X., Wang, Q., Donat, A., Xing, B., Li, G., Zhou, H., Celik, O., Blessing, D., Lioutikov, R., Neumann, G.: Mail: Improving imitation learning with selective state space models. In: Agrawal, P., Kroemer, O., Burgard, W. (eds.) Proceedings of The 8th Conference on Robot Learning. Proceedings of Machine Learning Research, vol. 270, pp. 3888–3907. PMLR (0...

    work page 2025

  31. [32]

    Arxiv (2024)

    Ke,T.W.,Gkanatsios,N.,Fragkiadaki,K.:3ddiffuseractor:Policydiffusion with 3d scene representations. Arxiv (2024)

    work page 2024

  32. [33]

    Unified Video Action Model

    Li, S., Gao, Y., Sadigh, D., Song, S.: Unified video action model. arXiv preprint arXiv:2503.00200 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  33. [34]

    WorldEval: World Model as Real-World Robot Policies Evaluator

    Li, Y., Zhu, Y., Wen, J., Shen, C., Xu, Y.: Worldeval: World model as real-world robot policies evaluator. arXiv preprint arXiv:2505.19017 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  34. [35]

    Video Generators are Robot Policies

    Liang, J., Tokmakov, P., Liu, R., Sudhakar, S., Shah, P., Ambrus, R., Vondrick, C.: Video generators are robot policies. arXiv preprint arXiv:2508.00795 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  35. [36]

    LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

    Liu, B., Zhu, Y., Gao, C., Feng, Y., Liu, Q., Zhu, Y., Stone, P.: Libero: Benchmarking knowledge transfer for lifelong robot learning. arXiv preprint arXiv:2306.03310 (2023)

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  36. [37]

    In: Inter- national Conference on Machine Learning

    Ma, Y.J., Kumar, V., Zhang, A., Bastani, O., Jayaraman, D.: Liv: Language-image representations and rewards for robotic control. In: Inter- national Conference on Machine Learning. pp. 23301–23320. PMLR (2023)

    work page 2023

  37. [38]

    Holt, Rinehart and Winston, New York (1960)

    Miller, G.A., Galanter, E., Pribram, K.H.: Plans and the Structure of Be- havior. Holt, Rinehart and Winston, New York (1960)

    work page 1960

  38. [39]

    Nature 518(7540), 529–533 (2015)

    Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Belle- mare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)

    work page 2015

  39. [40]

    RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots

    Nasiriany, S., Maddukuri, A., Zhang, L., Parikh, A., Lo, A., Joshi, A., Man- dlekar, A., Zhu, Y.: Robocasa: Large-scale simulation of everyday tasks for generalist robots. arXiv preprint arXiv:2406.02523 (2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  40. [41]

    Cosmos World Foundation Model Platform for Physical AI

    NVIDIA, :, Agarwal, N., Ali, A., Bala, M., Balaji, Y., Barker, E., Cai, T., Chattopadhyay, P., Chen, Y., Cui, Y., Ding, Y., Dworakowski, D., Fan, J., Fenzi, M., Ferroni, F., Fidler, S., Fox, D., Ge, S., Ge, Y., Gu, J., Gururani, S., He, E., Huang, J., Huffman, J., Jannaty, P., Jin, J., Kim, S.W., Klár, G., Lam,G.,Lan,S.,Leal-Taixe,L.,Li,A.,Li,Z.,Lin,C.H.,...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  41. [42]

    google/discover/blog/genie-2-a-large-scale-foundation-world- model/, last accessed 2026-06-29 (2024)

    Parker-Holder, J., Ball, P., Bruce, J., Dasagi, V., Holsheimer, K., Kaplanis, C., Moufarek, A., Scully, G., Shar, J., Shi, J., Spencer, S., Yung, J., Dennis, M., Kenjeyev, S., Long, S., Mnih, V., Chan, H., Gazeau, M., Li, B., Pardo, F., Wang, L., Zhang, L., Besse, F., Harley, T., Mitenkova, A., Wang, J., Clune, J., Hassabis, D., Hadsell, R., Bolton, A., S...

    work page 2026

  42. [43]

    ACM Computing Surveys54(5), 1–35 (2021)

    Pateria, S., Subagdja, B., Tan, A.h., Quek, C.: Hierarchical reinforcement learning: A comprehensive survey. ACM Computing Surveys54(5), 1–35 (2021)

    work page 2021

  43. [44]

    In: Robotics: Science and Systems (2024)

    Prasad, A., Lin, K., Wu, J., Zhou, L., Bohg, J.: Consistency policy: Accel- erated visuomotor policies via consistency distillation. In: Robotics: Science and Systems (2024)

    work page 2024

  44. [45]

    In: Robotics: Science and Systems (2024)

    Reuss, M., Yağmurlu, Ö.E., Wenzel, F., Lioutikov, R.: Multimodal diffu- sion transformer: Learning versatile behavior from multimodal goals. In: Robotics: Science and Systems (2024)

    work page 2024

  45. [46]

    arXiv preprint arXiv:2511.07732 (2025)

    Routray, S., Pan, H., Jain, U., Bahl, S., Pathak, D.: Vipra: Video prediction for robot actions. arXiv preprint arXiv:2511.07732 (2025)

    work page arXiv 2025

  46. [47]

    Nature 588(7839), 604–609 (2020)

    Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al.: Mas- tering atari, go, chess and shogi by planning with a learned model. Nature 588(7839), 604–609 (2020)

    work page 2020

  47. [48]

    Proximal Policy Optimization Algorithms

    Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  48. [49]

    In: Conference on Robot Learning (CoRL)

    Shah, D., Osinski, B., Ichter, B., Levine, S.: LM-nav: Robotic navigation with large pre-trained models of language, vision, and action. In: Conference on Robot Learning (CoRL). PMLR (2023)

    work page 2023

  49. [50]

    Denoising Diffusion Implicit Models

    Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  50. [51]

    ACM SIGART Bulletin2(4), 160–163 (1991)

    Sutton, R.S.: Dyna, an integrated architecture for learning, planning, and reacting. ACM SIGART Bulletin2(4), 160–163 (1991)

    work page 1991

  51. [52]

    CoRL (2024)

    Wang, Y., Zhang, Y., Huo, M., Tian, R., Zhang, X., Xie, Y., Xu, C., Ji, P., Zhan, W., Ding, M., et al.: Sparse diffusion policy: A sparse, reusable, and flexible policy for robot learning. CoRL (2024)

    work page 2024

  52. [53]

    International Conference on Learn- ing Representations (2023)

    Wang, Z., Hunt, J.J., Zhou, M.: Diffusion policies as an expressive policy class for offline reinforcement learning. International Conference on Learn- ing Representations (2023)

    work page 2023

  53. [54]

    One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation

    Wang, Z., Li, Z., Mandlekar, A., Xu, Z., Fan, J., Narang, Y., Fan, L., Zhu, Y., Balaji, Y., Zhou, M., Liu, M.Y., Zeng, Y.: One-step diffusion policy: Fast visuomotor policies via diffusion distillation. arXiv preprint arXiv:2410.21257 (2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  54. [55]

    DNAct: Diffusion Guided Multi-Task 3D Policy Learning

    Yan, G., Wu, Y.H., Wang, X.: Dnact: Diffusion guided multi-task 3d policy learning. arXiv preprint arXiv:2403.04115 (2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  55. [56]

    International Conference on Learning Representations (2024) RoboTALES 19

    Yang, M., Du, Y., Ghasemipour, K., Tompson, J., Schuurmans, D., Abbeel, P.: Learning interactive real-world simulators. International Conference on Learning Representations (2024) RoboTALES 19

    work page 2024

  56. [57]

    3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations

    Ze, Y., Zhang, G., Zhang, K., Hu, C., Wang, M., Xu, H.: 3d diffusion pol- icy: Generalizable visuomotor policy learning via simple 3d representations. arXiv preprint arXiv:2403.03954 (2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  57. [58]

    In: Agrawal, P., Kroemer, O., Burgard, W

    Zhao, T.Z., Tompson, J., Driess, D., Florence, P., Ghasemipour, S.K.S., Finn, C., Wahid, A.: Aloha unleashed: A simple recipe for robot dexterity. In: Agrawal, P., Kroemer, O., Burgard, W. (eds.) Proceedings of The 8th Conference on Robot Learning. Proceedings of Machine Learning Research, vol. 270, pp. 1910–1924. PMLR (06–09 Nov 2025)

    work page 1910

  58. [59]

    Scaling Diffusion Policy in Transformer to 1 Billion Parameters for Robotic Manipulation

    Zhu, M., Zhu, Y., Li, J., Wen, J., Xu, Z., Liu, N., Cheng, R., Shen, C., Peng, Y., Feng, F., et al.: Scaling diffusion policy in transformer to 1 bil- lion parameters for robotic manipulation. arXiv preprint arXiv:2409.14411 (2024) 20 Hanan Gani et al. RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures A Appendix The ap...

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  59. [60]

    Place the moka pot on the stove burner Put the black bowl in the bottom drawer of the cabinet and close it

    work page

  60. [61]

    Pick up the black bowl

    work page

  61. [62]

    Open the cabinet bottom drawer

    work page

  62. [63]

    Place the black bowl in the drawer

    work page

  63. [64]

    Close the cabinet bottom drawer Put the yellow and white mug in the microwave and close it

    work page

  64. [65]

    Put the yellow and white mug in the mi- crowave

    work page

  65. [66]

    Place the first moka pot on the stove

    Close the microwave door Put both moka pots on the stove 1. Place the first moka pot on the stove

    work page

  66. [67]

    Place the second moka pot on the stove Put both the alphabet soup and the cream cheese box in the basket

    work page

  67. [68]

    Pick up the alphabet soup

    work page

  68. [70]

    Pick up the cream cheese box

    work page

  69. [71]

    Place the cream cheese box in the basket Put both the alphabet soup and the tomato sauce in the basket

    work page

  70. [72]

    Place the alphabet soup in the basket

    work page

  71. [73]

    Place the tomato sauce in the basket Put both the cream cheese box and the butter in the basket

    work page

  72. [74]

    Place the cream cheese box in the basket

    work page

  73. [75]

    Place the butter in the basket Put the white mug on the left plate and put the yellow and white mug on the right plate

    work page

  74. [76]

    Put the white mug on the left plate

    work page

  75. [77]

    Put the yellow and white mug on the right plate Put the white mug on the plate and put the chocolate pudding to the right of the plate

    work page

  76. [78]

    Put the white mug on the plate

    work page

  77. [79]

    Place the chocolate pudding to the right of the plate Pick up the book and place it in the back compartment of the caddy

    work page

  78. [80]

    First, our VLM Critic is frozen throughout training and provides a relatively coarse, task-level reward signal

    Place the book in the caddy back compart- ment A.5 Limitations While RoboTALES demonstrates strong performance across diverse manipula- tion tasks, it is worth noting certain limitations. First, our VLM Critic is frozen throughout training and provides a relatively coarse, task-level reward signal. A learnable reward model that co-adapts with the video ge...

    work page