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

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Hanan Gani, Tejal Kulkarni, Madhoolika Chodavarapu, Nicklas Hansen, Manmohan Chandraker

Robotics · Submitted 7 Jul 2026 · 78 references

Pith's verdict: Clean systems recipe for LLM-guided, VLM-scored video futures in robot learning; the anti-drift claim hangs on an unshown VLM–progress link.

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arXiv ID2607.06018 (version 1)
Pith IDRHFWO573, Pith's permanent record of this paper and its review
CitationHanan Gani, Madhoolika Chodavarapu, Manmohan Chandraker, Nicklas Hansen, Tejal Kulkarni. "RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures." arXiv:2607.06018 (2026). https://pith.science/paper/RHFWO573
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The paper

The paper claims

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

The reading

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.

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.

Extended reading notes

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.

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.

Editorial extensions

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.

Reading between the lines

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.
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
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 precise robot actions for long-horizon manipulation. Works such as Video-Policy [35], Gen2Act [6], and ViPRA [46] demonstrate that video generators can serve as powerful priors for…
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. Crucially, action-level gradients propagate back into the selected decoder layers of Gθ, enabling the world model to co-adapt its representations for both visual prediction and downstrea
Figure 3. Qualitative Comparison of Task Execution
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
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￾porally coherent representations that translate to smoother robot actions. (a) Demo Efficiency (b) Inference Time
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 rollouts into re￾ward quartiles, success rate rises monotonically from 0.40 in the lowest quartile to 0.88 in the highest (Fig. 7a), confirming that the critic is well calibrated: higher…
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 average success rate as shown in (b)
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 on 150 demos across 3 tasks. As shown in
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. Joint Training vs. Stage-wise Training. As described in the main paper, RoboTALES adopts a coupled, single-stage joint training strategy in which the video generator and action policy…
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 original SVD model and our jointly trained video model on non-robotics real videos (from UCF101 dataset), using 200 matched conditioning samples and 30 denoising steps. We compare…
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 absolute-gain overlay). Our model improves all metrics, with the largest gains in Fréchet and MMD alignment, and substantially better diversity/covariance matching to the real-video…
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, completing the tasks accurately and more quickly
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 generator leads to more reliable robot performance in real-world settings

Showing 13 of the paper's 14 figures. See all figures in the PDF ↗ Figures reproduced from arXiv:2607.06018 by the authors.

References

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 19 sentences without a confident label

Contests 3 papers
  • In contrast to prior video-based action extraction pipelines [ref] [Reproduced numbers have been verified via correspondence with the VideoPolicy [ref] authors; they acknowledge a discrepancy in performance between their paper and released codebase.

    Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation · Quantitative Results · contest 0.81

  • In contrast to prior video-based action extraction pipelines [ref] [Reproduced numbers have been verified via correspondence with the VideoPolicy [ref] authors; they acknowledge a discrepancy in performance between their paper and released codebase.

    Video Generators are Robot Policies · Quantitative Results · contest 0.81

  • In contrast to prior video-based action extraction pipelines [ref] [Reproduced numbers have been verified via correspondence with the VideoPolicy [ref] authors; they acknowledge a discrepancy in performance between their paper and released codebase.

    2511.07732 · Quantitative Results · contest 0.81

Uses the method or tool of 4 papers
Uses data or values from 2 papers
Compares with 4 papers
  • We use the baselines reported in [ref] —including the RoboCasa-trained versions of [ref] and ResNet- and CLIP-based variants of [ref] .

    Unified Video Action Model · Experimentation · 0.93

  • We use the baselines reported in [ref] —including the RoboCasa-trained versions of [ref] and ResNet- and CLIP-based variants of [ref] .

    Video Generators are Robot Policies · Experimentation · 0.93

  • In particular, on challenging Pick-and-Place tasks in RoboCasa, RoboTALES reaches a mean success rate of 48%, outperforming [ref] and the strongest prior baseline [ref] .

    A Dual Process VLA: Efficient Robotic Manipulation Leveraging VLM · Introduction · 1.00

Cites as support 2 papers
  • This is because the underlying video generators are still optimized primarily for visual realism or reconstruction objectives, hence their simulated futures can remain weakly grounded in task intent; moreover, making generated videos faithfully follow robot actions is itself nontrivial [ref] .

    WorldEval: World Model as Real-World Robot Policies Evaluator · Introduction · 0.97

  • This converts a compressed instruction into an explicit milestone sequence, providing dense semantic structure that improves long-horizon coherence compared to using $τ$ alone [ref] .

    Planning with Reasoning using Vision Language World Model · Architecture and Components · 0.99

Cites as background 11 papers

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