World Models Dream of Success: Diagnosing and Repairing Failure Insensitivity in Robot World Models

Summary

Robot world models support policy evaluation, planning, and synthetic data generation, but these applications require predictions that distinguish successful actions from failures.

Abstract Summary

Robot world models support policy evaluation, planning, and synthetic data generation, but these applications require predictions that distinguish successful actions from failures. Across four released checkpoints from two architecture families, we observe weak sensitivity to action changes and success-like predictions on verified failures. Although recent work incorporates failures into model training, which data can repair released checkpoints without changing their architecture or training objective still remains underexplored. To this end, we introduce CureWM, which constructs alternative actions from successful demonstrations across a severity grid, verifies their outcomes through execution in simulation or on hardware, and fine-tunes released models on the resulting failures and surviving successes alongside nominal demonstrations. This construction provides controlled action contrasts from shared starting contexts. On 484 held-out LIBERO failure counterfactuals, optimism falls from 80\% after fine-tuning on the official data to 30--43\% across four independently fine-tuned CureWM models (38\% mean). In two separate evaluations on a physical robot arm, failure predictions scored as success-like by a latent-distance diagnostic decrease from 90\% after fine-tuning on successful demonstrations alone to 33\% with CureWM. With failure counts per task, successful replay data, and training budget matched, counterfactual failures yield a success--failure value gap of 0.124, compared with 0.014 for freshly collected on-policy failures. These findings support execution-verified counterfactual replay for post-hoc repair and show why reduced optimism must be evaluated alongside success--failure discrimination. Code is available at https://github.com/jiuyixu25/CureWM.

Key Points

Robot world models support policy evaluation, planning, and synthetic data generation, but these applications require predictions that distinguish successful actions from failures.
Across four released checkpoints from two architecture families, we observe weak sensitivity to action changes and success-like predictions on verified failures.
Although recent work incorporates failures into model training, which data can repair released checkpoints without changing their architecture or training objective still remains underexplored.
To this end, we introduce CureWM, which constructs alternative actions from successful demonstrations across a severity grid, verifies their outcomes through execution in simulation or on hardware, and fine-tunes released models on the resulting failures and surviving successes alongside nominal demonstrations.
This construction provides controlled action contrasts from shared starting contexts

Method

Let $x_{t}$ denote the observation history and task context, and let $a^{+}_{t:T}$ and $a^{-}_{t:T}$ denote action sequences that succeed and fail from the same starting context. We call a model failure-insensitive when its predictions fail to distinguish these outcomes. Figure 1 illustrates this behavior.

This differs from optimism alone: a model can assign similar values to successful and failing actions even when neither value is high.

Method — World Models Dream of Success: Diagnosing and Repairing Failure Insensitivity in Robot World Models — Figure 1: Failure insensitivity on a held-out Franka counterfactual. From the same starting context, the original action sequence succeeds and a perturbed sequence fails under physical execution, yet the model predicts a success-like future for the failing sequence.

Simulation Experiments

The baseline and CureWM use official data from all four LIBERO suites; CureWM additionally uses counterfactual replays from LIBERO-Goal. Goal evaluation contains 484 verified failures and 1,196 successful perturbed replays from two independently generated sets using the training perturbation families and severity grid.

Their source demonstrations are excluded from counterfactual construction but mostly present in the official data. The second set was generated after the compared models finished training.

Simulation Experiments — World Models Dream of Success: Diagnosing and Repairing Failure Insensitivity in Robot World Models — Figure 2: The overview of CureWM. We perturb successful demonstrations at different severities, verify outcomes through execution, and use both successful and failing replays for post-training alongside the original training data. Severity values, outcome assignments, and predicted-value bars are used for illustration rather than measured results.

Repair on a Physical Robot

We evaluate CureWM on a Franka Research 3 using DROID-pretrained Ctrl-World ( Khazatsky et al., 2024 ; Guo et al., 2026 ) . Cup pick-and-place is the primary repair task. Cube picking tests task-specific repair and transfer from cup-task repair. Replay outcomes are verified through physical execution.

After 1,400 steps of scene adaptation using successful demonstrations, we fine-tune CureWM on 90 failed replays, 24 successful wrist-tilt replays, and 31 unperturbed successful trajectories.

Repair on a Physical Robot — World Models Dream of Success: Diagnosing and Repairing Failure Insensitivity in Robot World Models — Figure 3: Predicted futures for a failing cup-task action sequence. Columns show successive time steps. Rows show recorded execution, the scene-adapted model, and CureWM. CureWM predicts the observed release; the adapted model predicts continued grasping.

Sources and Demonstrations

Figures are reproduced from Xu et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.