arXiv:2605.29360v1 Announce Type: new Abstract: Action-conditioned world models are increasingly used as scalable simulators for robot learning, yet current evaluations provide limited evidence that their predictions are reliable under the actions they condition on. Existing benchmarks largely emphasize visual fidelity, leaving unclear whether predicted futures are physically plausible, faithful to commanded actions, and calibrated to failure when actions should not succeed. We introduce \textsc{MiraBench}, a hierarchical benchmark that defines \emph{action-conditioned reliability} as a core e
The increasing reliance on action-conditioned world models for robot learning necessitates more robust evaluation methods, with current benchmarks falling short in assessing reliability beyond visual fidelity.
Reliable action-conditioned world models are critical for the safe and effective deployment of AI in robotic systems, enabling more robust robot learning and reducing unexpected failures.
The introduction of MiraBench shifts the evaluation focus from mere visual fidelity to the physical plausibility, action faithfulness, and calibrated failure prediction of robotic world models.
- · Robotics researchers
- · AI safety engineers
- · Robotics companies
- · Developers of robust world models
- · World models with poor action-conditioned reliability
- · Benchmarks focused solely on visual fidelity
Improved reliability and safety of robots trained with world models.
Accelerated development and adoption of AI-powered robotic systems in real-world applications.
Increased public and industry trust in autonomous robotic technologies leading to wider deployment.
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Read at arXiv cs.AI