The Planning Limits of Latent World Models
World models offer a promising way to help robots understand how the physical world evolves and plan complex behaviours through imagination. Yet existing studies mainly demonstrate what these models can accomplish, leaving unclear when their predictions remain useful for planning and where they fail. We study this question using action-conditioned predictors built on five frozen self-supervised visual backbones: V-JEPA 2, V-JEPA 2.1, VideoMAEv2, VideoPrism, and DINOv2. We use frozen backbones to test representations intended to transfer across environments. We evaluate these models on diverse Meta-World manipulation tasks and real-robot interactions from BridgeData V2. We find that a worl...
| Authors | Ali Alrasheed, Basim Azam, Naveed Akhtar |
|---|---|
| Primary category | cs.RO |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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