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This work introduces ID-WM (Implicit Dynamics World Models), a hierarchical world model that decouples state-space reachability from local control and yields a 135% performance gain over single-level world model MPC, particularly in complex, unseen configurations for long-horizon tasks.
Latent world models have shown a remarkable ability to predict future states and to plan in the real world. In practice, however, we lack a principled way to estimate how their capabilities scale with model size, data, and compute, an open problem that slows progress in the field. In this work we present RoboJEPA, a wo...
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