AdaJEPA: An Adaptive Latent World Model

Latent world models enable planning from high-dimensional observations by predicting future states in a compact latent space.

However, these models are typically kept frozen at test time: when their predictions become inaccurate, planning can fail, especially under test-time distribution shift.

To address this, we propose AdaJEPA, an adaptive latent world model that performs test-time adaptation within the closed loop of model predictive control (MPC).

After training, AdaJEPA plans and executes the first action chunk, uses the observed next-state transition as a self-supervised adaptation signal, and replans with the updated model.

This closed-loop update continuously recalibrates the world model without additional expert demonstrations.

Across a range of goal-reaching tasks, AdaJEPA substantially improves planning success with as few as one gradient step per MPC replanning step.