AdaJEPA: An Adaptive Latent World Model
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Computer Science > Machine Learning
Title:AdaJEPA: An Adaptive Latent World Model
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.32026 [cs.LG] |
| (or arXiv:2606.32026v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.32026
arXiv-issued DOI via DataCite (pending registration)
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