JEPA-Bisim: Learning Robust Visual Representations for Planning with Joint-Embedding Predictive World Models

Abstract:World models learned from high-dimensional visual observations allow agents to make decisions and plan directly in latent space, avoiding pixel-level reconstruction. However, recent latent predictive architectures (JEPAs), including the DINO world model (DINO-WM), display a degradation in test time robustness due to their sensitivity to ``slow features". These include visual variations such as background changes and distractors that are irrelevant to the task being solved. We address this limitation by augmenting the predictive objective with a bisimulation encoder that enforces control-relevant state equivalence, mapping states with similar transition dynamics to nearby latent states while limiting contributions from slow features. We evaluate our model on a navigation task (PointMaze) and on a manipulation task (PushT) under different test-time background changes and visual distractors. Across all benchmarks, our model consistently improves robustness to slow features while operating in a reduced latent space, up to $10\times$ smaller than that of DINO-WM. Moreover, our model is agnostic to the choice of pre-trained visual encoder and maintains robustness when paired with DINOv2, SimDINOv2, and iBOT features.

Machine Learning (cs.LG); Optimization and Control (math.OC)

arXiv:2602.18639 [cs.LG]

(or arXiv:2602.18639v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2602.18639 Focus to learn more arXiv-issued DOI via DataCite

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