VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model
Pretraining Vision-Language-Action (VLA) policies on internet-scale video is appealing, yet current latent-action objectives often learn the wrong thing: they remain anchored to pixel variation rather than action-relevant state transitions, making them vulnerable to appearance bias, nuisance motion, and information leakage. We introduce VLA-JEPA, a JEPA-style pretraining framework that sidesteps these pitfalls by design. The key idea is leakage-free state prediction: a target encoder produces latent representations from future frames, while the student pathway sees only the current observation -- future information is used solely as supervision targets, never as input. By predicting in latent space rather than pixel space, VLA-JEPA learns dynamics abstractions that are robust to camera motion and irrelevant background changes. This yields a simple two-stage recipe -- JEPA pretraining followed by action-head fine-tuning -- without the multi-stage complexity of prior latent-action pipelines. Experiments on LIBERO, LIBERO-Plus, SimplerEnv and real-world manipulation tasks show that VLA-JEPA achieves consistent gains in generalization and robustness over existing methods.
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Jingwen Sun
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Wenyao Zhang
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Zekun Qi
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Shaojie Ren
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Zezhi Liu
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Hanxin Zhu
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Guangzhong Sun
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Xin Jin
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Zhibo Chen
VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model
Pretraining Vision-Language-Action (VLA) policies on internet-scale video is appealing, yet current latent-action objectives often learn the wrong thing: they remain anchored to pixel variation rather than action-relevant state transitions, making them vulnerable to appearance bias, nuisance motion, and information leakage. We introduce VLA-JEPA, a JEPA-style pretraining framework that sidesteps these pitfalls by design. The key idea is leakage-free state prediction: a target encoder produces latent representations from future frames, while the student pathway sees only the current observation -- future information is used solely as supervision targets, never as input. By predicting in latent space rather than pixel space, VLA-JEPA learns dynamics abstractions that are robust to camera motion and irrelevant background changes. This yields a simple two-stage recipe -- JEPA pretraining followed by action-head fine-tuning -- without the multi-stage complexity of prior latent-action pipelines. Experiments on LIBERO, LIBERO-Plus, SimplerEnv and real-world manipulation tasks show that VLA-JEPA achieves consistent gains in generalization and robustness over existing methods.
VLA-JEPA is a JEPA-style pretraining framework that improves vision-language-action policy learning by using leakage-free state prediction in latent space, enhancing generalization and robustness in manipulation tasks.
Vision-Language-Action JEPA latent-action objectives pixel variation action-relevant state transitions appearance bias nuisance motion information leakage target encoder student pathway latent representations future frames current observation latent space dynamics abstractions camera motion background changes JEPA pretraining action-head fine-tuning generalization robustness