AquaJEPA: An Action-Conditioned Multimodal JEPA Family for Underwater Robot Dynamics

Alan-Barsag Gazzaev; Alexey Gavrilov; Sergey Muravyov

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arXiv · 2607.29393

AquaJEPA: An Action-Conditioned Multimodal JEPA Family for Underwater Robot Dynamics

Alan-Barsag Gazzaev·Alexey Gavrilov·Sergey Muravyov

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Abstract

Underwater robots rely on complementary sensors whose reliability changes abruptly with water visibility and vehicle motion. We introduce AquaJEPA, a sensor-configurable family of action-conditioned joint-embedding predictive models spanning full multimodal, camera-only, sonar-only, and sensor-dropout configurations. Its members share a latent objective and receding-horizon control interface that predict future representations and physical dynamics from camera, forward-looking sonar, proprioception, and thruster commands. Trained from scratch on one hour of action-labelled data, the family is evaluated in Stonefish on 120 fresh paired scenarios spanning unseen layouts, visibility changes, dynamics shifts, and scheduled DVL loss. AquaJEPA-base achieves the strongest aggregate closed-loop performance, improving success over state-only by 12.5 percentage points and reducing final error by 0.189 m; both paired 95% intervals exclude zero. In a separate three-seed evaluation, it reduces paired final error relative to AquaJEPA-S by 0.118 m, with the same direction for every seed. AquaJEPA-robust more than halves prediction error during camera and camera-DVL blackouts. These results show that full multimodal prediction improves over state-only control and the sonar-only family member in this benchmark, while sensor-dropout training provides robustness under sensor loss.

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BibTeX RIS

Alan-Barsag Gazzaev, Alexey Gavrilov, Sergey Muravyov. 2026-08-10. AquaJEPA: An Action-Conditioned Multimodal JEPA Family for Underwater Robot Dynamics. https://arxiv.org/abs/2607.29393

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