Underwater C3-JEPA: An Object-Centric Cross-View World Model for ROV Salvage
Researchers introduce Underwater C3-JEPA, a control-conditioned world model for near-field ROV salvage.
The paper introduces Underwater C3-JEPA, an object-centric multi-view world model for near-field heavy-load underwater ROV salvage. From synchronized RGB views and vehicle controls, it predicts latent task-object state through contact and hydrodynamic lag without contact sensors. Cross-camera attention, weak binding, and SIGReg shape a representation the authors say transfers more task-relevant information than a reconstruction-free latent baseline. Validation on real underwater video recovered a withheld camera's object state and stayed ahead of persistence, supporting MPC evaluation and imagined-rollout training.
- C3-JEPA predicts task-object state from multi-view RGB and vehicle controls.
- It uses cross-camera attention, weak binding, and SIGReg without contact sensors.
- Features transfer more task information than a reconstruction-free latent baseline.
- The interface supports MPC evaluation and imagined-rollout agent training.
- Real underwater video recovered a withheld camera's object state ahead of persistence.
From the original article
1 min read at arxiv.orgWe present Underwater C$^{3}$-JEPA (cross-view, control-conditioned, context-extended), an object-centric multi-view predictive world model for near-field heavy-load underwater ROV salvage.
By Yuncong Yang · arxiv.org
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