外观
论文arXiv 2609.302141 个月前
Underwater C3-JEPA：面向 ROV 打捞的以物体为中心跨视角世界模型
原文链接：Underwater C3-JEPA: An Object-Centric Cross-View World Model for ROV Salvage（arXiv 2609.30214 · 发布日期：2026-09-24）
Underwater C3-JEPA: An Object-Centric Cross-View World Model for ROV Salvage
面向无接触传感器的近场重载 ROV 打捞，提出多视角预测世界模型，并在实验和真实水下视频上验证。
核心要点
- 针对水下 ROV 打捞中接触交互与车辆水动力滞后下的物体状态预测问题。
- 从同步多视角 RGB 与控制信号编码物体和上下文 token，跨相机融合并预测未来状态。
- 弱绑定以低标注成本锚定目标和夹爪，SIGReg 用于增强几何表征。
- 实验显示表征向下游探针传递更多任务相关信息；真实水下视频中恢复遮留相机物体状态。
验证范围与局限：验证范围包括实验比较和真实水下视频中的遮留相机物体状态恢复。
英文摘要 (Abstract)
We 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. Without contact sensors, it predicts in latent space how the task-object state evolves through contact interaction and under the hydrodynamic lag of the vehicle, from synchronized multi-view RGB observations and vehicle control signals. C$^{3}$-JEPA encodes multi-camera observations into task-object and context tokens, fuses cross-camera evidence through held-out-view attention, and directly predicts future states conditioned on control. Weak binding anchors the target and gripper at low annotation cost, while SIGReg sharpens the geometric representation. Experiments show that the learned representation transfers substantially more task-relevant information to downstream probes than a reconstruction-free latent baseline, while keeping the predictor lightweight. The resulting predictive interface supports model-predictive-control (MPC) candidate evaluation and imagined-rollout behavior-agent training. Validation on real underwater video shows the same architecture recovering a withheld camera's object state and staying ahead of persistence, so the recipe transfers beyond simulation.
作者：Yuncong Yang, Jinlong Li, Yulong Xue, Feng Wu, Chunwen Zhang, Lei Qiao, Xuyang Wang
来源
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