Underwater C3-JEPA: An Object-Centric Cross-View World Model for ROV Salvage

arXiv:2609.30214 · cs.RO, cs.AI · Submitted 2026-09-24 · Read on arXiv

cs.RO, cs.AI

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• World Model — 31× in this paper · explained in 7 episodes
• Alignment — 8× in this paper · explained in 1 episode
• Consistency — 5× in this paper · explained in 3 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Regularization — 5× in this paper · explained in 3 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Latent Dynamics — 3× in this paper · explained in 2 episodes
• Latent World Models — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• International Conference on Machine Learning — 2× in this paper
• Latent Space — 2× in this paper · explained in 7 episodes
• Reconstruction Error — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Self-Supervised Learning — 2× in this paper · explained in 7 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 1× in this paper
• Provenance — 1× in this paper · explained in 3 episodes
• Regret — 1× in this paper · explained in 3 episodes

Sources

• Challenges of Real-World Reinforcement Learning
• Mastering Diverse Domains through World Models
• Conditional Object-Centric Learning from Video
• Bridging the Gap to Real-World Object-Centric Learning
• ROOTS: Object-Centric Representation and Rendering of 3D Scenes
• Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views
• AquaJEPA: An Action-Conditioned Multimodal JEPA Family for Underwater Robot Dynamics
• Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
• AdaJEPA: An Adaptive Latent World Model
• SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors
• Causal-JEPA: Learning World Models through Object-Level Latent Masking
• LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
• Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data
• DINOv3
• ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model
• JEPA-x: Cross-Predictive Physics Grounding for Forecastable Latent Dynamics

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